From 4fd18db6cf8e49fb2a4fa84b5e8a794eaeb65df6 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 01:29:33 -0400 Subject: [PATCH 01/29] feat(asap-tools): batch driver for random-workload arms random_workload_batch.py runs every arm of the given workload sets on a cluster, one job per two-node slot, longest first; cleans a slot's nodes before each job, retries a failed job once under a new name, writes a compare_arms summary per workload (ClickHouse+MV as the reference), and keeps its state on disk so a restarted driver resumes. Co-Authored-By: Claude Opus 5.5 --- .../experiments/random_workload_batch.py | 330 ++++++++++++++++++ .../tests/test_random_workload_batch.py | 40 +++ 2 files changed, 370 insertions(+) create mode 100644 asap-tools/experiments/random_workload_batch.py create mode 100644 asap-tools/experiments/tests/test_random_workload_batch.py diff --git a/asap-tools/experiments/random_workload_batch.py b/asap-tools/experiments/random_workload_batch.py new file mode 100644 index 00000000..bc874980 --- /dev/null +++ b/asap-tools/experiments/random_workload_batch.py @@ -0,0 +1,330 @@ +#!/usr/bin/env python3 +"""Run a batch of random-workload arms on a cluster, many at once. + +Each job is one arm (asap, prom, vm, ch, chmv) of one workload, an +experiment_run_e2e.py or experiment_run_clickhouse.py run on its own slot: +two nodes, node_offset = 2 * slot, num_nodes = 1. Jobs run longest first. A +failed job retries once under a new experiment name. Before each job the +slot's nodes are cleaned of anything an earlier, killed job left running. +When every arm of a workload is done, compare_arms.py writes its summary. + +State is kept in /state.json after every change, so a restarted +driver resumes: finished jobs are skipped and jobs that were running rerun. + +Configs come from random_workload_artifacts.py --pilot-dir, under +config/experiment_type/random_workload///. + +Usage (from asap-tools/experiments, in tmux): + python3 random_workload_batch.py --batch overnight-1015 \\ + --username milindsr --hostname-suffix --slots 21 \\ + --set seed-862:01-10 --set seed-862-long:01-10 --set seed-862-x5:01 \\ + [--dry-run] [--stop-launching-after-hours 7] +""" + +import argparse +import gzip +import json +import os +import shlex +import subprocess +import sys +import time +from pathlib import Path + +import yaml + +EXPERIMENTS_DIR = Path(__file__).resolve().parent +OUTPUTS_DIR = EXPERIMENTS_DIR.parent.parent / "experiment_outputs" +CONFIG_DIR = EXPERIMENTS_DIR / "config" / "experiment_type" / "random_workload" +COMPARE_ARMS = ( + EXPERIMENTS_DIR / "post_experiment" / "multi_experiment" / "compare_arms.py" +) +POLL_SECS = 30 +MAX_ATTEMPTS = 2 +REFERENCE_ARM = "chmv" # exact, and the fastest exact arm: answers every window + +# arm -> (runner, config, extra overrides, mode directory) +ARMS = { + "asap": ( + "experiment_run_e2e.py", + "e2e", + ["experiment_params.experiment=[{mode:sketchdb,server:sketchdb}]"], + "sketchdb", + ), + "prom": ( + "experiment_run_e2e.py", + "e2e", + ["experiment_params.experiment=[{mode:baseline,server:prometheus}]"], + "baseline", + ), + "vm": ("experiment_run_e2e.py", "victoriametrics", [], "baseline"), + "ch": ("experiment_run_clickhouse.py", "clickhouse", [], "baseline"), + "chmv": ("experiment_run_clickhouse.py", "clickhouse_mv", [], "baseline_mv"), +} +# Arms that fall behind 1 s queries in the pilot, and so take longest. +SLOW_ARMS = {"prom", "ch"} +# Anything a job leaves running on its nodes: feeders, bare-metal +# Prometheus, monitors and query clients, and every system's container. +CLEANUP = ( + "pkill -f '[t]arget/release/paced_feeder'; pkill -x prometheus; " + "pkill -f '[r]emote_monitor.py'; pkill -f '[q]uery_engine_rust'; " + "docker rm -f clickhouse-server victoriametrics-single victoriametrics-agent " + "sketchdb-queryengine-rust sketchdb-prometheusclient prometheus-container " + ">/dev/null 2>&1; true" +) + + +def parse_set(spec): + """`seed-862-long:01-10` or `seed-862-x5:01,03` -> (set, [workload ids]).""" + name, _, ranges = spec.partition(":") + ids = [] + for part in ranges.split(","): + lo, _, hi = part.partition("-") + ids += [f"workload-{n:02d}" for n in range(int(lo), int(hi or lo) + 1)] + return name, ids + + +def warmup_secs(set_name, workload): + config = CONFIG_DIR / set_name / workload / "clickhouse.yaml" + groups = yaml.safe_load(config.read_text())["query_groups"] + return min(g["client_options"]["starting_delay"] for g in groups) + + +def make_jobs(sets): + jobs = [] + for set_name, workloads in sets: + for workload in workloads: + for arm in ARMS: + config = CONFIG_DIR / set_name / workload / f"{ARMS[arm][1]}.yaml" + if not config.exists(): + raise SystemExit(f"missing {config}; render it first") + variant = set_name.count("-") > 1 # seed-862-long, not seed-862 + # Only for ordering: slow arms of stretched sets take longest. + estimate = warmup_secs(set_name, workload) + ( + (6000 if variant else 2400) if arm in SLOW_ARMS else 900 + ) + jobs.append( + { + "key": f"{set_name}/{workload}/{arm}", + "set": set_name, + "workload": workload, + "arm": arm, + "estimate_secs": estimate, + "attempts": 0, + "status": "queued", + "names": [], + } + ) + jobs.sort(key=lambda j: -j["estimate_secs"]) + return jobs + + +def experiment_name(job): + tag = job["set"].replace("seed-", "s").replace("-", "") + name = f"{tag}_{job['workload'].replace('workload-', 'w')}_{job['arm']}" + return name if job["attempts"] == 1 else f"{name}_r{job['attempts']}" + + +def command(job, name, slot, args): + runner, config, extra, _ = ARMS[job["arm"]] + cmd = [ + sys.executable, + runner, + f"experiment_type=random_workload/{job['set']}/{job['workload']}/{config}", + f"experiment.name={name}", + "providers.cloudlab.num_nodes=1", + f"providers.cloudlab.node_offset={2 * slot}", + f"providers.cloudlab.username={args.username}", + f"providers.cloudlab.hostname_suffix={args.hostname_suffix}", + "controller.punting=false", + *extra, + ] + if runner == "experiment_run_e2e.py": + # The Prometheus TSDB copy is large for long and x5, and unused. + cmd.append("flow.skip_copy_prometheus_data=true") + return cmd + + +def succeeded(job, name): + """The run exited 0 and recorded latencies and results.""" + out = OUTPUTS_DIR / name / ARMS[job["arm"]][3] / "prometheus_client_output" + for f in ("query_latencies.jsonl.gz", "query_results.jsonl.gz"): + path = out / f + if not path.exists() or path.stat().st_size == 0: + return False + try: + with gzip.open(out / "query_latencies.jsonl.gz", "rt") as f: + return bool(f.readline()) + except (OSError, EOFError): + return False + + +def clean_slot(slot, args): + for node in (2 * slot, 2 * slot + 1): + host = f"{args.username}@node{node}.{args.hostname_suffix}" + subprocess.run( + [ + "ssh", + "-o", + "StrictHostKeyChecking=no", + "-o", + "ConnectTimeout=20", + host, + CLEANUP, + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + timeout=120, + ) + + +def summarize(batch_dir, jobs, set_name, workload): + done = { + j["arm"]: j["names"][-1] + for j in jobs + if j["set"] == set_name and j["workload"] == workload and j["status"] == "done" + } + reference = REFERENCE_ARM if REFERENCE_ARM in done else "prom" + if reference not in done: + return + cmd = [sys.executable, str(COMPARE_ARMS), "--reference", reference] + for arm, name in done.items(): + cmd += ["--arm", f"{arm}={OUTPUTS_DIR / name / ARMS[arm][3]}"] + out = batch_dir / "summaries" / f"{set_name}_{workload}.txt" + out.parent.mkdir(exist_ok=True) + with open(out, "w") as f: + f.write(f"arms: {done}\n\n") + f.flush() + subprocess.run(cmd, stdout=f, stderr=subprocess.STDOUT, cwd=EXPERIMENTS_DIR) + + +def save(batch_dir, jobs): + tmp = batch_dir / "state.json.tmp" + tmp.write_text(json.dumps(jobs, indent=1)) + os.replace(tmp, batch_dir / "state.json") + + +def log(batch_dir, message): + line = f"{time.strftime('%Y-%m-%d %H:%M:%S')} {message}" + print(line, flush=True) + with open(batch_dir / "driver.log", "a") as f: + f.write(line + "\n") + + +def main(): + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + "--batch", required=True, help="name; state under experiment_outputs/" + ) + parser.add_argument("--username", required=True) + parser.add_argument("--hostname-suffix", required=True) + parser.add_argument( + "--slots", type=int, required=True, help="two nodes each, from node0" + ) + parser.add_argument( + "--set", action="append", required=True, metavar="SET:WORKLOADS" + ) + parser.add_argument("--stop-launching-after-hours", type=float) + parser.add_argument("--dry-run", action="store_true") + args = parser.parse_args() + + batch_dir = OUTPUTS_DIR / args.batch + batch_dir.mkdir(parents=True, exist_ok=True) + state = batch_dir / "state.json" + if state.exists(): + jobs = json.loads(state.read_text()) + for j in jobs: + if j["status"] == "running": # the driver died under it + j["status"] = "queued" + else: + jobs = make_jobs([parse_set(s) for s in args.set]) + save(batch_dir, jobs) + + if args.dry_run: + for j in jobs: + j["attempts"] += 1 + print( + f"{j['estimate_secs'] / 3600:4.1f} h " + + shlex.join(command(j, experiment_name(j), 0, args)) + ) + j["attempts"] -= 1 + print( + f"{len(jobs)} jobs, ~{sum(j['estimate_secs'] for j in jobs) / 3600:.0f} " + f"slot-hours on {args.slots} slots" + ) + return + + started = time.time() + running = {} # slot -> (job, process) + while True: + for slot, (job, proc) in list(running.items()): + if proc.poll() is None: + continue + del running[slot] + name = job["names"][-1] + if proc.returncode == 0 and succeeded(job, name): + job["status"] = "done" + elif job["attempts"] < MAX_ATTEMPTS: + job["status"] = "queued" + else: + job["status"] = "failed" + log( + batch_dir, + f"slot {slot}: {name} exit {proc.returncode} -> {job['status']}", + ) + save(batch_dir, jobs) + peers = [ + j + for j in jobs + if (j["set"], j["workload"]) == (job["set"], job["workload"]) + ] + if all(j["status"] in ("done", "failed") for j in peers): + summarize(batch_dir, jobs, job["set"], job["workload"]) + log(batch_dir, f"summary: {job['set']} {job['workload']}") + + launching = args.stop_launching_after_hours is None or ( + time.time() - started < args.stop_launching_after_hours * 3600 + ) + for slot in range(args.slots): + if slot in running or not launching: + continue + # Retries first, then the longest queued job. + queued = [j for j in jobs if j["status"] == "queued"] + if not queued: + break + job = max(queued, key=lambda j: (j["attempts"], j["estimate_secs"])) + clean_slot(slot, args) + job["attempts"] += 1 + name = experiment_name(job) + job["names"].append(name) + job["status"] = "running" + save(batch_dir, jobs) + logs = batch_dir / "logs" + logs.mkdir(exist_ok=True) + out = open(logs / f"{name}.log", "w") + proc = subprocess.Popen( + command(job, name, slot, args), + cwd=EXPERIMENTS_DIR, + stdout=out, + stderr=subprocess.STDOUT, + env={**os.environ, "PYTHONUNBUFFERED": "1"}, + ) + running[slot] = (job, proc) + log(batch_dir, f"slot {slot} (node{2 * slot}-{2 * slot + 1}): start {name}") + + if not running and ( + not launching or not any(j["status"] == "queued" for j in jobs) + ): + break + time.sleep(POLL_SECS) + + counts = { + s: sum(j["status"] == s for j in jobs) for s in ("done", "failed", "queued") + } + log(batch_dir, f"batch finished: {counts}") + + +if __name__ == "__main__": + main() diff --git a/asap-tools/experiments/tests/test_random_workload_batch.py b/asap-tools/experiments/tests/test_random_workload_batch.py new file mode 100644 index 00000000..c9b1f1e0 --- /dev/null +++ b/asap-tools/experiments/tests/test_random_workload_batch.py @@ -0,0 +1,40 @@ +"""Tests for random_workload_batch.py's job planning.""" + +import unittest + +import random_workload_batch as batch + + +class BatchPlanningTest(unittest.TestCase): + def test_sets_name_their_workloads(self): + self.assertEqual( + batch.parse_set("seed-862-x5:01,03-04"), + ("seed-862-x5", ["workload-01", "workload-03", "workload-04"]), + ) + + def test_retries_get_a_new_experiment_name(self): + # A rerun under the old name hits the earlier run's ClickHouse data + # directory, which the runner can't chmod. + job = {"set": "seed-862-long", "workload": "workload-07", "arm": "ch"} + names = [] + for attempts in (1, 2): + names.append(batch.experiment_name({**job, "attempts": attempts})) + self.assertEqual(names, ["s862long_w07_ch", "s862long_w07_ch_r2"]) + + def test_each_job_runs_on_its_slots_two_nodes(self): + job = {"set": "seed-862", "workload": "workload-01", "arm": "prom"} + + class Args: + username, hostname_suffix = "u", "s" + + cmd = batch.command(job, "s862_w01_prom", 3, Args) + self.assertIn("providers.cloudlab.num_nodes=1", cmd) + self.assertIn("providers.cloudlab.node_offset=6", cmd) + self.assertIn("flow.skip_copy_prometheus_data=true", cmd) + self.assertIn( + "experiment_params.experiment=[{mode:baseline,server:prometheus}]", cmd + ) + + +if __name__ == "__main__": + unittest.main() From c449330c6ff27907d788c0550eecb3491718fb23 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 08:27:14 -0400 Subject: [PATCH 02/29] feat(asap-tools): resume a batch around runs still in flight On resume, a job whose run is still alive is left alone (orphaned) instead of rerun, and --skip-slots keeps the driver off busy nodes. Co-Authored-By: Claude Opus 5.5 --- .../experiments/random_workload_batch.py | 21 ++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/asap-tools/experiments/random_workload_batch.py b/asap-tools/experiments/random_workload_batch.py index bc874980..9eed2484 100644 --- a/asap-tools/experiments/random_workload_batch.py +++ b/asap-tools/experiments/random_workload_batch.py @@ -227,6 +227,12 @@ def main(): "--set", action="append", required=True, metavar="SET:WORKLOADS" ) parser.add_argument("--stop-launching-after-hours", type=float) + parser.add_argument( + "--skip-slots", + type=lambda s: {int(x) for x in s.split(",")}, + default=set(), + help="slots to leave alone, e.g. 3,15,16 (their nodes are busy)", + ) parser.add_argument("--dry-run", action="store_true") args = parser.parse_args() @@ -236,8 +242,17 @@ def main(): if state.exists(): jobs = json.loads(state.read_text()) for j in jobs: - if j["status"] == "running": # the driver died under it - j["status"] = "queued" + if j["status"] == "running": + # A run the earlier driver left behind keeps going on its own; + # this driver neither reruns nor watches it. + alive = ( + subprocess.run( + ["pgrep", "-f", f"experiment.name={j['names'][-1]}( |$)"], + stdout=subprocess.DEVNULL, + ).returncode + == 0 + ) + j["status"] = "orphaned" if alive else "queued" else: jobs = make_jobs([parse_set(s) for s in args.set]) save(batch_dir, jobs) @@ -288,7 +303,7 @@ def main(): time.time() - started < args.stop_launching_after_hours * 3600 ) for slot in range(args.slots): - if slot in running or not launching: + if slot in running or slot in args.skip_slots or not launching: continue # Retries first, then the longest queued job. queued = [j for j in jobs if j["status"] == "queued"] From 7c01819c1932f366e5349e36664d18ae0dea4016 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 09:29:12 -0400 Subject: [PATCH 03/29] feat(asap-tools): collate and plot a random-workload batch collate_batch.py writes one row per (set, workload, arm): CPU, memory, latency p50/p95/sum, failed queries, the feeder's ingest lag, and whether the run is valid (no failed queries, answers agreeing with Prometheus). It reports each baseline's ratio to ASAPQuery over valid pairs. plot_cost_latency.py draws p95 latency against total CPU per set, invalid runs hollow. compare_arms now reports failed queries instead of crashing. Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/collate_batch.py | 226 ++++++++++++++++++ .../multi_experiment/compare_arms.py | 20 +- .../multi_experiment/plot_cost_latency.py | 100 ++++++++ 3 files changed, 341 insertions(+), 5 deletions(-) create mode 100644 asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py create mode 100644 asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py diff --git a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py new file mode 100644 index 00000000..95db09a9 --- /dev/null +++ b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py @@ -0,0 +1,226 @@ +#!/usr/bin/env python3 +"""Collate a random_workload_batch.py batch: one row per (set, workload, arm) +with total CPU-seconds (warm-up + queries), peak memory, the median, p95 and +sum of every answered query's latency, and whether the run is valid: + +- no query failed or timed out; +- its answers agree with the reference arm (Prometheus, which keeps up): + exact arms to EXACT_REL_ERR, ASAPQuery's sketches to their bounds. + +Separately, whether ingest kept up: during the query phase the feeder never +ran more than MAX_INGEST_LAG_SECS behind (from the feeder's +/paced_feeder_output/*_stats.json). A lagging system answers on-time +queries from incomplete windows, which the agreement check catches; one that +answers late but correctly is slow, not wrong, and stays valid. + +Then each baseline's ratio to ASAPQuery (baseline / asap, > 1 means ASAPQuery +is better) over workloads where both runs are valid. Only workloads whose +arms all finished are collated. + +Usage: + python3 collate_batch.py / [--csv out.csv] +""" + +import argparse +import csv +import glob +import json +import statistics +import sys +from collections import defaultdict +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from compare_arms import Arm, agreement, percentile # noqa: E402 + +sys.path.insert(0, str(Path(__file__).resolve().parents[2])) +from random_workload_batch import ARMS, OUTPUTS_DIR # noqa: E402 + +REFERENCE = "prom" +# The queries' freshness delay: a feed further behind leaves on-time +# queries' windows incomplete. +MAX_INGEST_LAG_SECS = 10 +EXACT_REL_ERR = 1e-3 +EXACT_TOPK_PRECISION = 0.99 +# ASAPQuery's sketches: quantiles at the 0.95 accuracy SLA; sums are exact +# but for a window-boundary sample; top-k at its precision target. +ASAP_QUANTILE_REL_ERR = 0.05 +ASAP_SUM_REL_ERR = 5e-3 +ASAP_TOPK_PRECISION = 0.95 + +FIELDS = [ + "set", + "workload", + "arm", + "valid", + "why_invalid", + "ingest_kept_up", + "cpu_secs", + "warmup_cpu_secs", + "query_cpu_secs", + "peak_memory_gib", + "latency_p50", + "latency_p95", + "latency_sum", + "queries", + "failed_queries", + "max_ingest_lag_secs", +] + + +def max_ingest_lag(arm, mode_dir): + """The feeder's largest start lag (s) during the query phase, or None + without its stats.""" + stats = glob.glob(str(mode_dir / "paced_feeder_output" / "*_stats.json")) + if not stats: + return None + ticks = json.loads(Path(stats[0]).read_text())["ticks"] + query_ticks = [t for t in ticks if t["tick"] >= arm.warmup_secs] or ticks + return max(t["start_lag_ms"] for t in query_ticks) / 1000 + + +def disagreements(arm_name, arm, ref): + """The queries whose answers fall outside `arm_name`'s bounds against + the reference arm `ref`.""" + bad = [] + for gq, name in arm.names.items(): + topk = name.startswith("topk") + quantile = "quantile" in name + values = [] + for key, want in ref.results.items(): + if key[:2] != gq or key not in arm.results or not want: + continue + value, _ = agreement(arm.results[key], want, topk) + if value is not None: + values.append(value) + if not values: + bad.append(f"{name}: no comparable windows") + continue + mean = statistics.mean(values) + if arm_name == "asap": + ok = ( + mean >= ASAP_TOPK_PRECISION + if topk + else mean <= (ASAP_QUANTILE_REL_ERR if quantile else ASAP_SUM_REL_ERR) + ) + else: + ok = mean >= EXACT_TOPK_PRECISION if topk else mean <= EXACT_REL_ERR + if not ok: + bad.append(f"{name}: {'precision' if topk else 'rel err'} {mean:.3g}") + return bad + + +def rows(batch_dir): + jobs = json.loads((Path(batch_dir) / "state.json").read_text()) + by_workload = defaultdict(dict) + for j in jobs: + by_workload[(j["set"], j["workload"])][j["arm"]] = j + out = [] + for (set_name, workload), jobs_by_arm in sorted(by_workload.items()): + if set(jobs_by_arm) != set(ARMS) or any( + j["status"] != "done" for j in jobs_by_arm.values() + ): + continue + dirs = { + a: OUTPUTS_DIR / j["names"][-1] / ARMS[a][3] for a, j in jobs_by_arm.items() + } + arms = {a: Arm(d) for a, d in dirs.items()} + for arm_name, arm in arms.items(): + r = arm.resources() + recorded = [x for xs in arm.latencies.values() for x in xs] + # A query that failed or timed out records no latency. + latencies = [x for x in recorded if x is not None] + lag = max_ingest_lag(arm, dirs[arm_name]) + why = [] + if len(recorded) != len(latencies): + why.append(f"{len(recorded) - len(latencies)} failed queries") + if arm_name != REFERENCE: + why += disagreements(arm_name, arm, arms[REFERENCE]) + out.append( + { + "set": set_name, + "workload": workload, + "arm": arm_name, + "valid": not why, + "why_invalid": "; ".join(why), + "ingest_kept_up": ( + None if lag is None else lag <= MAX_INGEST_LAG_SECS + ), + "cpu_secs": round(r["cpu_secs"]), + "warmup_cpu_secs": round(r["warmup_cpu_secs"]), + "query_cpu_secs": round(r["query_cpu_secs"]), + "peak_memory_gib": round(r["peak_memory_bytes"] / 2**30, 2), + "latency_p50": round(statistics.median(latencies), 3), + "latency_p95": round(percentile(latencies, 95), 3), + "latency_sum": round(sum(latencies), 1), + "queries": len(recorded), + "failed_queries": len(recorded) - len(latencies), + "max_ingest_lag_secs": None if lag is None else round(lag, 1), + } + ) + return out + + +def ratios(table): + """Per set and baseline arm: the geometric mean, over workloads where + both it and ASAPQuery are valid, of baseline / asap.""" + by = {(r["set"], r["workload"], r["arm"]): r for r in table} + result = defaultdict(lambda: defaultdict(list)) + for (set_name, workload, arm), r in by.items(): + asap = by[(set_name, workload, "asap")] + if arm == "asap" or not (r["valid"] and asap["valid"]): + continue + for key in ("cpu_secs", "latency_p50", "latency_p95", "latency_sum"): + result[(set_name, arm)][key].append(r[key] / asap[key]) + return { + k: { + **{m: statistics.geometric_mean(v) for m, v in metrics.items()}, + "n": len(metrics["cpu_secs"]), + } + for k, metrics in result.items() + } + + +def main(): + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument("batch_dir") + parser.add_argument("--csv") + args = parser.parse_args() + table = rows(args.batch_dir) + if args.csv: + with open(args.csv, "w", newline="") as f: + w = csv.DictWriter(f, FIELDS) + w.writeheader() + w.writerows(table) + print("Invalid runs:") + for r in table: + if not r["valid"]: + print(f" {r['set']} {r['workload']} {r['arm']}: {r['why_invalid']}") + print( + "\nIngest fell behind (feeder lag > {}s in the query phase):".format( + MAX_INGEST_LAG_SECS + ) + ) + for r in table: + if r["ingest_kept_up"] is False: + print( + f" {r['set']} {r['workload']} {r['arm']}: {r['max_ingest_lag_secs']}s" + ) + print( + "\nASAPQuery improvement = baseline / asap, geometric mean over workloads " + "where both runs are valid" + ) + print( + f"{'set':14s} {'baseline':9s} {'n':>2s} {'CPU':>7s} {'p50':>7s} {'p95':>7s} {'sum':>7s}" + ) + for (set_name, arm), m in sorted(ratios(table).items()): + print( + f"{set_name:14s} {arm:9s} {m['n']:2d} {m['cpu_secs']:6.1f}x " + f"{m['latency_p50']:6.1f}x {m['latency_p95']:6.1f}x {m['latency_sum']:6.1f}x" + ) + + +if __name__ == "__main__": + main() diff --git a/asap-tools/experiments/post_experiment/multi_experiment/compare_arms.py b/asap-tools/experiments/post_experiment/multi_experiment/compare_arms.py index 7578016f..b8f1b707 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/compare_arms.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/compare_arms.py @@ -137,13 +137,23 @@ def report(arms, reference): interval = arms[reference].interval_secs[gq[0]] print(f"\n[{gq[0]}.{gq[1]}] every {interval:g}s: {name}") for arm_name, arm in arms.items(): - xs = arm.latencies.get(gq) - if not xs: + recorded = arm.latencies.get(gq) + if not recorded: continue - within = sum(x <= interval for x in xs) / len(xs) + # A failed or timed-out query records no latency; it never counts + # as answered within the interval. + xs = [x for x in recorded if x is not None] + failed = len(recorded) - len(xs) + within = sum(x <= interval for x in xs) / len(recorded) + stats = ( + f"{percentile(xs, 50):8.3f} {percentile(xs, 95):8.3f} " + f"{percentile(xs, 99):8.3f}" + if xs + else f"{'-':>8s} {'-':>8s} {'-':>8s}" + ) print( - f" {arm_name:16s} n={len(xs):5d} {percentile(xs, 50):8.3f} " - f"{percentile(xs, 95):8.3f} {percentile(xs, 99):8.3f} {within:6.1%}" + f" {arm_name:16s} n={len(recorded):5d} {stats} {within:6.1%}" + + (f" FAILED {failed}" if failed else "") ) print(f"\n== Agreement with {reference}") diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py new file mode 100644 index 00000000..f83a2c18 --- /dev/null +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +"""Cost-latency scatter from collate_batch.py's CSV: one panel per workload +set, one point per (workload, system); x = p95 query latency, y = total +CPU-seconds, both log. Lower left is better. A run collate_batch.py marks +invalid (failed queries, or answers that disagree with the reference) is +drawn hollow: its latency and CPU don't stand for a correct answer. + +Usage: python3 plot_cost_latency.py collated.csv out.png +""" + +import csv +import sys +from collections import defaultdict + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 + +# Validated categorical slots 1-5 (fixed order) and a marker per system, so +# identity never rests on color alone. +SYSTEMS = [ + ("asap", "ASAPQuery", "#2a78d6", "o", (8, 6)), + ("prom", "Prometheus", "#eb6834", "s", (8, 6)), + ("vm", "VictoriaMetrics", "#1baf7a", "^", (8, -14)), + ("ch", "ClickHouse", "#eda100", "D", (8, 6)), + ("chmv", "ClickHouse+MV", "#e87ba4", "v", (-30, 12)), +] +SURFACE, INK, MUTED = "#fcfcfb", "#0b0b0b", "#52514e" + + +def main(csv_path, out_path): + rows = list(csv.DictReader(open(csv_path))) + by_set = defaultdict(list) + for r in rows: + by_set[r["set"]].append(r) + sets = sorted(by_set) + fig, axes = plt.subplots( + 1, len(sets), figsize=(6 * len(sets), 5), facecolor=SURFACE, squeeze=False + ) + for ax, set_name in zip(axes[0], sets): + ax.set_facecolor(SURFACE) + workloads = sorted({r["workload"] for r in by_set[set_name]}) + for key, label, color, marker, offset in SYSTEMS: + pts = [r for r in by_set[set_name] if r["arm"] == key] + xs = [float(r["latency_p95"]) for r in pts] + ys = [float(r["cpu_secs"]) for r in pts] + valid = [r["valid"] == "True" for r in pts] + ax.scatter( + [x for x, v in zip(xs, valid) if v], + [y for y, v in zip(ys, valid) if v], + s=64, + color=color, + marker=marker, + label=label, + edgecolors=SURFACE, + linewidths=2, + zorder=3, + ) + ax.scatter( + [x for x, v in zip(xs, valid) if not v], + [y for y, v in zip(ys, valid) if not v], + s=56, + facecolors="none", + edgecolors=color, + marker=marker, + linewidths=1.5, + zorder=3, + ) + if xs: + # Direct label at the system's median point. + mx, my = sorted(xs)[len(xs) // 2], sorted(ys)[len(ys) // 2] + ax.annotate( + label, + (mx, my), + xytext=offset, + textcoords="offset points", + fontsize=9, + color=INK, + ) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel("p95 query latency (s)", color=MUTED) + ax.set_ylabel("total CPU (CPU-seconds, warm-up + queries)", color=MUTED) + title = ( + f"{set_name}: {len(workloads)} workload(s), lower left is better" + "\nhollow: invalid run (failed queries or wrong answers)" + ) + ax.set_title(title, color=INK, fontsize=10, loc="left") + ax.grid(True, which="major", color="#e6e5e1", linewidth=0.8) + ax.tick_params(colors=MUTED) + for spine in ax.spines.values(): + spine.set_color("#d4d3cf") + axes[0][0].legend(frameon=False, fontsize=9, loc="upper left") + fig.tight_layout() + fig.savefig(out_path, dpi=150, facecolor=SURFACE) + + +if __name__ == "__main__": + main(*sys.argv[1:3]) From d424e2cca53cf2fb1d0ae2e906b8bfcb10c94fc4 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 09:54:58 -0400 Subject: [PATCH 04/29] feat(asap-tools): VictoriaMetrics boundary tolerance; timestamped plots Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/collate_batch.py | 5 ++++- .../multi_experiment/plot_cost_latency.py | 14 +++++++++++--- 2 files changed, 15 insertions(+), 4 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py index 95db09a9..144b1478 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py @@ -41,6 +41,8 @@ # queries' windows incomplete. MAX_INGEST_LAG_SECS = 10 EXACT_REL_ERR = 1e-3 +# VictoriaMetrics may count a window-boundary sample Prometheus doesn't. +VM_REL_ERR = 5e-3 EXACT_TOPK_PRECISION = 0.99 # ASAPQuery's sketches: quantiles at the 0.95 accuracy SLA; sums are exact # but for a window-boundary sample; top-k at its precision target. @@ -104,7 +106,8 @@ def disagreements(arm_name, arm, ref): else mean <= (ASAP_QUANTILE_REL_ERR if quantile else ASAP_SUM_REL_ERR) ) else: - ok = mean >= EXACT_TOPK_PRECISION if topk else mean <= EXACT_REL_ERR + rel_err = VM_REL_ERR if arm_name == "vm" else EXACT_REL_ERR + ok = mean >= EXACT_TOPK_PRECISION if topk else mean <= rel_err if not ok: bad.append(f"{name}: {'precision' if topk else 'rel err'} {mean:.3g}") return bad diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index f83a2c18..c0831440 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -5,11 +5,15 @@ invalid (failed queries, or answers that disagree with the reference) is drawn hollow: its latency and CPU don't stand for a correct answer. -Usage: python3 plot_cost_latency.py collated.csv out.png +Writes /cost_latency__.png; never overwrites. + +Usage: python3 plot_cost_latency.py collated.csv out_dir """ import csv +import datetime import sys +from pathlib import Path from collections import defaultdict import matplotlib @@ -29,7 +33,7 @@ SURFACE, INK, MUTED = "#fcfcfb", "#0b0b0b", "#52514e" -def main(csv_path, out_path): +def main(csv_path, out_dir): rows = list(csv.DictReader(open(csv_path))) by_set = defaultdict(list) for r in rows: @@ -93,7 +97,11 @@ def main(csv_path, out_path): spine.set_color("#d4d3cf") axes[0][0].legend(frameon=False, fontsize=9, loc="upper left") fig.tight_layout() - fig.savefig(out_path, dpi=150, facecolor=SURFACE) + stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + out_path = Path(out_dir) / f"cost_latency_{'+'.join(sets)}_{stamp}.png" + with open(out_path, "xb") as f: + fig.savefig(f, dpi=150, facecolor=SURFACE, format="png") + print(out_path) if __name__ == "__main__": From e6922abb1480ae0bd0fd92f171c0555cb05d3352 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 10:10:50 -0400 Subject: [PATCH 05/29] feat(asap-tools): larger, unlabeled cost-latency plots; LiteOlly name Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 45 ++++++++----------- 1 file changed, 18 insertions(+), 27 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index c0831440..41ff20e2 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -24,11 +24,11 @@ # Validated categorical slots 1-5 (fixed order) and a marker per system, so # identity never rests on color alone. SYSTEMS = [ - ("asap", "ASAPQuery", "#2a78d6", "o", (8, 6)), - ("prom", "Prometheus", "#eb6834", "s", (8, 6)), - ("vm", "VictoriaMetrics", "#1baf7a", "^", (8, -14)), - ("ch", "ClickHouse", "#eda100", "D", (8, 6)), - ("chmv", "ClickHouse+MV", "#e87ba4", "v", (-30, 12)), + ("asap", "LiteOlly", "#2a78d6", "o"), + ("prom", "Prometheus", "#eb6834", "s"), + ("vm", "VictoriaMetrics", "#1baf7a", "^"), + ("ch", "ClickHouse", "#eda100", "D"), + ("chmv", "ClickHouse+MV", "#e87ba4", "v"), ] SURFACE, INK, MUTED = "#fcfcfb", "#0b0b0b", "#52514e" @@ -40,12 +40,12 @@ def main(csv_path, out_dir): by_set[r["set"]].append(r) sets = sorted(by_set) fig, axes = plt.subplots( - 1, len(sets), figsize=(6 * len(sets), 5), facecolor=SURFACE, squeeze=False + 1, len(sets), figsize=(7 * len(sets), 6), facecolor=SURFACE, squeeze=False ) for ax, set_name in zip(axes[0], sets): ax.set_facecolor(SURFACE) workloads = sorted({r["workload"] for r in by_set[set_name]}) - for key, label, color, marker, offset in SYSTEMS: + for key, label, color, marker in SYSTEMS: pts = [r for r in by_set[set_name] if r["arm"] == key] xs = [float(r["latency_p95"]) for r in pts] ys = [float(r["cpu_secs"]) for r in pts] @@ -53,49 +53,40 @@ def main(csv_path, out_dir): ax.scatter( [x for x, v in zip(xs, valid) if v], [y for y, v in zip(ys, valid) if v], - s=64, + s=128, color=color, marker=marker, label=label, edgecolors=SURFACE, - linewidths=2, + linewidths=1, zorder=3, ) ax.scatter( [x for x, v in zip(xs, valid) if not v], [y for y, v in zip(ys, valid) if not v], - s=56, + s=112, facecolors="none", edgecolors=color, marker=marker, - linewidths=1.5, + linewidths=2.5, zorder=3, ) - if xs: - # Direct label at the system's median point. - mx, my = sorted(xs)[len(xs) // 2], sorted(ys)[len(ys) // 2] - ax.annotate( - label, - (mx, my), - xytext=offset, - textcoords="offset points", - fontsize=9, - color=INK, - ) ax.set_xscale("log") ax.set_yscale("log") - ax.set_xlabel("p95 query latency (s)", color=MUTED) - ax.set_ylabel("total CPU (CPU-seconds, warm-up + queries)", color=MUTED) + ax.set_xlabel("p95 query latency (s)", color=MUTED, fontsize=14) + ax.set_ylabel( + "total CPU (CPU-seconds, warm-up + queries)", color=MUTED, fontsize=14 + ) title = ( f"{set_name}: {len(workloads)} workload(s), lower left is better" "\nhollow: invalid run (failed queries or wrong answers)" ) - ax.set_title(title, color=INK, fontsize=10, loc="left") + ax.set_title(title, color=INK, fontsize=14, loc="left") ax.grid(True, which="major", color="#e6e5e1", linewidth=0.8) - ax.tick_params(colors=MUTED) + ax.tick_params(colors=MUTED, labelsize=13) for spine in ax.spines.values(): spine.set_color("#d4d3cf") - axes[0][0].legend(frameon=False, fontsize=9, loc="upper left") + axes[0][0].legend(frameon=False, fontsize=13, loc="best") fig.tight_layout() stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") out_path = Path(out_dir) / f"cost_latency_{'+'.join(sets)}_{stamp}.png" From 944bf684d56cf328128fdae59b08abfc6a327715 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 10:17:09 -0400 Subject: [PATCH 06/29] feat(asap-tools): LiteOlly improvement over the worst baseline beside each scatter Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 59 ++++++++++++++++++- 1 file changed, 57 insertions(+), 2 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index 41ff20e2..bef561f6 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -5,6 +5,10 @@ invalid (failed queries, or answers that disagree with the reference) is drawn hollow: its latency and CPU don't stand for a correct answer. +Beside each scatter, two bars: LiteOlly's improvement in CPU and in p95 +latency over the worst baseline for that metric, as collate_batch.py's +geometric mean of baseline / LiteOlly over workloads where both are valid. + Writes /cost_latency__.png; never overwrites. Usage: python3 plot_cost_latency.py collated.csv out_dir @@ -21,6 +25,9 @@ matplotlib.use("Agg") import matplotlib.pyplot as plt # noqa: E402 +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from collate_batch import ratios # noqa: E402 + # Validated categorical slots 1-5 (fixed order) and a marker per system, so # identity never rests on color alone. SYSTEMS = [ @@ -39,10 +46,23 @@ def main(csv_path, out_dir): for r in rows: by_set[r["set"]].append(r) sets = sorted(by_set) + numeric = ("cpu_secs", "latency_p50", "latency_p95", "latency_sum") + improvement = ratios( + [ + {**r, "valid": r["valid"] == "True", **{k: float(r[k]) for k in numeric}} + for r in rows + ] + ) + names = {key: label for key, label, *_ in SYSTEMS} fig, axes = plt.subplots( - 1, len(sets), figsize=(7 * len(sets), 6), facecolor=SURFACE, squeeze=False + 1, + 2 * len(sets), + figsize=(9.5 * len(sets), 6), + facecolor=SURFACE, + squeeze=False, + gridspec_kw={"width_ratios": [3, 1.3] * len(sets)}, ) - for ax, set_name in zip(axes[0], sets): + for ax, bar_ax, set_name in zip(axes[0][::2], axes[0][1::2], sets): ax.set_facecolor(SURFACE) workloads = sorted({r["workload"] for r in by_set[set_name]}) for key, label, color, marker in SYSTEMS: @@ -86,6 +106,41 @@ def main(csv_path, out_dir): ax.tick_params(colors=MUTED, labelsize=13) for spine in ax.spines.values(): spine.set_color("#d4d3cf") + bars = [] + for metric, what in (("cpu_secs", "CPU"), ("latency_p95", "p95 latency")): + arm, m = max( + ((arm, m) for (s, arm), m in improvement.items() if s == set_name), + key=lambda am: am[1][metric], + ) + bars.append((what, names[arm], m[metric])) + whats, worst, heights = zip(*bars) + # One worst baseline goes in the title; two go under their bars. + one = len(set(worst)) == 1 + labels = whats if one else [f"{w}\n{b}" for w, b in zip(whats, worst)] + bar_ax.set_facecolor(SURFACE) + bar_ax.bar(labels, heights, width=0.5, color=SYSTEMS[0][2], zorder=3) + for i, h in enumerate(heights): + bar_ax.annotate( + f"{h:.1f}x", + (i, h), + xytext=(0, 4), + textcoords="offset points", + ha="center", + color=INK, + fontsize=13, + ) + bar_ax.set_ylim(0, max(heights) * 1.15) + bar_ax.set_ylabel("LiteOlly improvement, geomean (x)", color=MUTED, fontsize=14) + bar_ax.set_title( + "vs worst baseline" + (f":\n{worst[0]}" if one else ""), + color=INK, + fontsize=14, + loc="left", + ) + bar_ax.grid(True, axis="y", color="#e6e5e1", linewidth=0.8) + bar_ax.tick_params(colors=MUTED, labelsize=12) + for spine in bar_ax.spines.values(): + spine.set_color("#d4d3cf") axes[0][0].legend(frameon=False, fontsize=13, loc="best") fig.tight_layout() stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") From defa76eea51a5be44a111fc6e635c4a8d7a9d6de Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 10:20:16 -0400 Subject: [PATCH 07/29] feat(asap-tools): improvement arrows in the scatter instead of bars Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 108 ++++++++++-------- 1 file changed, 60 insertions(+), 48 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index bef561f6..331f21b0 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -5,9 +5,10 @@ invalid (failed queries, or answers that disagree with the reference) is drawn hollow: its latency and CPU don't stand for a correct answer. -Beside each scatter, two bars: LiteOlly's improvement in CPU and in p95 -latency over the worst baseline for that metric, as collate_batch.py's -geometric mean of baseline / LiteOlly over workloads where both are valid. +Two arrows from LiteOlly's geometric-mean point show its improvement in CPU +(vertical) and p95 latency (horizontal) over the worst baseline for that +metric: collate_batch.py's geometric mean of baseline / LiteOlly over +workloads where both are valid. Arrow lengths are those ratios. Writes /cost_latency__.png; never overwrites. @@ -16,6 +17,7 @@ import csv import datetime +import statistics import sys from pathlib import Path from collections import defaultdict @@ -40,6 +42,50 @@ SURFACE, INK, MUTED = "#fcfcfb", "#0b0b0b", "#52514e" +def arrows(ax, rows, improvement, set_name): + """LiteOlly's improvement over the worst baseline per metric, drawn from + LiteOlly's geometric-mean point (over its valid runs, else all).""" + names = {key: label for key, label, *_ in SYSTEMS} + lite = [r for r in rows if r["arm"] == "asap"] + lite = [r for r in lite if r["valid"] == "True"] or lite + lx = statistics.geometric_mean(float(r["latency_p95"]) for r in lite) + ly = statistics.geometric_mean(float(r["cpu_secs"]) for r in lite) + worst = {} + for metric in ("cpu_secs", "latency_p95"): + worst[metric] = max( + ((arm, m[metric]) for (s, arm), m in improvement.items() if s == set_name), + key=lambda am: am[1], + ) + style = dict(arrowstyle="->", color=INK, linewidth=2, mutation_scale=22) + arm, cpu = worst["cpu_secs"] + x = lx / 1.5 + ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) + ax.text( + x * 1.08, + ly * cpu**0.75, + f"{cpu:.1f}x less CPU\nvs {names[arm]}", + color=INK, + fontsize=13, + va="center", + ) + arm, lat = worst["latency_p95"] + y = ly / 1.4 + ax.annotate("", (lx, y), xytext=(lx * lat, y), arrowprops=style, zorder=4) + ax.text( + lx * lat**0.5, + y / 1.08, + f"{lat:.1f}x lower p95 latency vs {names[arm]}", + color=INK, + fontsize=13, + ha="center", + va="top", + ) + left, right = ax.get_xlim() + ax.set_xlim(min(left, lx / 2), right) + bottom, top = ax.get_ylim() + ax.set_ylim(min(bottom, ly / 2), top) + + def main(csv_path, out_dir): rows = list(csv.DictReader(open(csv_path))) by_set = defaultdict(list) @@ -53,16 +99,10 @@ def main(csv_path, out_dir): for r in rows ] ) - names = {key: label for key, label, *_ in SYSTEMS} fig, axes = plt.subplots( - 1, - 2 * len(sets), - figsize=(9.5 * len(sets), 6), - facecolor=SURFACE, - squeeze=False, - gridspec_kw={"width_ratios": [3, 1.3] * len(sets)}, + 1, len(sets), figsize=(7 * len(sets), 6), facecolor=SURFACE, squeeze=False ) - for ax, bar_ax, set_name in zip(axes[0][::2], axes[0][1::2], sets): + for ax, set_name in zip(axes[0], sets): ax.set_facecolor(SURFACE) workloads = sorted({r["workload"] for r in by_set[set_name]}) for key, label, color, marker in SYSTEMS: @@ -106,43 +146,15 @@ def main(csv_path, out_dir): ax.tick_params(colors=MUTED, labelsize=13) for spine in ax.spines.values(): spine.set_color("#d4d3cf") - bars = [] - for metric, what in (("cpu_secs", "CPU"), ("latency_p95", "p95 latency")): - arm, m = max( - ((arm, m) for (s, arm), m in improvement.items() if s == set_name), - key=lambda am: am[1][metric], - ) - bars.append((what, names[arm], m[metric])) - whats, worst, heights = zip(*bars) - # One worst baseline goes in the title; two go under their bars. - one = len(set(worst)) == 1 - labels = whats if one else [f"{w}\n{b}" for w, b in zip(whats, worst)] - bar_ax.set_facecolor(SURFACE) - bar_ax.bar(labels, heights, width=0.5, color=SYSTEMS[0][2], zorder=3) - for i, h in enumerate(heights): - bar_ax.annotate( - f"{h:.1f}x", - (i, h), - xytext=(0, 4), - textcoords="offset points", - ha="center", - color=INK, - fontsize=13, - ) - bar_ax.set_ylim(0, max(heights) * 1.15) - bar_ax.set_ylabel("LiteOlly improvement, geomean (x)", color=MUTED, fontsize=14) - bar_ax.set_title( - "vs worst baseline" + (f":\n{worst[0]}" if one else ""), - color=INK, - fontsize=14, - loc="left", - ) - bar_ax.grid(True, axis="y", color="#e6e5e1", linewidth=0.8) - bar_ax.tick_params(colors=MUTED, labelsize=12) - for spine in bar_ax.spines.values(): - spine.set_color("#d4d3cf") - axes[0][0].legend(frameon=False, fontsize=13, loc="best") - fig.tight_layout() + arrows(ax, by_set[set_name], improvement, set_name) + fig.legend( + *axes[0][0].get_legend_handles_labels(), + frameon=False, + fontsize=13, + loc="lower center", + ncol=len(SYSTEMS), + ) + fig.tight_layout(rect=(0, 0.07, 1, 1)) stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") out_path = Path(out_dir) / f"cost_latency_{'+'.join(sets)}_{stamp}.png" with open(out_path, "xb") as f: From 7a2e5828c2b53fa96b823e2a14c8e0063ecb8b5f Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 11:06:47 -0400 Subject: [PATCH 08/29] feat(asap-tools): time-ordered raw ClickHouse table; batch reruns of chosen arms The raw table's label-first order made every query read the whole table. The batch driver can rerun some arms under a name tag and from a config variant directory. Co-Authored-By: Claude Opus 5.5 --- .../fake_metric/sketch_bench_workloads.py | 6 +++- .../experiments/random_workload_batch.py | 31 ++++++++++++++++--- .../tests/test_random_workload_batch.py | 29 +++++++++++++++++ .../tests/test_sketch_bench_workloads.py | 5 +++ 4 files changed, 65 insertions(+), 6 deletions(-) diff --git a/asap-tools/experiments/datasets/fake_metric/sketch_bench_workloads.py b/asap-tools/experiments/datasets/fake_metric/sketch_bench_workloads.py index 01fd972f..cb3655d1 100644 --- a/asap-tools/experiments/datasets/fake_metric/sketch_bench_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/sketch_bench_workloads.py @@ -581,10 +581,14 @@ def init_sql(metrics: dict, views: List[View], create_views: bool) -> str: columns = [f"{name} String" for name in names] + [ f"{value} Float64" for value in value_columns(desc) ] + # Raw queries read a time window across all series, so only a time + # order lets ClickHouse skip what's outside it. Views read their own + # tables; their source keeps its label order. + order = f"({', '.join(names)}, ts)" if create_views else "ts" statements += [ f"DROP TABLE IF EXISTS {table}", f"CREATE TABLE {table} (ts DateTime64(3), {', '.join(columns)}) " - f"ENGINE = MergeTree ORDER BY ({', '.join(names)}, ts)", + f"ENGINE = MergeTree ORDER BY {order}", ] if create_views: statements += [view_ddl(view) for view in views] diff --git a/asap-tools/experiments/random_workload_batch.py b/asap-tools/experiments/random_workload_batch.py index 9eed2484..06bfa370 100644 --- a/asap-tools/experiments/random_workload_batch.py +++ b/asap-tools/experiments/random_workload_batch.py @@ -19,6 +19,9 @@ --username milindsr --hostname-suffix --slots 21 \\ --set seed-862:01-10 --set seed-862-long:01-10 --set seed-862-x5:01 \\ [--dry-run] [--stop-launching-after-hours 7] + +A rerun of some arms with changed configs: --arm ch --tag ts names the runs +s862_w01_ch_ts, ...; --config-variant sla10 reads -sla10/ configs. """ import argparse @@ -90,12 +93,13 @@ def warmup_secs(set_name, workload): return min(g["client_options"]["starting_delay"] for g in groups) -def make_jobs(sets): +def make_jobs(sets, arms=tuple(ARMS), tag="", config_variant=""): jobs = [] for set_name, workloads in sets: for workload in workloads: - for arm in ARMS: - config = CONFIG_DIR / set_name / workload / f"{ARMS[arm][1]}.yaml" + config_dir = f"{workload}-{config_variant}" if config_variant else workload + for arm in arms: + config = CONFIG_DIR / set_name / config_dir / f"{ARMS[arm][1]}.yaml" if not config.exists(): raise SystemExit(f"missing {config}; render it first") variant = set_name.count("-") > 1 # seed-862-long, not seed-862 @@ -109,6 +113,8 @@ def make_jobs(sets): "set": set_name, "workload": workload, "arm": arm, + "tag": tag, + "config_dir": config_dir, "estimate_secs": estimate, "attempts": 0, "status": "queued", @@ -122,6 +128,8 @@ def make_jobs(sets): def experiment_name(job): tag = job["set"].replace("seed-", "s").replace("-", "") name = f"{tag}_{job['workload'].replace('workload-', 'w')}_{job['arm']}" + if job.get("tag"): + name += f"_{job['tag']}" return name if job["attempts"] == 1 else f"{name}_r{job['attempts']}" @@ -130,7 +138,8 @@ def command(job, name, slot, args): cmd = [ sys.executable, runner, - f"experiment_type=random_workload/{job['set']}/{job['workload']}/{config}", + "experiment_type=random_workload/" + f"{job['set']}/{job.get('config_dir', job['workload'])}/{config}", f"experiment.name={name}", "providers.cloudlab.num_nodes=1", f"providers.cloudlab.node_offset={2 * slot}", @@ -233,6 +242,13 @@ def main(): default=set(), help="slots to leave alone, e.g. 3,15,16 (their nodes are busy)", ) + parser.add_argument( + "--arm", action="append", choices=ARMS, help="only these arms (default all)" + ) + parser.add_argument("--tag", default="", help="suffix for experiment names") + parser.add_argument( + "--config-variant", default="", help="read configs from -VARIANT/" + ) parser.add_argument("--dry-run", action="store_true") args = parser.parse_args() @@ -254,7 +270,12 @@ def main(): ) j["status"] = "orphaned" if alive else "queued" else: - jobs = make_jobs([parse_set(s) for s in args.set]) + jobs = make_jobs( + [parse_set(s) for s in args.set], + args.arm or tuple(ARMS), + args.tag, + args.config_variant, + ) save(batch_dir, jobs) if args.dry_run: diff --git a/asap-tools/experiments/tests/test_random_workload_batch.py b/asap-tools/experiments/tests/test_random_workload_batch.py index c9b1f1e0..940aacaf 100644 --- a/asap-tools/experiments/tests/test_random_workload_batch.py +++ b/asap-tools/experiments/tests/test_random_workload_batch.py @@ -1,6 +1,9 @@ """Tests for random_workload_batch.py's job planning.""" +import tempfile import unittest +from pathlib import Path +from unittest import mock import random_workload_batch as batch @@ -21,6 +24,32 @@ def test_retries_get_a_new_experiment_name(self): names.append(batch.experiment_name({**job, "attempts": attempts})) self.assertEqual(names, ["s862long_w07_ch", "s862long_w07_ch_r2"]) + def test_a_tagged_variant_rerun_names_and_configs_apart(self): + # Reruns must not reuse the original runs' names, whose outputs they + # would overwrite. + with tempfile.TemporaryDirectory() as tmp: + sets = Path(tmp) / "seed-862-long" + (sets / "workload-04").mkdir(parents=True) + (sets / "workload-04" / "clickhouse.yaml").write_text( + "query_groups: [{client_options: {starting_delay: 600}}]" + ) + (sets / "workload-04-sla10").mkdir() + (sets / "workload-04-sla10" / "e2e.yaml").write_text("") + with mock.patch.object(batch, "CONFIG_DIR", Path(tmp)): + [job] = batch.make_jobs( + [("seed-862-long", ["workload-04"])], ("asap",), "sla10", "sla10" + ) + job["attempts"] = 1 + self.assertEqual(batch.experiment_name(job), "s862long_w04_asap_sla10") + + class Args: + username, hostname_suffix = "u", "s" + + self.assertIn( + "experiment_type=random_workload/seed-862-long/workload-04-sla10/e2e", + batch.command(job, "n", 0, Args), + ) + def test_each_job_runs_on_its_slots_two_nodes(self): job = {"set": "seed-862", "workload": "workload-01", "arm": "prom"} diff --git a/asap-tools/experiments/tests/test_sketch_bench_workloads.py b/asap-tools/experiments/tests/test_sketch_bench_workloads.py index 1330a381..9275e7e5 100644 --- a/asap-tools/experiments/tests/test_sketch_bench_workloads.py +++ b/asap-tools/experiments/tests/test_sketch_bench_workloads.py @@ -186,6 +186,11 @@ def test_metric_without_labels_raises(self): with self.assertRaises(ValueError): sbw.init_sql({"data": {"type": "gauge", "labels": []}}, [], False) + def test_raw_table_is_ordered_by_time_only(self): + metrics = {"data": {"type": "gauge", "labels": [{"name": "job"}]}} + self.assertIn("ORDER BY ts;", sbw.init_sql(metrics, [], False)) + self.assertIn("ORDER BY (job, ts);", sbw.init_sql(metrics, [], True)) + def test_describe_is_one_line(self): entry = {"promql": "sum by (job) (\n data\n)", "interval_secs": 1} self.assertEqual(sbw.describe(entry), "sum by (job) ( data )") From 0a716635c815280496a295bfa961cb7aeb796a3c Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 11:23:40 -0400 Subject: [PATCH 09/29] fix(asap-tools): a resumed batch keeps its orphaned runs' slots It cleaned and reused them, wrecking the runs still going there. Co-Authored-By: Claude Opus 5.5 --- .../experiments/random_workload_batch.py | 50 +++++++++++++++---- .../tests/test_random_workload_batch.py | 13 +++++ 2 files changed, 52 insertions(+), 11 deletions(-) diff --git a/asap-tools/experiments/random_workload_batch.py b/asap-tools/experiments/random_workload_batch.py index 06bfa370..b4a35ff6 100644 --- a/asap-tools/experiments/random_workload_batch.py +++ b/asap-tools/experiments/random_workload_batch.py @@ -207,6 +207,34 @@ def summarize(batch_dir, jobs, set_name, workload): subprocess.run(cmd, stdout=f, stderr=subprocess.STDOUT, cwd=EXPERIMENTS_DIR) +def alive(name): + """Whether a runner for experiment `name` runs on this machine.""" + pattern = f"experiment.name={name}( |$)" + return ( + subprocess.run(["pgrep", "-f", pattern], stdout=subprocess.DEVNULL).returncode + == 0 + ) + + +def settle_orphans(jobs, is_alive=alive): + """The slots of orphaned jobs (runs an earlier driver left going) that + still run, so nothing launches over them. A finished orphan becomes done, + or queued for its retry.""" + busy = set() + for j in jobs: + if j["status"] != "orphaned": + continue + if "slot" not in j: + raise SystemExit(f"{j['names'][-1]} has no recorded slot") + if is_alive(j["names"][-1]): + busy.add(j["slot"]) + elif succeeded(j, j["names"][-1]): + j["status"] = "done" + else: + j["status"] = "queued" if j["attempts"] < MAX_ATTEMPTS else "failed" + return busy + + def save(batch_dir, jobs): tmp = batch_dir / "state.json.tmp" tmp.write_text(json.dumps(jobs, indent=1)) @@ -259,16 +287,9 @@ def main(): jobs = json.loads(state.read_text()) for j in jobs: if j["status"] == "running": - # A run the earlier driver left behind keeps going on its own; - # this driver neither reruns nor watches it. - alive = ( - subprocess.run( - ["pgrep", "-f", f"experiment.name={j['names'][-1]}( |$)"], - stdout=subprocess.DEVNULL, - ).returncode - == 0 - ) - j["status"] = "orphaned" if alive else "queued" + # A run the earlier driver left behind keeps going on its own + # and keeps its slot until it ends. + j["status"] = "orphaned" if alive(j["names"][-1]) else "queued" else: jobs = make_jobs( [parse_set(s) for s in args.set], @@ -323,8 +344,14 @@ def main(): launching = args.stop_launching_after_hours is None or ( time.time() - started < args.stop_launching_after_hours * 3600 ) + orphan_slots = settle_orphans(jobs) for slot in range(args.slots): - if slot in running or slot in args.skip_slots or not launching: + if ( + slot in running + or slot in args.skip_slots + or slot in orphan_slots + or not launching + ): continue # Retries first, then the longest queued job. queued = [j for j in jobs if j["status"] == "queued"] @@ -336,6 +363,7 @@ def main(): name = experiment_name(job) job["names"].append(name) job["status"] = "running" + job["slot"] = slot save(batch_dir, jobs) logs = batch_dir / "logs" logs.mkdir(exist_ok=True) diff --git a/asap-tools/experiments/tests/test_random_workload_batch.py b/asap-tools/experiments/tests/test_random_workload_batch.py index 940aacaf..97213359 100644 --- a/asap-tools/experiments/tests/test_random_workload_batch.py +++ b/asap-tools/experiments/tests/test_random_workload_batch.py @@ -50,6 +50,19 @@ class Args: batch.command(job, "n", 0, Args), ) + def test_a_live_orphan_keeps_its_slot(self): + # A resumed driver once cleaned and reused the slots of runs it + # had left going, wrecking them. + jobs = [ + {"status": "orphaned", "names": ["a"], "slot": 9, "attempts": 1}, + {"status": "orphaned", "names": ["b"], "slot": 10, "attempts": 1}, + {"status": "queued", "names": [], "attempts": 0}, + ] + with mock.patch.object(batch, "succeeded", return_value=False): + busy = batch.settle_orphans(jobs, is_alive=lambda name: name == "a") + self.assertEqual(busy, {9}) + self.assertEqual([j["status"] for j in jobs], ["orphaned", "queued", "queued"]) + def test_each_job_runs_on_its_slots_two_nodes(self): job = {"set": "seed-862", "workload": "workload-01", "arm": "prom"} From 828236784aee915959eb8ac34f11d09a9a8d0eef Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 12:05:12 -0400 Subject: [PATCH 10/29] fix(asap-tools): fit a one-panel cost-latency legend Co-Authored-By: Claude Opus 5.5 --- .../post_experiment/multi_experiment/plot_cost_latency.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index 331f21b0..20e9a4e5 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -152,9 +152,10 @@ def main(csv_path, out_dir): frameon=False, fontsize=13, loc="lower center", - ncol=len(SYSTEMS), + ncol=min(len(SYSTEMS), 3 * len(sets)), ) - fig.tight_layout(rect=(0, 0.07, 1, 1)) + # A one-panel figure fits three legend entries a row, so two rows. + fig.tight_layout(rect=(0, 0.07 if len(sets) > 1 else 0.12, 1, 1)) stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") out_path = Path(out_dir) / f"cost_latency_{'+'.join(sets)}_{stamp}.png" with open(out_path, "xb") as f: From 1fea0bcc0c7f2e17d8541f4cefe372efbb984a4f Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 12:38:23 -0400 Subject: [PATCH 11/29] feat(asap-tools): baseline ovals, double arrows, denser log ticks in cost-latency plot Also a figure title, thicker axes, and "total compute cost" on the y axis. Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 42 ++++++++++++++++--- 1 file changed, 36 insertions(+), 6 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index 20e9a4e5..f9e68191 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -10,6 +10,8 @@ metric: collate_batch.py's geometric mean of baseline / LiteOlly over workloads where both are valid. Arrow lengths are those ratios. +An oval encloses each baseline's valid runs (all its runs if none is valid). + Writes /cost_latency__.png; never overwrites. Usage: python3 plot_cost_latency.py collated.csv out_dir @@ -23,9 +25,11 @@ from collections import defaultdict import matplotlib +import numpy as np matplotlib.use("Agg") import matplotlib.pyplot as plt # noqa: E402 +from matplotlib import ticker # noqa: E402 sys.path.insert(0, str(Path(__file__).resolve().parent)) from collate_batch import ratios # noqa: E402 @@ -42,6 +46,22 @@ SURFACE, INK, MUTED = "#fcfcfb", "#0b0b0b", "#52514e" +def oval(ax, xs, ys, color): + """Ellipse in log space along the points' covariance, scaled to enclose + them all with a fixed margin (in decades) for the markers.""" + pts = np.log10(np.column_stack([xs, ys])) + center = pts.mean(axis=0) + # The small diagonal keeps one or collinear points drawable. + cov = np.cov(pts.T, bias=True) + np.eye(2) * 1e-4 + inv = np.linalg.inv(cov) + d = pts - center + reach = np.sqrt(np.einsum("ij,jk,ik->i", d, inv, d).max()) + vals, vecs = np.linalg.eigh(cov) + t = np.linspace(0, 2 * np.pi, 200) + ring = (vecs * (np.sqrt(vals) * reach + 0.05)) @ np.vstack([np.cos(t), np.sin(t)]) + ax.plot(*10 ** (ring + center[:, None]), color=color, linewidth=1.5, zorder=2) + + def arrows(ax, rows, improvement, set_name): """LiteOlly's improvement over the worst baseline per metric, drawn from LiteOlly's geometric-mean point (over its valid runs, else all).""" @@ -56,7 +76,7 @@ def arrows(ax, rows, improvement, set_name): ((arm, m[metric]) for (s, arm), m in improvement.items() if s == set_name), key=lambda am: am[1], ) - style = dict(arrowstyle="->", color=INK, linewidth=2, mutation_scale=22) + style = dict(arrowstyle="<->", color=INK, linewidth=2, mutation_scale=22) arm, cpu = worst["cpu_secs"] x = lx / 1.5 ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) @@ -131,22 +151,32 @@ def main(csv_path, out_dir): linewidths=2.5, zorder=3, ) + # Invalid runs don't stand for the system, so they don't stretch its oval. + fit = [(x, y) for x, y, v in zip(xs, ys, valid) if v] or list(zip(xs, ys)) + if key != "asap" and fit: + oval(ax, *zip(*fit), color) ax.set_xscale("log") ax.set_yscale("log") ax.set_xlabel("p95 query latency (s)", color=MUTED, fontsize=14) - ax.set_ylabel( - "total CPU (CPU-seconds, warm-up + queries)", color=MUTED, fontsize=14 - ) + ax.set_ylabel("total compute cost", color=MUTED, fontsize=14) + for axis in (ax.xaxis, ax.yaxis): + axis.set_major_locator(ticker.LogLocator(subs=(1, 2, 5))) + axis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v:g}")) + axis.set_minor_formatter(ticker.NullFormatter()) title = ( f"{set_name}: {len(workloads)} workload(s), lower left is better" "\nhollow: invalid run (failed queries or wrong answers)" ) ax.set_title(title, color=INK, fontsize=14, loc="left") ax.grid(True, which="major", color="#e6e5e1", linewidth=0.8) - ax.tick_params(colors=MUTED, labelsize=13) + ax.tick_params(colors=MUTED, labelsize=13, width=2) for spine in ax.spines.values(): - spine.set_color("#d4d3cf") + spine.set_color(INK) + spine.set_linewidth(2) arrows(ax, by_set[set_name], improvement, set_name) + fig.suptitle( + "Cost-latency tradeoffs of LiteOlly vs. baselines", color=INK, fontsize=16 + ) fig.legend( *axes[0][0].get_legend_handles_labels(), frameon=False, From d54efa34b3852bc0fa50009e237b0ef3ab72b9e2 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 12:49:37 -0400 Subject: [PATCH 12/29] feat(asap-tools): multiple-dashboard random-workload configs random_workload_artifacts.py --dashboards K writes -dashK/ run configs with every query group repeated K times (e2e, VictoriaMetrics, ClickHouse and ClickHouse+MV), on the same plan, feeder and data. Each copy is its own client group and thread, so copies are issued concurrently and their latencies recorded apart. The query client's connection pool now fits every query in flight; the default 10 would open a new connection per excess request at K >= 2. collate_batch.py collates batches of an arm subset, against ClickHouse+MV when Prometheus didn't run. Co-Authored-By: Claude Opus 5.5 --- .../fake_metric/random_workload_artifacts.py | 46 +++++++++++++++++-- .../multi_experiment/collate_batch.py | 24 ++++++---- .../tests/test_random_workload_artifacts.py | 36 +++++++++++++++ .../main_prometheus_client.py | 7 ++- 4 files changed, 96 insertions(+), 17 deletions(-) diff --git a/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py b/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py index 289236a8..5a1fb248 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py @@ -133,7 +133,24 @@ def workload_set(workload): return f"seed-{workload['seed']}" + (f"-{variant}" if variant else "") -def pilot_configs(workload, pilot_dir): +def as_dashboards(groups, dashboards, key): + """`groups` repeated once per dashboard, each copy its own client thread + and output group; `key` ("id" or "name") stays unique per copy.""" + if dashboards == 1: + return groups + copies = [] + for d in range(1, dashboards + 1): + for group in groups: + group = copy.deepcopy(group) + if key == "id": + group["id"] = len(copies) + 1 + else: + group[key] = f"{group[key]}_d{d}" + copies.append(group) + return copies + + +def pilot_configs(workload, pilot_dir, dashboards=1): """The five-system pilot's run configs, {path under EXPERIMENTS_DIR: content}: ASAPQuery (the saved plan in `pilot_dir`) and Prometheus in one experiment_run_e2e.py config, VictoriaMetrics in another, and raw @@ -144,7 +161,10 @@ def pilot_configs(workload, pilot_dir): freshness delay, then evaluates each query at multiples of its interval for PILOT_DURATION_MS: the same windows relative to its data start. Raw ClickHouse runs the manifest's clickhouse_sql, and ClickHouse+MV - the same RQEs' Query over exact views (sketch_bench_workloads).""" + the same RQEs' Query over exact views (sketch_bench_workloads). + + With `dashboards` K > 1, every query group runs K times concurrently, + as K dashboards over the same data, from `-dashK/`.""" queries = [q for q in workload["queries"] if q["support"] == PILOT_SUPPORT] lookback_secs = max(q["lookback_ms"] for q in queries) // 1000 starting_delay = lookback_secs + QUERY_FRESHNESS_DELAY_SECS @@ -152,7 +172,8 @@ def pilot_configs(workload, pilot_dir): feed_secs = starting_delay + FEED_BOUND_QUERY_PHASES * duration seed = workload["data_facts"]["feeder_seed"] table = pilot_dir / "eval_table.json" - out_dir = Path(CONFIG_DIR) / workload_set(workload) / workload["id"] + variant = f"-dash{dashboards}" if dashboards > 1 else "" + out_dir = Path(CONFIG_DIR) / workload_set(workload) / (workload["id"] + variant) e2e, _ = build_config( str(table), @@ -163,6 +184,10 @@ def pilot_configs(workload, pilot_dir): feeder_duration_secs=feed_secs, prebuilt_output_dir=str(pilot_dir), ) + e2e_params = e2e["experiment_params"] + e2e_params["query_groups"] = as_dashboards( + e2e_params["query_groups"], dashboards, "id" + ) # One monitoring tool per run, so VictoriaMetrics is its own baseline run. vm = copy.deepcopy(e2e) del vm["controller"] @@ -232,7 +257,7 @@ def pilot_configs(workload, pilot_dir): "paced_feeder": feeder, "clickhouse_image_tag": CLICKHOUSE_IMAGE_TAG, }, - "query_groups": groups, + "query_groups": as_dashboards(groups, dashboards, "name"), "data_ingestion_interval_ms": workload["data_facts"]["scrape_interval_ms"], } files[name] = "# @package experiment_params\n" + yaml.safe_dump( @@ -277,7 +302,16 @@ def main(): help="the workload's archived pilot plan (eval_table.json and planner " f"configs): also write the pilot's run configs under {CONFIG_DIR}", ) + parser.add_argument( + "--dashboards", + type=int, + default=1, + help="with --pilot-dir: issue every query group this many times " + "concurrently, into -dashK/", + ) args = parser.parse_args() + if args.dashboards < 1: + parser.error("--dashboards must be at least 1") workload = json.loads(args.manifest.read_text()) rendered = render(workload) args.out.mkdir(parents=True, exist_ok=True) @@ -305,7 +339,9 @@ def main(): ) print(table) if args.pilot_dir: - for path, content in pilot_configs(workload, args.pilot_dir).items(): + for path, content in pilot_configs( + workload, args.pilot_dir, args.dashboards + ).items(): path = Path(EXPERIMENTS_DIR) / path path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py index 144b1478..abb675e2 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py @@ -4,8 +4,9 @@ sum of every answered query's latency, and whether the run is valid: - no query failed or timed out; -- its answers agree with the reference arm (Prometheus, which keeps up): - exact arms to EXACT_REL_ERR, ASAPQuery's sketches to their bounds. +- its answers agree with the reference arm (Prometheus, which keeps up, or + else ClickHouse+MV): exact arms to EXACT_REL_ERR, ASAPQuery's sketches to + their bounds. Separately, whether ingest kept up: during the query phase the feeder never ran more than MAX_INGEST_LAG_SECS behind (from the feeder's @@ -15,7 +16,7 @@ Then each baseline's ratio to ASAPQuery (baseline / asap, > 1 means ASAPQuery is better) over workloads where both runs are valid. Only workloads whose -arms all finished are collated. +batch arms all finished, one of them a reference, are collated. Usage: python3 collate_batch.py / [--csv out.csv] @@ -37,6 +38,8 @@ from random_workload_batch import ARMS, OUTPUTS_DIR # noqa: E402 REFERENCE = "prom" +# The reference of a batch without Prometheus: exact, and answers every window. +FALLBACK_REFERENCE = "chmv" # The queries' freshness delay: a feed further behind leaves on-time # queries' windows incomplete. MAX_INGEST_LAG_SECS = 10 @@ -120,9 +123,10 @@ def rows(batch_dir): by_workload[(j["set"], j["workload"])][j["arm"]] = j out = [] for (set_name, workload), jobs_by_arm in sorted(by_workload.items()): - if set(jobs_by_arm) != set(ARMS) or any( - j["status"] != "done" for j in jobs_by_arm.values() - ): + if any(j["status"] != "done" for j in jobs_by_arm.values()): + continue + reference = REFERENCE if REFERENCE in jobs_by_arm else FALLBACK_REFERENCE + if reference not in jobs_by_arm: continue dirs = { a: OUTPUTS_DIR / j["names"][-1] / ARMS[a][3] for a, j in jobs_by_arm.items() @@ -137,8 +141,8 @@ def rows(batch_dir): why = [] if len(recorded) != len(latencies): why.append(f"{len(recorded) - len(latencies)} failed queries") - if arm_name != REFERENCE: - why += disagreements(arm_name, arm, arms[REFERENCE]) + if arm_name != reference: + why += disagreements(arm_name, arm, arms[reference]) out.append( { "set": set_name, @@ -170,8 +174,8 @@ def ratios(table): by = {(r["set"], r["workload"], r["arm"]): r for r in table} result = defaultdict(lambda: defaultdict(list)) for (set_name, workload, arm), r in by.items(): - asap = by[(set_name, workload, "asap")] - if arm == "asap" or not (r["valid"] and asap["valid"]): + asap = by.get((set_name, workload, "asap")) + if arm == "asap" or not (asap and r["valid"] and asap["valid"]): continue for key in ("cpu_secs", "latency_p50", "latency_p95", "latency_sum"): result[(set_name, arm)][key].append(r[key] / asap[key]) diff --git a/asap-tools/experiments/tests/test_random_workload_artifacts.py b/asap-tools/experiments/tests/test_random_workload_artifacts.py index 47ba9ddd..cd158a2f 100644 --- a/asap-tools/experiments/tests/test_random_workload_artifacts.py +++ b/asap-tools/experiments/tests/test_random_workload_artifacts.py @@ -194,6 +194,42 @@ def test_every_arm_feeds_the_same_data_and_schedule(self): raw_sql = "".join(files[f"interval_{t}s_baseline.sql"] for t in (1, 20, 30)) self.assertTrue(all(sql in raw_sql for sql in pilot_sql)) + def test_two_dashboards_repeat_every_group_on_the_same_plan(self): + workload = workloads.generate_workloads(seed=862)[0] + + def configs(dashboards): + files = artifacts.pilot_configs(workload, PILOT_DIR, dashboards) + # The SQL files are the same, under the variant's directory. + return { + p.name: yaml.safe_load(c.replace("workload-01-dash2/", "workload-01/")) + for p, c in files.items() + if p.suffix == ".yaml" + }, {p.parent.name for p in files} + + one, _ = configs(1) + two, dirs = configs(2) + self.assertEqual(dirs, {"workload-01-dash2"}) + for name, key in ( + ("e2e.yaml", "id"), + ("clickhouse.yaml", "name"), + ("clickhouse_mv.yaml", "name"), + ): + with self.subTest(config=name): + single, double = one[name], two[name] + params = ( + (single["experiment_params"], double["experiment_params"]) + if name == "e2e.yaml" + else (single, double) + ) + groups, copies = (p.pop("query_groups") for p in params) + # Same plan, feeder and data; only the groups repeat. + self.assertEqual(double, single) + # Each copy is its own group, so the client keeps its latencies apart. + self.assertEqual(len({g[key] for g in copies}), 2 * len(groups)) + for g in groups + copies: + del g[key] + self.assertEqual(copies, 2 * groups) + class PilotConfigsPreflightTest(unittest.TestCase): """Each config through Hydra and the checks its runner makes before it diff --git a/asap-tools/queriers/prometheus-client/main_prometheus_client.py b/asap-tools/queriers/prometheus-client/main_prometheus_client.py index 1382c7c3..44512f40 100644 --- a/asap-tools/queriers/prometheus-client/main_prometheus_client.py +++ b/asap-tools/queriers/prometheus-client/main_prometheus_client.py @@ -652,6 +652,9 @@ def main(args: Any) -> None: global_query_idx += len(query_group.queries) server_url_for_alignment = None + # Every query of every group can be in flight at once; a smaller pool + # opens a new connection per excess request, inside its latency. + pool_size = max(requests.adapters.DEFAULT_POOLSIZE, global_query_idx) servers: Dict[str, QueryClient] = {} for server in config.servers: @@ -676,7 +679,7 @@ def main(args: Any) -> None: ) # Mount debug adapter for HTTP request logging - debug_adapter = DebugHTTPAdapter(server.name) + debug_adapter = DebugHTTPAdapter(server.name, pool_maxsize=pool_size) client.session.mount("http://", debug_adapter) client.session.mount("https://", debug_adapter) else: @@ -691,7 +694,7 @@ def main(args: Any) -> None: ) # Mount debug adapter for HTTP request logging - debug_adapter = DebugHTTPAdapter(server.name) + debug_adapter = DebugHTTPAdapter(server.name, pool_maxsize=pool_size) client.session.mount("http://", debug_adapter) client.session.mount("https://", debug_adapter) From 19a8fa2be7440d5fe3ad8534c44247c4f6a09ec8 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 12:48:32 -0400 Subject: [PATCH 13/29] feat(asap-tools): replay recorded gauge values in the paced feeder A workload table's gauge can carry recorded values (values.replay), which the feeder loops one step per sample in place of generated ones. make_replay.py fits Alibaba v2022 pod CPU into the random workloads' label shape and writes such a table. Co-Authored-By: Claude Opus 5.5 --- .../data-sources/paced-feeder/src/table.rs | 52 +++++++++ .../data-sources/paced-feeder/src/workload.rs | 6 + .../datasets/alibaba/make_replay.py | 110 ++++++++++++++++++ 3 files changed, 168 insertions(+) create mode 100644 asap-tools/experiments/datasets/alibaba/make_replay.py diff --git a/asap-tools/data-sources/paced-feeder/src/table.rs b/asap-tools/data-sources/paced-feeder/src/table.rs index 97cd6546..dd33aa6b 100644 --- a/asap-tools/data-sources/paced-feeder/src/table.rs +++ b/asap-tools/data-sources/paced-feeder/src/table.rs @@ -57,6 +57,15 @@ struct GaugeValues { dist: ParetoDist, } +/// A recording to replay instead of generated values: `steps` values per +/// series, series-major in [`series_labels`] order. +#[derive(Deserialize)] +#[serde(deny_unknown_fields)] +struct ReplayValues { + steps: usize, + values: Vec, +} + #[derive(Deserialize)] #[serde(deny_unknown_fields)] struct ParetoDist { @@ -161,6 +170,31 @@ fn metric(name: &str, spec: &Value, seed: u64) -> Result<(Metric, u32)> { ); } let series = series_labels(&spec.labels)?; + if let Some(replay) = spec.values.get("replay") { + ensure!(spec.kind == "gauge", "`replay` values apply to gauges only"); + let replay: ReplayValues = + serde_json::from_value(replay.clone()).context("gauge `values.replay`")?; + ensure!(replay.steps > 0, "`replay.steps` must be positive"); + ensure!( + replay.values.len() == series.len() * replay.steps, + "`replay.values` has {} values, not {} series x {} steps", + replay.values.len(), + series.len(), + replay.steps + ); + let metric = Metric { + name: name.to_string(), + labels: spec.labels, + scales: vec![1.0; series.len()], + series, + kind: Kind::Replay { + steps: replay.steps, + values: Arc::new(replay.values), + }, + seed: splitmix64(seed ^ fnv1a(name)), + }; + return Ok((metric, spec.samples_per_sec_per_series)); + } let (skew, kind) = if spec.kind == "gauge" { let values: GaugeValues = serde_json::from_value(spec.values).context("gauge `values`")?; ensure!( @@ -415,6 +449,24 @@ mod tests { assert_eq!(series[11], ["1", "2", "2.1"]); } + #[test] + fn replay_values_loop_per_series() { + let spec = json!({ + "type": "gauge", + "samples_per_sec_per_series": 10, + "labels": [{"name": "job", "cardinality": 2}], + "values": {"replay": {"steps": 3, "values": [1.0, 2.0, 3.0, 10.0, 20.0, 30.0]}}, + }); + let (m, rate) = metric("data", &spec, 7).unwrap(); + assert_eq!(rate, 10); + let seen: Vec = (0..4).map(|n| m.value(1, n)).collect(); + assert_eq!(seen, [10.0, 20.0, 30.0, 10.0]); + // One value per series and step, no more and no fewer. + let mut short = spec.clone(); + short["values"]["replay"]["values"] = json!([1.0, 2.0]); + assert!(metric("data", &short, 7).is_err()); + } + #[test] fn rejects_a_child_before_its_parent() { let labels = vec![label("label_0", 3, Some("job")), label("job", 2, None)]; diff --git a/asap-tools/data-sources/paced-feeder/src/workload.rs b/asap-tools/data-sources/paced-feeder/src/workload.rs index 81acc6d6..a25c8a4b 100644 --- a/asap-tools/data-sources/paced-feeder/src/workload.rs +++ b/asap-tools/data-sources/paced-feeder/src/workload.rs @@ -59,6 +59,9 @@ pub enum Kind { /// `(n + 1) · scale` at sample index `n`: starts at the first sample, grows /// by the series' scale per sample and never resets. Counter, + /// Recorded values, `steps` per series, series-major: sample index `n` + /// takes step `n mod steps`, so the recording loops. + Replay { steps: usize, values: Arc> }, } #[derive(Debug, Clone)] @@ -213,6 +216,9 @@ impl Metric { scale * pareto_scale * u.powf(-1.0 / pareto_shape) } Kind::Counter => (sample_index + 1) as f64 * scale, + Kind::Replay { steps, ref values } => { + values[series * steps + (sample_index % steps as u64) as usize] + } } } } diff --git a/asap-tools/experiments/datasets/alibaba/make_replay.py b/asap-tools/experiments/datasets/alibaba/make_replay.py new file mode 100644 index 00000000..1f5b469a --- /dev/null +++ b/asap-tools/experiments/datasets/alibaba/make_replay.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 +"""A replay table for paced-feeder from Alibaba v2022 MSMetrics: pod CPU +utilization in the random workloads' label shape (4 region x 5 service x 10 +job x 50 label_0), so their plans apply unchanged. + +Each job is one microservice and each label_0 one of its pods; region and +service only group microservices. Label values follow the feeder's table +convention: a child's value is its parent's, a `.`, and its index (`job="3.1.7"`). +Microservices are drawn by `--seed` among those with at least 50 pods that +report at every step of the input; pods are their first 50 by name. + +Writes `.csv` (`offset_ms,region,service,job,label_0,value`, sorted by +offset, offsets from the first step) and `_mapping.csv` (labels to +msname/msinstanceid). With `--table`, also `_table.json`: that +workload table with its `data` metric's values replaced by the replay, which +paced-feeder loops (one trace step per sample). + +Usage: python3 make_replay.py MSMetricsUpdate_0.csv MSMetricsUpdate_1.csv \ + --out replay [--table eval_table.json] +""" + +import argparse +import csv +import json +import random +from collections import defaultdict + +REGIONS, SERVICES, JOBS, PODS = 4, 5, 10, 50 + + +def rows(paths): + for path in paths: + with open(path, newline="") as f: + for r in csv.DictReader(f): + yield r + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("csv", nargs="+") + parser.add_argument("--out", required=True) + parser.add_argument("--seed", type=int, default=862) + parser.add_argument("--table", help="workload table whose `data` gets the replay") + args = parser.parse_args() + + steps, seen = set(), defaultdict(set) # pod -> steps + pods = defaultdict(set) # msname -> pods + for r in rows(args.csv): + steps.add(r["timestamp"]) + seen[r["msinstanceid"]].add(r["timestamp"]) + pods[r["msname"]].add(r["msinstanceid"]) + full = {p for p, s in seen.items() if len(s) == len(steps)} + eligible = sorted(m for m, ps in pods.items() if len(ps & full) >= PODS) + jobs = REGIONS * SERVICES * JOBS + print(f"{len(steps)} steps; {len(eligible)} microservices eligible") + chosen = random.Random(args.seed).sample(eligible, jobs) + + labels = {} # pod -> (region, service, job, label_0) + with open(f"{args.out}_mapping.csv", "w", newline="") as f: + w = csv.writer(f) + w.writerow(["region", "service", "job", "label_0", "msname", "msinstanceid"]) + for i, msname in enumerate(chosen): + region = str(i // (SERVICES * JOBS)) + service = f"{region}.{i // JOBS % SERVICES}" + job = f"{service}.{i % JOBS}" + ordered = sorted( + pods[msname] & full, key=lambda p: int(p.rsplit("_", 1)[1]) + ) + for k, pod in enumerate(ordered[:PODS]): + labels[pod] = (region, service, job, f"{job}.{k}") + w.writerow([*labels[pod], msname, pod]) + + start = min(int(s) for s in steps) + out = sorted( + (int(r["timestamp"]) - start, *labels[r["msinstanceid"]], r["cpu_utilization"]) + for r in rows(args.csv) + if r["msinstanceid"] in labels + ) + with open(f"{args.out}.csv", "w", newline="") as f: + w = csv.writer(f) + w.writerow(["offset_ms", "region", "service", "job", "label_0", "value"]) + w.writerows(out) + print(f"{len(out)} rows, {len(labels)} series") + if args.table: + write_table(args.table, f"{args.out}_table.json", out, len(steps)) + + +def write_table(table_path, out_path, rows, steps): + """`table_path` with `data`'s values replaced by `rows`, series-major in + paced-feeder's series order (mixed radix, label_0 fastest).""" + by_series = {} + for offset, *_, label_0, value in rows: # sorted by offset + by_series.setdefault(label_0, []).append(round(float(value), 6)) + values = [] + for r in range(REGIONS): + for s in range(SERVICES): + for j in range(JOBS): + for k in range(PODS): + values += by_series[f"{r}.{s}.{j}.{k}"] + assert len(values) == len(by_series) * steps + table = json.load(open(table_path)) + data = table["workloads"][0]["e2e"]["metrics"]["data"] + data["values"] = {"replay": {"steps": steps, "values": values}} + with open(out_path, "w") as f: + json.dump(table, f) + print(out_path) + + +if __name__ == "__main__": + main() From aa454fc73538a826086ef6590b6e553ce8fdcf2b Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:03:19 -0400 Subject: [PATCH 14/29] feat(asap-tools): cost on x, p95 latency on y in cost-latency plot Panel subtitles drop the workload set name. Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 36 +++++++++---------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index f9e68191..3cb749df 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -1,12 +1,12 @@ #!/usr/bin/env python3 """Cost-latency scatter from collate_batch.py's CSV: one panel per workload -set, one point per (workload, system); x = p95 query latency, y = total -CPU-seconds, both log. Lower left is better. A run collate_batch.py marks +set, one point per (workload, system); x = total CPU-seconds, y = p95 query +latency, both log. Lower left is better. A run collate_batch.py marks invalid (failed queries, or answers that disagree with the reference) is drawn hollow: its latency and CPU don't stand for a correct answer. Two arrows from LiteOlly's geometric-mean point show its improvement in CPU -(vertical) and p95 latency (horizontal) over the worst baseline for that +(horizontal) and p95 latency (vertical) over the worst baseline for that metric: collate_batch.py's geometric mean of baseline / LiteOlly over workloads where both are valid. Arrow lengths are those ratios. @@ -68,8 +68,8 @@ def arrows(ax, rows, improvement, set_name): names = {key: label for key, label, *_ in SYSTEMS} lite = [r for r in rows if r["arm"] == "asap"] lite = [r for r in lite if r["valid"] == "True"] or lite - lx = statistics.geometric_mean(float(r["latency_p95"]) for r in lite) - ly = statistics.geometric_mean(float(r["cpu_secs"]) for r in lite) + lx = statistics.geometric_mean(float(r["cpu_secs"]) for r in lite) + ly = statistics.geometric_mean(float(r["latency_p95"]) for r in lite) worst = {} for metric in ("cpu_secs", "latency_p95"): worst[metric] = max( @@ -77,24 +77,24 @@ def arrows(ax, rows, improvement, set_name): key=lambda am: am[1], ) style = dict(arrowstyle="<->", color=INK, linewidth=2, mutation_scale=22) - arm, cpu = worst["cpu_secs"] + arm, lat = worst["latency_p95"] x = lx / 1.5 - ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) + ax.annotate("", (x, ly), xytext=(x, ly * lat), arrowprops=style, zorder=4) ax.text( x * 1.08, - ly * cpu**0.75, - f"{cpu:.1f}x less CPU\nvs {names[arm]}", + ly * lat**0.75, + f"{lat:.1f}x lower p95\nlatency vs\n{names[arm]}", color=INK, fontsize=13, va="center", ) - arm, lat = worst["latency_p95"] + arm, cpu = worst["cpu_secs"] y = ly / 1.4 - ax.annotate("", (lx, y), xytext=(lx * lat, y), arrowprops=style, zorder=4) + ax.annotate("", (lx, y), xytext=(lx * cpu, y), arrowprops=style, zorder=4) ax.text( - lx * lat**0.5, + lx * cpu**0.5, y / 1.08, - f"{lat:.1f}x lower p95 latency vs {names[arm]}", + f"{cpu:.1f}x less CPU vs {names[arm]}", color=INK, fontsize=13, ha="center", @@ -127,8 +127,8 @@ def main(csv_path, out_dir): workloads = sorted({r["workload"] for r in by_set[set_name]}) for key, label, color, marker in SYSTEMS: pts = [r for r in by_set[set_name] if r["arm"] == key] - xs = [float(r["latency_p95"]) for r in pts] - ys = [float(r["cpu_secs"]) for r in pts] + xs = [float(r["cpu_secs"]) for r in pts] + ys = [float(r["latency_p95"]) for r in pts] valid = [r["valid"] == "True" for r in pts] ax.scatter( [x for x, v in zip(xs, valid) if v], @@ -157,14 +157,14 @@ def main(csv_path, out_dir): oval(ax, *zip(*fit), color) ax.set_xscale("log") ax.set_yscale("log") - ax.set_xlabel("p95 query latency (s)", color=MUTED, fontsize=14) - ax.set_ylabel("total compute cost", color=MUTED, fontsize=14) + ax.set_xlabel("Total Compute Cost", color=MUTED, fontsize=14) + ax.set_ylabel("p95 Query Latency", color=MUTED, fontsize=14) for axis in (ax.xaxis, ax.yaxis): axis.set_major_locator(ticker.LogLocator(subs=(1, 2, 5))) axis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v:g}")) axis.set_minor_formatter(ticker.NullFormatter()) title = ( - f"{set_name}: {len(workloads)} workload(s), lower left is better" + f"{len(workloads)} workload(s), lower left is better" "\nhollow: invalid run (failed queries or wrong answers)" ) ax.set_title(title, color=INK, fontsize=14, loc="left") From 8a9e9b01f2cf076b82939e89ee79da082431e286 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:05:55 -0400 Subject: [PATCH 15/29] feat(asap-tools): drop per-panel subtitle from cost-latency plot Co-Authored-By: Claude Opus 5.5 --- .../post_experiment/multi_experiment/plot_cost_latency.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index 3cb749df..fb96f562 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -124,7 +124,6 @@ def main(csv_path, out_dir): ) for ax, set_name in zip(axes[0], sets): ax.set_facecolor(SURFACE) - workloads = sorted({r["workload"] for r in by_set[set_name]}) for key, label, color, marker in SYSTEMS: pts = [r for r in by_set[set_name] if r["arm"] == key] xs = [float(r["cpu_secs"]) for r in pts] @@ -163,11 +162,6 @@ def main(csv_path, out_dir): axis.set_major_locator(ticker.LogLocator(subs=(1, 2, 5))) axis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v:g}")) axis.set_minor_formatter(ticker.NullFormatter()) - title = ( - f"{len(workloads)} workload(s), lower left is better" - "\nhollow: invalid run (failed queries or wrong answers)" - ) - ax.set_title(title, color=INK, fontsize=14, loc="left") ax.grid(True, which="major", color="#e6e5e1", linewidth=0.8) ax.tick_params(colors=MUTED, labelsize=13, width=2) for spine in ax.spines.values(): From 458b8c525097cbc3b7568596d2cd1c79712237b3 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:07:51 -0400 Subject: [PATCH 16/29] feat(asap-tools): cost back on y, units on cost-latency axis labels Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index fb96f562..edf8ae7a 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -1,12 +1,12 @@ #!/usr/bin/env python3 """Cost-latency scatter from collate_batch.py's CSV: one panel per workload -set, one point per (workload, system); x = total CPU-seconds, y = p95 query -latency, both log. Lower left is better. A run collate_batch.py marks +set, one point per (workload, system); x = p95 query latency, y = total +CPU-seconds, both log. Lower left is better. A run collate_batch.py marks invalid (failed queries, or answers that disagree with the reference) is drawn hollow: its latency and CPU don't stand for a correct answer. Two arrows from LiteOlly's geometric-mean point show its improvement in CPU -(horizontal) and p95 latency (vertical) over the worst baseline for that +(vertical) and p95 latency (horizontal) over the worst baseline for that metric: collate_batch.py's geometric mean of baseline / LiteOlly over workloads where both are valid. Arrow lengths are those ratios. @@ -68,8 +68,8 @@ def arrows(ax, rows, improvement, set_name): names = {key: label for key, label, *_ in SYSTEMS} lite = [r for r in rows if r["arm"] == "asap"] lite = [r for r in lite if r["valid"] == "True"] or lite - lx = statistics.geometric_mean(float(r["cpu_secs"]) for r in lite) - ly = statistics.geometric_mean(float(r["latency_p95"]) for r in lite) + lx = statistics.geometric_mean(float(r["latency_p95"]) for r in lite) + ly = statistics.geometric_mean(float(r["cpu_secs"]) for r in lite) worst = {} for metric in ("cpu_secs", "latency_p95"): worst[metric] = max( @@ -77,24 +77,24 @@ def arrows(ax, rows, improvement, set_name): key=lambda am: am[1], ) style = dict(arrowstyle="<->", color=INK, linewidth=2, mutation_scale=22) - arm, lat = worst["latency_p95"] + arm, cpu = worst["cpu_secs"] x = lx / 1.5 - ax.annotate("", (x, ly), xytext=(x, ly * lat), arrowprops=style, zorder=4) + ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) ax.text( x * 1.08, - ly * lat**0.75, - f"{lat:.1f}x lower p95\nlatency vs\n{names[arm]}", + ly * cpu**0.75, + f"{cpu:.1f}x less CPU\nvs {names[arm]}", color=INK, fontsize=13, va="center", ) - arm, cpu = worst["cpu_secs"] + arm, lat = worst["latency_p95"] y = ly / 1.4 - ax.annotate("", (lx, y), xytext=(lx * cpu, y), arrowprops=style, zorder=4) + ax.annotate("", (lx, y), xytext=(lx * lat, y), arrowprops=style, zorder=4) ax.text( - lx * cpu**0.5, + lx * lat**0.5, y / 1.08, - f"{cpu:.1f}x less CPU vs {names[arm]}", + f"{lat:.1f}x lower p95 latency vs {names[arm]}", color=INK, fontsize=13, ha="center", @@ -126,8 +126,8 @@ def main(csv_path, out_dir): ax.set_facecolor(SURFACE) for key, label, color, marker in SYSTEMS: pts = [r for r in by_set[set_name] if r["arm"] == key] - xs = [float(r["cpu_secs"]) for r in pts] - ys = [float(r["latency_p95"]) for r in pts] + xs = [float(r["latency_p95"]) for r in pts] + ys = [float(r["cpu_secs"]) for r in pts] valid = [r["valid"] == "True" for r in pts] ax.scatter( [x for x, v in zip(xs, valid) if v], @@ -156,8 +156,8 @@ def main(csv_path, out_dir): oval(ax, *zip(*fit), color) ax.set_xscale("log") ax.set_yscale("log") - ax.set_xlabel("Total Compute Cost", color=MUTED, fontsize=14) - ax.set_ylabel("p95 Query Latency", color=MUTED, fontsize=14) + ax.set_xlabel("p95 Query Latency (s)", color=MUTED, fontsize=14) + ax.set_ylabel("Total Compute Cost (CPU-sec)", color=MUTED, fontsize=14) for axis in (ax.xaxis, ax.yaxis): axis.set_major_locator(ticker.LogLocator(subs=(1, 2, 5))) axis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v:g}")) From f0e58efa7defdf57e71693c68b272e2625faab80 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:14:46 -0400 Subject: [PATCH 17/29] feat(asap-tools): cost-latency arrows labelled faster/cheaper vs Prometheus and ClickHouse Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 32 +++++++++++++------ 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index edf8ae7a..c3cc63f6 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -8,7 +8,8 @@ Two arrows from LiteOlly's geometric-mean point show its improvement in CPU (vertical) and p95 latency (horizontal) over the worst baseline for that metric: collate_batch.py's geometric mean of baseline / LiteOlly over -workloads where both are valid. Arrow lengths are those ratios. +workloads where both are valid. Arrow lengths are those ratios; labels give +the ratios against Prometheus and ClickHouse. An oval encloses each baseline's valid runs (all its runs if none is valid). @@ -64,9 +65,11 @@ def oval(ax, xs, ys, color): def arrows(ax, rows, improvement, set_name): """LiteOlly's improvement over the worst baseline per metric, drawn from - LiteOlly's geometric-mean point (over its valid runs, else all).""" + LiteOlly's geometric-mean point (over its valid runs, else all) and + labelled with its ratios against Prometheus and ClickHouse.""" names = {key: label for key, label, *_ in SYSTEMS} lite = [r for r in rows if r["arm"] == "asap"] + leftmost = min(float(r["latency_p95"]) for r in lite) lite = [r for r in lite if r["valid"] == "True"] or lite lx = statistics.geometric_mean(float(r["latency_p95"]) for r in lite) ly = statistics.geometric_mean(float(r["cpu_secs"]) for r in lite) @@ -76,17 +79,28 @@ def arrows(ax, rows, improvement, set_name): ((arm, m[metric]) for (s, arm), m in improvement.items() if s == set_name), key=lambda am: am[1], ) + + def vs(metric, word): + return "\n".join( + f"{improvement[(set_name, a)][metric]:.1f}x {word} than {names[a]}" + for a in ("prom", "ch") + ) + style = dict(arrowstyle="<->", color=INK, linewidth=2, mutation_scale=22) arm, cpu = worst["cpu_secs"] - x = lx / 1.5 + # Left of every LiteOlly point, so the label beside it clears them. + x = leftmost / 1.3 ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) ax.text( - x * 1.08, - ly * cpu**0.75, - f"{cpu:.1f}x less CPU\nvs {names[arm]}", + x / 1.1, + ly * cpu**0.5, + vs("cpu_secs", "cheaper"), color=INK, fontsize=13, + rotation=90, + ha="right", va="center", + multialignment="center", ) arm, lat = worst["latency_p95"] y = ly / 1.4 @@ -94,16 +108,16 @@ def arrows(ax, rows, improvement, set_name): ax.text( lx * lat**0.5, y / 1.08, - f"{lat:.1f}x lower p95 latency vs {names[arm]}", + vs("latency_p95", "faster"), color=INK, fontsize=13, ha="center", va="top", ) left, right = ax.get_xlim() - ax.set_xlim(min(left, lx / 2), right) + ax.set_xlim(min(left, x / 2), right) bottom, top = ax.get_ylim() - ax.set_ylim(min(bottom, ly / 2), top) + ax.set_ylim(min(bottom, ly / 2.6), top) def main(csv_path, out_dir): From f474ee6229e63f601be76f1277a622bad3607eb3 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:17:30 -0400 Subject: [PATCH 18/29] feat(asap-tools): horizontal cost-latency arrow labels, largest ratio first Co-Authored-By: Claude Opus 5.5 --- .../multi_experiment/plot_cost_latency.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index c3cc63f6..c5b474cc 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -81,26 +81,26 @@ def arrows(ax, rows, improvement, set_name): ) def vs(metric, word): - return "\n".join( - f"{improvement[(set_name, a)][metric]:.1f}x {word} than {names[a]}" - for a in ("prom", "ch") + """e.g. "6.2x cheaper vs. ClickHouse,\n4.9x vs. Prometheus", largest first.""" + (a, ra), (b, rb) = sorted( + ((a, improvement[(set_name, a)][metric]) for a in ("prom", "ch")), + key=lambda ar: -ar[1], ) + return f"{ra:.1f}x {word} vs. {names[a]},\n{rb:.1f}x vs. {names[b]}" style = dict(arrowstyle="<->", color=INK, linewidth=2, mutation_scale=22) arm, cpu = worst["cpu_secs"] - # Left of every LiteOlly point, so the label beside it clears them. + # Left of every LiteOlly point; its label sits above the arrow's top. x = leftmost / 1.3 ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) ax.text( - x / 1.1, - ly * cpu**0.5, + x, + ly * cpu * 1.12, vs("cpu_secs", "cheaper"), color=INK, fontsize=13, - rotation=90, - ha="right", - va="center", - multialignment="center", + ha="center", + va="bottom", ) arm, lat = worst["latency_p95"] y = ly / 1.4 @@ -115,9 +115,9 @@ def vs(metric, word): va="top", ) left, right = ax.get_xlim() - ax.set_xlim(min(left, x / 2), right) + ax.set_xlim(min(left, x / 2.5), right) bottom, top = ax.get_ylim() - ax.set_ylim(min(bottom, ly / 2.6), top) + ax.set_ylim(min(bottom, ly / 2.6), max(top, ly * cpu * 2.2)) def main(csv_path, out_dir): From cff51d9a995ff8d93bbc75baffa846f28e41763a Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:18:33 -0400 Subject: [PATCH 19/29] fix(asap-tools): cheaper label starts at its arrow, clear of the y axis Co-Authored-By: Claude Opus 5.5 --- .../post_experiment/multi_experiment/plot_cost_latency.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py index c5b474cc..9a998526 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/plot_cost_latency.py @@ -94,12 +94,12 @@ def vs(metric, word): x = leftmost / 1.3 ax.annotate("", (x, ly), xytext=(x, ly * cpu), arrowprops=style, zorder=4) ax.text( - x, + x / 1.15, ly * cpu * 1.12, vs("cpu_secs", "cheaper"), color=INK, fontsize=13, - ha="center", + ha="left", va="bottom", ) arm, lat = worst["latency_p95"] @@ -115,7 +115,7 @@ def vs(metric, word): va="top", ) left, right = ax.get_xlim() - ax.set_xlim(min(left, x / 2.5), right) + ax.set_xlim(min(left, x / 2), right) bottom, top = ax.get_ylim() ax.set_ylim(min(bottom, ly / 2.6), max(top, ly * cpu * 2.2)) From 22b3e263aa9926c527de74f932f8001f9540e1d8 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 13:34:46 -0400 Subject: [PATCH 20/29] feat(asap-tools): x2 random-workload variant (20,000 series) Frozen for seed 862 with saved plans for all ten workloads, for a cardinality point between base and x5. Co-Authored-By: Claude Opus 5.5 --- .../datasets/fake_metric/random_workloads.py | 2 + .../workload-01-pilot/eval_table.json | 574 ++++++++++++++++++ .../workload-01-pilot/inference_config.yaml | 80 +++ .../workload-01-pilot/plans.json.gz | Bin 0 -> 2492 bytes .../workload-01-pilot/streaming_config.yaml | 89 +++ .../seed-862-x2/workload-01.json | 363 +++++++++++ .../workload-02-pilot/eval_table.json | 573 +++++++++++++++++ .../workload-02-pilot/inference_config.yaml | 79 +++ .../workload-02-pilot/plans.json.gz | Bin 0 -> 2485 bytes .../workload-02-pilot/streaming_config.yaml | 89 +++ .../seed-862-x2/workload-02.json | 361 +++++++++++ .../workload-03-pilot/eval_table.json | 520 ++++++++++++++++ .../workload-03-pilot/inference_config.yaml | 80 +++ .../workload-03-pilot/plans.json.gz | Bin 0 -> 2310 bytes .../workload-03-pilot/streaming_config.yaml | 89 +++ .../seed-862-x2/workload-03.json | 361 +++++++++++ .../workload-04-pilot/eval_table.json | 519 ++++++++++++++++ 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100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10.json diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads.py b/asap-tools/experiments/datasets/fake_metric/random_workloads.py index 24834873..5bb2a4d6 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads.py @@ -69,6 +69,8 @@ "base": (1, 50), # Lookbacks x3: 1m -> 3m, ..., 20m -> 60m; quantiles 100s -> 300s. "long": (3, 50), + # x2 series: 20,000 at 10/s, 200,000 samples/s. + "x2": (1, 100), # x5 series: 50,000 at 10/s, 500,000 samples/s. "x5": (1, 250), } diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-01-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-01-pilot/eval_table.json new file mode 100644 index 00000000..a83c16b5 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-01-pilot/eval_table.json @@ -0,0 +1,574 @@ +{ + "inputs": { + "workload-01.json": "e77d8bca3a39966f67be06c554d6866be939c444dac5f87be2c677bb74acefd7" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "a3753f2e3fdbe686db450179b8c018e95f02e17f", + "workloads": [ + { + "dataset": "random/seed=862/workload-01", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 20000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q01-sum-fine/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 20, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-01/q01-sum-fine/keys/600s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum-coarse/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-01/q02-sum-coarse/keys/600s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service", + "job" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + "random/seed=862/workload-01/q03-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/q04-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + "random/seed=862/workload-01/q04-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service", + "job" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/q05-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + "random/seed=862/workload-01/q05-quantile/values/100s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s", + "k": 4, + "lookback_ms": 60000, + "metric": "data", + "topk_by": [ + "region", + "service" + ] + } + }, + "interval_secs": 1, + "promql": "topk by (region, service) (4, sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s", + "k": 4, + "lookback_ms": 1200000, + "metric": "data", + "topk_by": [ + "region", + "service" + ] + } + }, + "interval_secs": 30, + "promql": "topk by (region, service) (4, sum_over_time(data[20m]))", + "rqes": [ + "random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s" + ] + } + ] + }, + "rqes": [ + { + "assumptions": [], + "capability": "sum", + "covered_groups": 200, + "covered_max_N": 600000.0, + "covered_min_N": 600000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q01-sum-fine/keys/600s", + "instant": false, + "interval_secs": 20, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 600.0, + "max_N": 600000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 600000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q01-sum-fine", + "range": "600s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "sum", + "covered_groups": 20, + "covered_max_N": 6000000.0, + "covered_min_N": 6000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum-coarse/keys/600s", + "instant": false, + "interval_secs": 20, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 600.0, + "max_N": 6000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q02-sum-coarse", + "range": "600s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/q03-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q03-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/q04-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-01/q05-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 600000.0, + "covered_min_N": 600000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 600000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 600000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 12000000.0, + "covered_min_N": 12000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 12000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 12000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-01-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-01-pilot/inference_config.yaml new file mode 100644 index 00000000..9a005085 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-01-pilot/inference_config.yaml @@ -0,0 +1,80 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region, service) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - 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sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 20000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 20000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + 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"queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-02", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-02/q05-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 600000.0, + "covered_min_N": 600000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-02", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-02/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 600000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 600000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 12000000.0, + "covered_min_N": 12000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-02", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-02/topk4_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 60, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 12000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 12000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02-pilot/inference_config.yaml new file mode 100644 index 00000000..3d590028 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02-pilot/inference_config.yaml @@ -0,0 +1,79 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02-pilot/plans.json.gz 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data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 25000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 5000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 5000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 80 + width: 4096 + slideIntervalMs: 60000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 60000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02.json new file mode 100644 index 00000000..4c04d6cd --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-02.json @@ -0,0 +1,361 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 20000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-02", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ], + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 5000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 5000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q05-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q06-topk-sum", + "interval_ms": 1000, + "lookback_ms": 60000, + "output_groups": 20, + "output_series_upper_bound": 80, + "promql": "topk by (region, service) (4, sum_over_time(data[1m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q06-topk-sum/rqe", + "interval_ms": 1000, + "k": 4, + "lookback_ms": 60000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 80, + "promql": "topk by (region, service) (4, sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 5) * 5 - 300) >= 1.1 * avg_step, avg_step / 2, 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ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 5000, + "lookback_ms": 300000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, service, job, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, service, job", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "x2", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/eval_table.json new file mode 100644 index 00000000..a5910fbd --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/eval_table.json @@ -0,0 +1,520 @@ +{ + "inputs": { + "workload-03.json": "ac913568d60bae42366e422f000fd20d9bdfbe37d32cb8b0a4feca21ab9d175f" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "a3753f2e3fdbe686db450179b8c018e95f02e17f", + "workloads": [ + { + "dataset": "random/seed=862/workload-03", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + 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"child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/inference_config.yaml new file mode 100644 index 00000000..3e9e32f5 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/inference_config.yaml @@ -0,0 +1,80 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[1m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[1m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[1m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query: topk by (region) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03-pilot/plans.json.gz new file mode 100644 index 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aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 240 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03.json new file mode 100644 index 00000000..1573d1f5 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-03.json @@ -0,0 +1,361 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 20000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-04-pilot/inference_config.yaml new file mode 100644 index 00000000..ec06c999 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-04-pilot/inference_config.yaml @@ -0,0 +1,77 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 40 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 30 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: 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a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-05-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-05-pilot/inference_config.yaml new file mode 100644 index 00000000..fd47a6c3 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-05-pilot/inference_config.yaml @@ -0,0 +1,79 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 20 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 59 + query: topk by (region, service) (8, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) 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"interval_ms": 10000, + "k": 8, + "lookback_ms": 300000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 32, + "promql": "topk by (region) (8, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, service, job, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, service, job", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "x2", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-06-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-06-pilot/eval_table.json new 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"cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-06-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-06-pilot/inference_config.yaml new file mode 100644 index 00000000..d31f2ec0 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-06-pilot/inference_config.yaml @@ -0,0 +1,79 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.50, data[100s])) + planned_subquery: quantile_over_time(0.5, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + 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region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 5000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 5000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 1024 + slideIntervalMs: 2000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 60000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 60000 + windowType: tumbling +metrics: + data: + - job + - 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1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-07", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-07/q05-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 4, + "covered_max_N": 3000000.0, + "covered_min_N": 3000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-07", + "families": [ + { + "family": "topk", + "grid_K": 5000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-07/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 4, + "keys_per_window": 5000 + }, + "lookback_secs": 60.0, + "max_N": 3000000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 12000000.0, + "covered_min_N": 12000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-07", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-07/topk8_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 60, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 12000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 12000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-07-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-07-pilot/inference_config.yaml new file mode 100644 index 00000000..7832e815 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-07-pilot/inference_config.yaml @@ -0,0 +1,80 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query: topk by (region) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.50, data[100s])) + planned_subquery: quantile_over_time(0.5, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-07-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-07-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..5f5c73c2b1790db94ca6c459fdb7b1f79f385315 GIT binary patch literal 2501 zcmZY7dpHwpAHZ=WS@k9)q7#-mJPtXQ2M@ihjnEjPv8cU|Im}_=3A1uck=ZQCVVL6z 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+ "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + 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"common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 2) * 2 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 2) * 2, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q06-topk-sum", + "interval_ms": 2000, + "lookback_ms": 60000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, sum_over_time(data[1m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region" + ], + "id": "q06-topk-sum/rqe", + "interval_ms": 2000, + "k": 4, + "lookback_ms": 60000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ 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"topk by (region) (4, increase(data_total[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 20000, + "k": 4, + "lookback_ms": 600000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q10-cardinality", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -600, NOW()) AND NOW() GROUP BY region", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "x2", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/eval_table.json new file mode 100644 index 00000000..e59dcf2b --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/eval_table.json @@ -0,0 +1,518 @@ +{ + "inputs": { + "workload-08.json": "ee2864b9ecf924c78c7c5d2a0aa3afcca8d38fb9d84f9364da129da66483ee8d" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "a3753f2e3fdbe686db450179b8c018e95f02e17f", + "workloads": [ + { + "dataset": "random/seed=862/workload-08", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 20000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-08/q01-sum-fine/keys/300s", + "lookback_ms": 300000, + "metric": "data" + } + }, + "interval_secs": 10, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + "random/seed=862/workload-08/q01-sum-fine/keys/300s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-08/q02-sum-coarse/keys/300s", + "lookback_ms": 300000, + "metric": "data" + } + }, + "interval_secs": 10, + "promql": "sum by (region, service) (sum_over_time(data[5m]))", + "rqes": [ + "random/seed=862/workload-08/q02-sum-coarse/keys/300s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-08/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + "random/seed=862/workload-08/q03-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-08/q04-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + "random/seed=862/workload-08/q04-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-08/q04-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + "random/seed=862/workload-08/q04-quantile/values/100s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-08/topk4_q06-topk-sum/keys/60s", + "k": 4, + "lookback_ms": 60000, + "metric": "data", + "topk_by": [ + "region" + ] + } + }, + "interval_secs": 1, + "promql": "topk by (region) (4, sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-08/topk4_q06-topk-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + 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"min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q03-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-08", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-08/q04-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 4, + "covered_max_N": 3000000.0, + "covered_min_N": 3000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-08", + "families": [ + { + "family": "topk", + "grid_K": 5000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-08/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 4, + "keys_per_window": 5000 + }, + "lookback_secs": 60.0, + "max_N": 3000000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 4, + "covered_max_N": 60000000.0, + "covered_min_N": 60000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-08", + "families": [ + { + "family": "topk", + "grid_K": 5000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-08/topk4_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 4, + "keys_per_window": 5000 + }, + "lookback_secs": 1200.0, + "max_N": 60000000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 60000000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/inference_config.yaml new file mode 100644 index 00000000..76fac1df --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/inference_config.yaml @@ -0,0 +1,78 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query: topk by (region) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/plans.json.gz new file mode 100644 index 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08-pilot/streaming_config.yaml @@ -0,0 +1,89 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - label_0 + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 20000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 10000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 10000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 240 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08.json new file mode 100644 index 00000000..313fac22 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-08.json @@ -0,0 +1,351 @@ +{ 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"arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 60.0, + "max_N": 600000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 600000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q02-sum-coarse", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-09", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-09/q03-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q03-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-09", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-09/q04-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 5e-05, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 5e-05, + "queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 600000.0, + "covered_min_N": 600000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-09", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-09/topk8_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 600000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 600000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 12000000.0, + "covered_min_N": 12000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-09", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-09/topk8_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 200000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 12000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 12000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-09-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-09-pilot/inference_config.yaml new file mode 100644 index 00000000..a21787b7 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-09-pilot/inference_config.yaml @@ -0,0 +1,81 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[1m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[1m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[1m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query: topk by (region, service) (8, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: 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"output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 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"interval_ms": 1000, + "k": 8, + "lookback_ms": 60000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q07-topk-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 30000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 2) * 2 - 60) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 2) * 2 - 60)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 2) * 2 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 2) * 2 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 2) * 2 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 2) * 2, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q08-topk-count", + "interval_ms": 2000, + "lookback_ms": 60000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, increase(data_total[1m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 2000, + "k": 4, + "lookback_ms": 60000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 30) * 30 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 30) * 30 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 30) * 30 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 30) * 30 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q09-rate", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 80, + "promql": "topk by (region, service) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-rate/rqe", + "interval_ms": 30000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 2) * 2 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 2) * 2, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q10-cardinality", + "interval_ms": 2000, + "lookback_ms": 60000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region", + "service" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 2000, + "lookback_ms": 60000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, service, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -60, NOW()) AND NOW() GROUP BY region, service", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "x2", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/eval_table.json new file mode 100644 index 00000000..2373612d --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/eval_table.json @@ -0,0 +1,569 @@ +{ + "inputs": { + "workload-10.json": "9c023862533163e359fcbd2af0b847ce17adbe214640103a16b8bc97665cbd9c" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "a3753f2e3fdbe686db450179b8c018e95f02e17f", + "workloads": [ + { + "dataset": "random/seed=862/workload-10", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 20000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-10/q01-sum-fine/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 20, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-10/q01-sum-fine/keys/600s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-10/q02-sum-coarse/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-10/q02-sum-coarse/keys/600s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-10/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + "random/seed=862/workload-10/q03-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": 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"max_N": 12000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 12000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/inference_config.yaml new file mode 100644 index 00000000..f9dc1204 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2/workload-10-pilot/inference_config.yaml @@ -0,0 +1,77 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query: topk by (region) (8, sum_over_time(data[1m])) + planned_subquery: topk by (region) (8, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - 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z1WPeyc+$kiVi-cqi02ZU}?{1?_p^ik7exf-ea zf6-6gPnbG%-1M+VPlvcAy`_#)57E`6fM}`|ke~X2O5@r2?H#BH&ZzC28(+e4U=v(% zKWz)G@G7Ij1bvuhAo@|08`>U=Qmz$CQZo1hLY9cY1(uj6e($vB|D6`KKY9Vg^>Iy_ z&+{ms7J2oU)SmvBR2BLGQ#n^WD= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.90)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q05-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 2) * 2 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 2) * 2, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region", + "grouping": [ + "region" + ], + "id": "q06-topk-sum", + "interval_ms": 2000, + "lookback_ms": 60000, + "output_groups": 4, + "output_series_upper_bound": 32, + "promql": "topk by (region) (8, sum_over_time(data[1m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region" + ], + "id": "q06-topk-sum/rqe", + "interval_ms": 2000, + "k": 8, + "lookback_ms": 60000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 20) * 20 - 600) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 20) * 20 - 600)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 20) * 20 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 20) * 20 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-topk-count", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, increase(data_total[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 20000, + "k": 8, + "lookback_ms": 600000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 80, + "promql": "topk by (region, service) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q10-cardinality", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -600, NOW()) AND NOW() GROUP BY region", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "x2", + "version": 1 +} From 556e2cd54f0d1f81655b2897e6f0ece3c5d692a2 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 14:48:49 -0400 Subject: [PATCH 21/29] feat(asap-tools): pooled quantiles and top-k in random workloads --pooled keeps a workload's random choices and makes its quantiles pooled (one over each group's samples; the PromQL stays the avg-of-series stand-in) and its sum top-k rank the fine grouping's groups. ClickHouse SQL renders both from the RQE. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../datasets/fake_metric/random_workloads.py | 75 +++++++++++++++---- .../tests/test_random_workloads.py | 44 ++++++++++- 2 files changed, 104 insertions(+), 15 deletions(-) diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads.py b/asap-tools/experiments/datasets/fake_metric/random_workloads.py index 5bb2a4d6..ea5868db 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads.py @@ -96,6 +96,14 @@ def rqe_query(rqe): } if capability == "sum": return Query(**common, grouping=grouping, series_fn=None, agg="sum") + if capability == "quantile" and rqe.get("pooled"): + return Query( + **common, + grouping=grouping, + series_fn=None, + agg="quantile", + quantile=f"{rqe['phi']:.2f}", + ) if capability == "quantile": quantile = f"{rqe['phi']:.2f}" return Query( @@ -105,6 +113,10 @@ def rqe_query(rqe): agg="avg", quantile=quantile, ) + if capability == "topk_by_value" and rqe.get("pooled"): + return Query( + **common, grouping=grouping, series_fn=None, agg="sum", topk=rqe["k"] + ) if capability == "topk_by_value": return Query( **common, @@ -247,7 +259,12 @@ def sum_query(query_id, grouping, lookback_ms, interval_ms): ) -def quantile_query(query_id, grouping, phi, lookback_ms=QUANTILE_LOOKBACK_MS): +def quantile_query( + query_id, grouping, phi, lookback_ms=QUANTILE_LOOKBACK_MS, pooled=False +): + """A quantile by `grouping`. Pooled, its RQE is the quantile of all of a + group's samples; PromQL can't say that, so the PromQL is the avg of + per-series quantiles either way, a stand-in when pooled.""" rendered = f"avg by ({grouping_text(grouping)}) (quantile_over_time({phi:.2f}, data[{lookback_ms // SECOND_MS}s]))" return query( query_id, @@ -258,12 +275,18 @@ def quantile_query(query_id, grouping, phi, lookback_ms=QUANTILE_LOOKBACK_MS): rendered, "common-promql", phi=phi, + **({"pooled": True} if pooled else {}), ) -def topk_sum_query(query_id, grouping, lookback_ms, interval_ms, k): - rendered = f"topk by ({grouping_text(grouping)}) ({k}, sum_over_time(data[{duration(lookback_ms)}]))" - return query( +def topk_sum_query(query_id, grouping, lookback_ms, interval_ms, k, pooled=False): + """The k largest windowed sums: per series within each `grouping` group, + or pooled, of `grouping`'s groups over all series.""" + if pooled: + rendered = f"topk({k}, sum by ({grouping_text(grouping)}) (sum_over_time(data[{duration(lookback_ms)}])))" + else: + rendered = f"topk by ({grouping_text(grouping)}) ({k}, sum_over_time(data[{duration(lookback_ms)}]))" + item = query( query_id, "topk_by_value", grouping, @@ -272,7 +295,11 @@ def topk_sum_query(query_id, grouping, lookback_ms, interval_ms, k): rendered, "common-promql", k=k, + **({"pooled": True} if pooled else {}), ) + if pooled: + item["output_groups"], item["output_series_upper_bound"] = 1, k + return item def topk_count_query(query_id, grouping, lookback_ms, interval_ms, k): @@ -323,9 +350,11 @@ def cardinality_query(query_id, grouping, lookback_ms, interval_ms): ) -def generate_workload(seed, index, variant="base"): +def generate_workload(seed, index, variant="base", pooled=False): """Return one validated ten-query workload for `seed` and one-based - `index`, stretched as VARIANTS[`variant`] says.""" + `index`, stretched as VARIANTS[`variant`] says. `pooled` makes its + quantiles pooled and its sum top-k rank the fine grouping's groups; its + random choices stay the base's, so its queries pair with them.""" scale, label_0_cardinality = VARIANTS[variant] rng = random.Random(f"{seed}/{index}") fine, coarse = GROUPINGS[2], GROUPINGS[1] @@ -334,28 +363,36 @@ def generate_workload(seed, index, variant="base"): short_w, short_t = scale * MINUTE_MS, rng.choice((SECOND_MS, 2 * SECOND_MS)) long_w, long_t = scale * 20 * MINUTE_MS, rng.choice((30 * SECOND_MS, MINUTE_MS)) quantile_w = scale * QUANTILE_LOOKBACK_MS + + def topk_grouping(): + # Drawn either way, so later draws match the base's. + drawn = rng.choice(GROUPINGS[:2]) + return fine if pooled else drawn + quantile_specs = rng.sample( [(grouping, phi) for grouping in GROUPINGS for phi in QUANTILES], 3 ) queries = [ sum_query("q01-sum-fine", fine, rollup_w, rollup_t), sum_query("q02-sum-coarse", coarse, rollup_w, rollup_t), - quantile_query("q03-quantile", *quantile_specs[0], quantile_w), - quantile_query("q04-quantile", *quantile_specs[1], quantile_w), - quantile_query("q05-quantile", *quantile_specs[2], quantile_w), + quantile_query("q03-quantile", *quantile_specs[0], quantile_w, pooled), + quantile_query("q04-quantile", *quantile_specs[1], quantile_w, pooled), + quantile_query("q05-quantile", *quantile_specs[2], quantile_w, pooled), topk_sum_query( "q06-topk-sum", - rng.choice(GROUPINGS[:2]), + topk_grouping(), short_w, short_t, rng.choice(TOPK_VALUES), + pooled, ), topk_sum_query( "q07-topk-sum", - rng.choice(GROUPINGS[:2]), + topk_grouping(), long_w, long_t, rng.choice(TOPK_VALUES), + pooled, ), topk_count_query( "q08-topk-count", @@ -389,6 +426,8 @@ def generate_workload(seed, index, variant="base"): } if variant != "base": workload["variant"] = variant + if pooled: + workload["pooled"] = True validate_workload(workload) return workload @@ -421,6 +460,7 @@ def validate_workload(workload): rqe["interval_ms"], rqe.get("phi"), rqe.get("k"), + rqe.get("pooled", False), ) for item in queries for rqe in item["rqes"] @@ -448,10 +488,12 @@ def validate_workload(workload): raise ValueError("a workload must contain the fine side of a roll-up chain") -def generate_workloads(seed, workload_count=WORKLOAD_COUNT, variant="base"): +def generate_workloads( + seed, workload_count=WORKLOAD_COUNT, variant="base", pooled=False +): """Generate the deterministic workload suite for one experiment seed.""" return [ - generate_workload(seed, index, variant) + generate_workload(seed, index, variant, pooled) for index in range(1, workload_count + 1) ] @@ -461,9 +503,14 @@ def main(): parser.add_argument("--seed", type=int, required=True) parser.add_argument("--out", type=Path, required=True) parser.add_argument("--variant", choices=sorted(VARIANTS), default="base") + parser.add_argument( + "--pooled", action="store_true", help="pooled quantiles and top-k" + ) args = parser.parse_args() args.out.mkdir(parents=True, exist_ok=True) - for workload in generate_workloads(args.seed, variant=args.variant): + for workload in generate_workloads( + args.seed, variant=args.variant, pooled=args.pooled + ): path = args.out / f"{workload['id']}.json" path.write_text(json.dumps(workload, indent=2, sort_keys=True) + "\n") print(path) diff --git a/asap-tools/experiments/tests/test_random_workloads.py b/asap-tools/experiments/tests/test_random_workloads.py index 953e2c3e..2bf7a978 100644 --- a/asap-tools/experiments/tests/test_random_workloads.py +++ b/asap-tools/experiments/tests/test_random_workloads.py @@ -5,7 +5,6 @@ import unittest from copy import deepcopy - sys.path.insert( 0, os.path.join(os.path.dirname(__file__), "..", "datasets", "fake_metric") ) @@ -101,6 +100,49 @@ def test_variants_pair_with_the_base_query_for_query(self): self.assertEqual(vq_rqe.get("k"), bq_rqe.get("k")) self.assertNotIn("variant", base[0]) + def test_pooled_workloads_pair_with_the_base_query_for_query(self): + # Only the quantile and sum top-k shapes change; every random choice + # stays the base's. + base = workloads.generate_workloads(seed=862) + for b, p in zip(base, workloads.generate_workloads(seed=862, pooled=True)): + with self.subTest(workload=b["id"]): + self.assertTrue(p["pooled"]) + for bq, pq in zip(b["queries"], p["queries"]): + b_rqe, p_rqe = bq["rqes"][0], pq["rqes"][0] + for key in ("capability", "lookback_ms", "interval_ms"): + self.assertEqual(p_rqe[key], b_rqe[key]) + self.assertEqual(p_rqe.get("phi"), b_rqe.get("phi")) + self.assertEqual(p_rqe.get("k"), b_rqe.get("k")) + if b_rqe["capability"] not in ("quantile", "topk_by_value"): + self.assertEqual(pq, bq) + self.assertNotIn("pooled", base[0]) + + def test_a_pooled_quantile_is_one_over_each_groups_samples(self): + # PromQL keeps the avg-of-series stand-in; the RQE and ClickHouse + # take the quantile of the group's raw samples. + q = workloads.quantile_query("q", ("region",), 0.95, pooled=True) + self.assertEqual( + q["promql"], "avg by (region) (quantile_over_time(0.95, data[100s]))" + ) + self.assertTrue(q["rqes"][0]["pooled"]) + self.assertIn("quantileExactInclusive(0.95)(value)", q["clickhouse_sql"]) + self.assertIn("GROUP BY region", q["clickhouse_sql"]) + self.assertNotIn("label_0", q["clickhouse_sql"]) + + def test_a_pooled_topk_ranks_groups_globally(self): + q = workloads.topk_sum_query( + "q", ("region", "service", "job"), 60_000, 1_000, 4, pooled=True + ) + self.assertEqual( + q["promql"], + "topk(4, sum by (region, service, job) (sum_over_time(data[1m])))", + ) + self.assertEqual(q["output_series_upper_bound"], 4) + sql = q["clickhouse_sql"] + self.assertIn("GROUP BY region, service, job ORDER BY value DESC", sql) + self.assertTrue(sql.endswith("LIMIT 4")) + self.assertNotIn("label_0", sql) + def test_manifest_declares_deterministic_feeder_facts(self): facts = workloads.generate_workloads(seed=4)[0]["data_facts"] From 16cd2afb0942783ff01dfd6608d15991a947945c Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 14:54:19 -0400 Subject: [PATCH 22/29] feat(asap-tools): pooled random workloads run on ASAPQuery's RQE frontend seed-862-pooled freezes the pooled variant of the ten workloads with saved plans (expr-only, from sketch-bench eval/random-pooled e2fb479). Their pilot configs send ASAPQuery each query's tree (side file e2e.rqe_exprs.json) while Prometheus runs the PromQL; the e2e config writer reads a tree-only query's text from client_promql. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../fake_metric/random_workload_artifacts.py | 28 +- .../workload-01-pilot/eval_table.json | 530 +++++++++++++++++ .../workload-01-pilot/inference_config.yaml | 52 ++ .../workload-01-pilot/plans.json.gz | Bin 0 -> 2557 bytes .../workload-01-pilot/streaming_config.yaml | 103 ++++ .../seed-862-pooled/workload-01.json | 372 ++++++++++++ .../workload-02-pilot/eval_table.json | 528 +++++++++++++++++ .../workload-02-pilot/inference_config.yaml | 52 ++ .../workload-02-pilot/plans.json.gz | Bin 0 -> 2466 bytes .../workload-02-pilot/streaming_config.yaml | 104 ++++ .../seed-862-pooled/workload-02.json | 370 ++++++++++++ .../workload-03-pilot/eval_table.json | 530 +++++++++++++++++ .../workload-03-pilot/inference_config.yaml | 52 ++ .../workload-03-pilot/plans.json.gz | Bin 0 -> 2501 bytes .../workload-03-pilot/streaming_config.yaml | 104 ++++ .../seed-862-pooled/workload-03.json | 374 ++++++++++++ .../workload-04-pilot/eval_table.json | 524 +++++++++++++++++ .../workload-04-pilot/inference_config.yaml | 52 ++ .../workload-04-pilot/plans.json.gz | Bin 0 -> 2589 bytes .../workload-04-pilot/streaming_config.yaml | 85 +++ .../seed-862-pooled/workload-04.json | 368 ++++++++++++ .../workload-05-pilot/eval_table.json | 528 +++++++++++++++++ .../workload-05-pilot/inference_config.yaml | 52 ++ .../workload-05-pilot/plans.json.gz | Bin 0 -> 2483 bytes .../workload-05-pilot/streaming_config.yaml | 104 ++++ .../seed-862-pooled/workload-05.json | 372 ++++++++++++ .../workload-06-pilot/eval_table.json | 528 +++++++++++++++++ .../workload-06-pilot/inference_config.yaml | 52 ++ .../workload-06-pilot/plans.json.gz | Bin 0 -> 2477 bytes .../workload-06-pilot/streaming_config.yaml | 104 ++++ .../seed-862-pooled/workload-06.json | 374 ++++++++++++ .../workload-07-pilot/eval_table.json | 530 +++++++++++++++++ .../workload-07-pilot/inference_config.yaml | 52 ++ .../workload-07-pilot/plans.json.gz | Bin 0 -> 2562 bytes .../workload-07-pilot/streaming_config.yaml | 103 ++++ .../seed-862-pooled/workload-07.json | 370 ++++++++++++ .../workload-08-pilot/eval_table.json | 526 +++++++++++++++++ .../workload-08-pilot/inference_config.yaml | 52 ++ .../workload-08-pilot/plans.json.gz | Bin 0 -> 2521 bytes .../workload-08-pilot/streaming_config.yaml | 85 +++ .../seed-862-pooled/workload-08.json | 364 ++++++++++++ .../workload-09-pilot/eval_table.json | 532 ++++++++++++++++++ .../workload-09-pilot/inference_config.yaml | 52 ++ .../workload-09-pilot/plans.json.gz | Bin 0 -> 2356 bytes .../workload-09-pilot/streaming_config.yaml | 86 +++ .../seed-862-pooled/workload-09.json | 374 ++++++++++++ .../workload-10-pilot/eval_table.json | 524 +++++++++++++++++ .../workload-10-pilot/inference_config.yaml | 52 ++ .../workload-10-pilot/plans.json.gz | Bin 0 -> 2540 bytes .../workload-10-pilot/streaming_config.yaml | 85 +++ .../seed-862-pooled/workload-10.json | 366 ++++++++++++ .../sketch_bench_e2e_configs.py | 13 +- .../tests/test_random_workload_artifacts.py | 49 +- 53 files changed, 10543 insertions(+), 14 deletions(-) create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10.json diff --git a/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py b/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py index 1b574f40..f47be7db 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py @@ -19,6 +19,10 @@ os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ) sys.path.insert(0, EXPERIMENTS_DIR) +from experiment_utils.config import ( # noqa: E402 + QUERY_FRONTEND_PROMQL, + QUERY_FRONTEND_RQE, +) from sketch_bench_e2e.sketch_bench_e2e_configs import ( # noqa: E402 BASELINE_MODE, build_config, @@ -34,6 +38,8 @@ QUERY_FRESHNESS_DELAY_SECS = 10 # Relative to EXPERIMENTS_DIR, where the runners read configs and SQL. CONFIG_DIR = "config/experiment_type/random_workload" +# A pooled workload's query text -> RQE tree, next to its e2e.yaml. +RQE_EXPRS_FILE = "e2e.rqe_exprs.json" VICTORIAMETRICS_URL = "http://localhost:8428" # 26.6 and later need AVX2, which CloudLab's Xeon E5-2450 nodes lack; the # ClickHouse SQL's semantics tests also pass on 23.8. @@ -128,9 +134,11 @@ def query_groups(queries, query_key): def workload_set(workload): - """The directory of `workload`'s seed and variant: seed-862, seed-862-long.""" + """The directory of `workload`'s seed, variant and query style: + seed-862, seed-862-long, seed-862-x2-pooled.""" variant = workload.get("variant") - return f"seed-{workload['seed']}" + (f"-{variant}" if variant else "") + pooled = "-pooled" if workload.get("pooled") else "" + return f"seed-{workload['seed']}" + (f"-{variant}" if variant else "") + pooled def as_dashboards(groups, dashboards, key): @@ -164,7 +172,11 @@ def pilot_configs(workload, pilot_dir, dashboards=1): the same RQEs' Query over exact views (sketch_bench_workloads). With `dashboards` K > 1, every query group runs K times concurrently, - as K dashboards over the same data, from `-dashK/`.""" + as K dashboards over the same data, from `-dashK/`. + + A pooled workload's ASAPQuery queries go to its RQE frontend, each as its + tree from the eval table (side file `e2e.rqe_exprs.json`); Prometheus + runs their PromQL.""" queries = [q for q in workload["queries"] if q["support"] == PILOT_SUPPORT] lookback_secs = max(q["lookback_ms"] for q in queries) // 1000 starting_delay = lookback_secs + QUERY_FRESHNESS_DELAY_SECS @@ -175,7 +187,8 @@ def pilot_configs(workload, pilot_dir, dashboards=1): variant = f"-dash{dashboards}" if dashboards > 1 else "" out_dir = Path(CONFIG_DIR) / workload_set(workload) / (workload["id"] + variant) - e2e, _, _ = build_config( + pooled = workload.get("pooled", False) + e2e, _, rqe_exprs = build_config( str(table), seed, starting_delay, @@ -183,7 +196,11 @@ def pilot_configs(workload, pilot_dir, dashboards=1): QUERY_FRESHNESS_DELAY_SECS, feeder_duration_secs=feed_secs, prebuilt_output_dir=str(pilot_dir), + frontend=QUERY_FRONTEND_RQE if pooled else QUERY_FRONTEND_PROMQL, ) + side_file = out_dir / RQE_EXPRS_FILE + if pooled: + e2e["query_engine"]["rqe_exprs_file"] = os.path.join(EXPERIMENTS_DIR, side_file) e2e_params = e2e["experiment_params"] e2e_params["query_groups"] = as_dashboards( e2e_params["query_groups"], dashboards, "id" @@ -191,6 +208,7 @@ def pilot_configs(workload, pilot_dir, dashboards=1): # One monitoring tool per run, so VictoriaMetrics is its own baseline run. vm = copy.deepcopy(e2e) del vm["controller"] + vm.pop("query_engine", None) vm_params = vm["experiment_params"] vm_params["experiment"] = [BASELINE_MODE] vm_params["monitoring"] = { @@ -265,6 +283,8 @@ def pilot_configs(workload, pilot_dir, dashboards=1): ) for name, config in (("e2e.yaml", e2e), ("victoriametrics.yaml", vm)): files[name] = "# @package _global_\n" + yaml.safe_dump(config, sort_keys=False) + if pooled: + files[RQE_EXPRS_FILE] = json.dumps(rqe_exprs, indent=1) + "\n" return {out_dir / name: content for name, content in files.items()} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json new file mode 100644 index 00000000..c7abe97c --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json @@ -0,0 +1,530 @@ +{ + "inputs": { + "workload-01.json": "f6ae7dad0bc8193ccd0c0e5344bf1e3537ce189ab9c61fdb4cf27bd1e588eca2" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "e2fb479604437205ea78d11786df24b132d68c9d", + "workloads": [ + { + "dataset": "random/seed=862/workload-01", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 10000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q01-sum-fine/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 20, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-01/q01-sum-fine/keys/600s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum-coarse/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-01/q02-sum-coarse/keys/600s" + ] + }, + { + "client_promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + }, + "interval_secs": 1, + "rqes": [ + "random/seed=862/workload-01/q03-quantile/values/100s" + ] + }, + { + "client_promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q04-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + }, + "interval_secs": 1, + "rqes": [ + "random/seed=862/workload-01/q04-quantile/values/100s" + ] + }, + { + "client_promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q05-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + }, + "interval_secs": 1, + "rqes": [ + "random/seed=862/workload-01/q05-quantile/values/100s" + ] + }, + { + "client_promql": "topk(4, sum by (region, service, job) (sum_over_time(data[1m])))", + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s", + "k": 4, + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 1, + "rqes": [ + "random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s" + ] + }, + { + "client_promql": "topk(4, sum by (region, service, job) (sum_over_time(data[20m])))", + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s", + "k": 4, + "lookback_ms": 1200000, + "metric": "data" + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s" + ] + } + ] + }, + "rqes": [ + { + "assumptions": [], + "capability": "sum", + "covered_groups": 200, + "covered_max_N": 300000.0, + "covered_min_N": 300000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q01-sum-fine/keys/600s", + "instant": false, + "interval_secs": 20, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 600.0, + "max_N": 300000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 300000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q01-sum-fine", + "range": "600s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "sum", + "covered_groups": 20, + "covered_max_N": 3000000.0, + "covered_min_N": 3000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum-coarse/keys/600s", + "instant": false, + "interval_secs": 20, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 600.0, + "max_N": 3000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q02-sum-coarse", + "range": "600s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 200, + "covered_max_N": 50000.0, + "covered_min_N": 50000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q03-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 50000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 50000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q03-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 4, + "covered_max_N": 2500000.0, + "covered_min_N": 2500000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q04-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 4, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 2500000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 2500000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 200, + "covered_max_N": 50000.0, + "covered_min_N": 50000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q05-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 50000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 50000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 6000000.0, + "covered_min_N": 6000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 6000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 120000000.0, + "covered_min_N": 120000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 120000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 120000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/inference_config.yaml new file mode 100644 index 00000000..fefa7a40 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-01/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-01/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-01/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-01/q04-quantile/values/100s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-01/q05-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-01/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-01/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/plans.json.gz 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+ ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/inference_config.yaml new file mode 100644 index 00000000..19a9b213 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-02/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-02/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-02/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-02/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-02/q05-quantile/values/100s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 99 + query_time_aggregations: [] + random/seed=862/workload-02/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-02/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/plans.json.gz 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sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 5000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 5000 + windowType: tumbling +- aggregationId: 2 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 240 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 5 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json new file mode 100644 index 00000000..c0c6b0f2 --- /dev/null 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"child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/inference_config.yaml new file mode 100644 index 00000000..930fbad4 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-03/q01-sum-fine/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/q02-sum-coarse/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-03/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-03/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-03/q05-quantile/values/100s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 99 + query_time_aggregations: [] + random/seed=862/workload-03/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/plans.json.gz 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"target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-04/q04-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 500000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 500000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 4, + "covered_max_N": 2500000.0, + "covered_min_N": 2500000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-04", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", 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"grouping": [], + "id": "random/seed=862/workload-04/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 6000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 120000000.0, + "covered_min_N": 120000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-04", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-04/topk4_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 120000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 120000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/inference_config.yaml new file mode 100644 index 00000000..9456994c --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-04/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-04/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-04/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-04/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + 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diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/streaming_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/streaming_config.yaml new file mode 100644 index 00000000..54c43cf0 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/streaming_config.yaml @@ -0,0 +1,85 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 10000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 10000 + windowType: tumbling +- aggregationId: 2 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 4096 + slideIntervalMs: 2000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json new file mode 100644 index 00000000..3a8e0fe6 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json @@ -0,0 +1,368 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 10000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-04", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ], + "pooled": true, + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 10000, + "lookback_ms": 600000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 10000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 10000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 10000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service", + "grouping": [ + "region", + 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}, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/inference_config.yaml new file mode 100644 index 00000000..a87ae631 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-05/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-05/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-05/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-05/q04-quantile/values/100s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 99 + query_time_aggregations: [] + random/seed=862/workload-05/q05-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-05/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-05/topk8_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 60 + 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aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 10000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 10000 + windowType: tumbling +- aggregationId: 2 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +- aggregationId: 5 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 480 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json new file mode 100644 index 00000000..76ea80a6 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json @@ -0,0 +1,372 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 10000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-05", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ], + "pooled": true, + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.50)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.5, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, quantileExactInclusive(0.90)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q05-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job ORDER BY value DESC, region, service, job LIMIT 8", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q06-topk-sum", + "interval_ms": 1000, + "lookback_ms": 60000, + "output_groups": 1, + "output_series_upper_bound": 8, + "promql": "topk(8, sum by (region, service, job) (sum_over_time(data[1m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q06-topk-sum/rqe", + "interval_ms": 1000, + "k": 8, + "lookback_ms": 60000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job ORDER BY value DESC, region, service, job LIMIT 4", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 1, + "output_series_upper_bound": 4, + "promql": "topk(4, sum by (region, service, job) (sum_over_time(data[20m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 10) * 10 - 300) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 10) * 10 - 300)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 10) * 10 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 10) * 10 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-topk-count", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, increase(data_total[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 10000, + "k": 8, + "lookback_ms": 300000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 32, + "promql": "topk by (region) (8, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": 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user_id) FROM events WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, service, job", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json new file mode 100644 index 00000000..6ea6f7c4 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json @@ -0,0 +1,528 @@ +{ + "inputs": { + "workload-06.json": "7adc3fc78cc0d4b87a03ec909e6216c32bbc04c575d4b8d5cefd5c9d77e46899" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "e2fb479604437205ea78d11786df24b132d68c9d", + "workloads": [ + { + "dataset": "random/seed=862/workload-06", + "e2e": { + "metrics": { + "data": { + "labels": [ + 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"name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/inference_config.yaml new file mode 100644 index 00000000..b26da77d --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-06/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-06/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: + - 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num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-07/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-07/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-07/q05-quantile/values/100s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-07/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-07/topk8_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 20 + query_time_aggregations: [] diff --git 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grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 4096 + slideIntervalMs: 2000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: tumbling +- aggregationId: 5 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 60000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 60000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json new file mode 100644 index 00000000..b983956e --- /dev/null +++ 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+ ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/inference_config.yaml new file mode 100644 index 00000000..75e7ce10 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-08/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-08/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-08/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-08/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-08/q05-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-08/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-08/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/plans.json.gz 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a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/inference_config.yaml new file mode 100644 index 00000000..33caa83e --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-09/q01-sum-fine/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-09/q02-sum-coarse/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-09/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-09/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-09/q05-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-09/topk8_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-09/topk8_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/plans.json.gz 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/streaming_config.yaml @@ -0,0 +1,86 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 2000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: tumbling +- aggregationId: 2 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 4000 + windowType: sliding +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 480 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 320 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json new file mode 100644 index 00000000..e3e15bb8 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json @@ -0,0 +1,374 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 10000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-09", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + 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--git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json new file mode 100644 index 00000000..f0143366 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json @@ -0,0 +1,524 @@ +{ + "inputs": { + "workload-10.json": "7000c3dd1036282b7908afe3c5fa499ef4e872f9689aaa2f4ca366f07849f30a" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "e2fb479604437205ea78d11786df24b132d68c9d", + "workloads": [ + { + "dataset": "random/seed=862/workload-10", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 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a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/inference_config.yaml new file mode 100644 index 00000000..d03c89e5 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/inference_config.yaml @@ -0,0 +1,52 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-10/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-10/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-10/q03-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-10/q04-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-10/q05-quantile/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 100 + query_time_aggregations: [] + random/seed=862/workload-10/topk8_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-10/topk8_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/plans.json.gz 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"interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, quantileExactInclusive(0.90)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q05-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 2) * 2 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 2) * 2, 3) GROUP BY region, service, job ORDER BY value DESC, region, service, job LIMIT 8", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q06-topk-sum", + "interval_ms": 2000, + "lookback_ms": 60000, + "output_groups": 1, + "output_series_upper_bound": 8, + "promql": "topk(8, sum by (region, service, job) (sum_over_time(data[1m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q06-topk-sum/rqe", + "interval_ms": 2000, + "k": 8, + "lookback_ms": 60000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job ORDER BY value DESC, region, service, job LIMIT 8", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 1, + "output_series_upper_bound": 8, + "promql": "topk(8, sum by (region, service, job) (sum_over_time(data[20m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 20) * 20 - 600) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 20) * 20 - 600)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 20) * 20 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 20) * 20 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-topk-count", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, increase(data_total[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 20000, + "k": 8, + "lookback_ms": 600000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 80, + "promql": "topk by (region, service) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q10-cardinality", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -600, NOW()) AND NOW() GROUP BY region", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "version": 1 +} diff --git a/asap-tools/experiments/sketch_bench_e2e/sketch_bench_e2e_configs.py b/asap-tools/experiments/sketch_bench_e2e/sketch_bench_e2e_configs.py index db659658..14b1f895 100644 --- a/asap-tools/experiments/sketch_bench_e2e/sketch_bench_e2e_configs.py +++ b/asap-tools/experiments/sketch_bench_e2e/sketch_bench_e2e_configs.py @@ -119,9 +119,16 @@ def build_config( e2e = load_e2e(table_path) notes: List[str] = [] - # (entry, query text in the config) - promql_entries = [(q, q["promql"]) for q in e2e["queries"] if "promql" in q] - structured = [q for q in e2e["queries"] if "promql" not in q] + # (entry, query text in the config). A tree-only query's `client_promql` + # is what the other systems run. + promql_entries = [ + (q, q.get("promql") or q["client_promql"]) + for q in e2e["queries"] + if "promql" in q or "client_promql" in q + ] + structured = [ + q for q in e2e["queries"] if "promql" not in q and "client_promql" not in q + ] sql_entries: List[Tuple[dict, str]] = [] if structured and sql_queries_file is None: notes.append( diff --git a/asap-tools/experiments/tests/test_random_workload_artifacts.py b/asap-tools/experiments/tests/test_random_workload_artifacts.py index cd158a2f..e465dc40 100644 --- a/asap-tools/experiments/tests/test_random_workload_artifacts.py +++ b/asap-tools/experiments/tests/test_random_workload_artifacts.py @@ -40,7 +40,7 @@ def all_workloads(): w for variant in workloads.VARIANTS for w in workloads.generate_workloads(seed=862, variant=variant) - ] + ] + workloads.generate_workloads(seed=862, pooled=True) def pilot_dir_of(workload): @@ -111,22 +111,34 @@ def test_archived_pilot_plans_read_the_manifests_pilot_queries(self): pilot_dir = pilot_dir_of(workload) with open(pilot_dir / "eval_table.json") as f: e2e = json.load(f)["workloads"][0]["e2e"] + # A pooled query is its tree; its text is client_promql. + texts = [q.get("promql") or q["client_promql"] for q in e2e["queries"]] self.assertEqual( - [(q["promql"], q["interval_secs"] * 1000) for q in e2e["queries"]], + [ + (t, q["interval_secs"] * 1000) + for t, q in zip(texts, e2e["queries"]) + ], [ (q["promql"], q["interval_ms"]) for q in workload["queries"] if q["support"] == "common-promql" ], ) - # The planner's configs answer every pilot query. + # The planner's configs answer every pilot query: by text, or + # (pooled, an expr-only plan) by each tree's RQEs. inference = yaml.safe_load( (pilot_dir / "inference_config.yaml").read_text() ) - self.assertEqual( - sorted(q["query"] for q in inference["queries"]), - sorted(q["promql"] for q in e2e["queries"]), - ) + if workload.get("pooled"): + self.assertEqual( + set(inference["rqe_configs"]), + {r for q in e2e["queries"] for r in q["rqes"]}, + ) + else: + self.assertEqual( + sorted(q["query"] for q in inference["queries"]), + sorted(texts), + ) def test_every_workload_renders_its_pilot_configs(self): for workload in all_workloads(): @@ -194,6 +206,29 @@ def test_every_arm_feeds_the_same_data_and_schedule(self): raw_sql = "".join(files[f"interval_{t}s_baseline.sql"] for t in (1, 20, 30)) self.assertTrue(all(sql in raw_sql for sql in pilot_sql)) + def test_pooled_queries_reach_asapquery_as_trees_and_prometheus_as_text(self): + workload = workloads.generate_workloads(seed=862, pooled=True)[0] + files = artifacts.pilot_configs(workload, pilot_dir_of(workload)) + by_name = {p.name: c for p, c in files.items()} + e2e = yaml.safe_load(by_name["e2e.yaml"]) + side = json.loads(by_name[artifacts.RQE_EXPRS_FILE]) + texts = [ + q for g in e2e["experiment_params"]["query_groups"] for q in g["queries"] + ] + self.assertEqual(e2e["query_engine"]["frontend"], "rqe") + self.assertTrue(os.path.isabs(e2e["query_engine"]["rqe_exprs_file"])) + self.assertEqual(set(side), set(texts)) + # The pooled quantile: one leaf at its grouping, under the avg-of-series + # stand-in text Prometheus runs. + stand_in = "avg by (region) (quantile_over_time(0.95, data[100s]))" + self.assertEqual(side[stand_in]["rqe"]["grouping"], ["region"]) + self.assertNotIn( + "query_engine", + yaml.safe_load(by_name["victoriametrics.yaml"]), + ) + self.assertEqual({p.parent.name for p in files}, {"workload-01"}) + self.assertEqual({p.parent.parent.name for p in files}, {"seed-862-pooled"}) + def test_two_dashboards_repeat_every_group_on_the_same_plan(self): workload = workloads.generate_workloads(seed=862)[0] From 76f0f45e228f9fc6409aa9dce49c2539d25e3399 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 14:56:21 -0400 Subject: [PATCH 23/29] feat(asap-tools): check a pooled set's quantiles against raw ClickHouse Prometheus and VictoriaMetrics answer a pooled quantile with the avg-of-series stand-in, so those queries are left unchecked for them and the other arms' are checked against raw ClickHouse's exact pooled quantiles. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../multi_experiment/collate_batch.py | 33 +++++++++++-- .../experiments/tests/test_collate_batch.py | 47 +++++++++++++++++++ 2 files changed, 75 insertions(+), 5 deletions(-) create mode 100644 asap-tools/experiments/tests/test_collate_batch.py diff --git a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py index abb675e2..b6c2cc9e 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py @@ -6,7 +6,10 @@ - no query failed or timed out; - its answers agree with the reference arm (Prometheus, which keeps up, or else ClickHouse+MV): exact arms to EXACT_REL_ERR, ASAPQuery's sketches to - their bounds. + their bounds. In a pooled set (`*-pooled`) the quantiles are pooled, which + PromQL can't say: Prometheus and VictoriaMetrics run an avg-of-series + stand-in, unchecked, and the other arms' quantiles are checked against raw + ClickHouse instead. Separately, whether ingest kept up: during the query phase the feeder never ran more than MAX_INGEST_LAG_SECS behind (from the feeder's @@ -38,6 +41,8 @@ from random_workload_batch import ARMS, OUTPUTS_DIR # noqa: E402 REFERENCE = "prom" +# Exact pooled quantiles, for a pooled set's quantile queries. +POOLED_REFERENCE = "ch" # The reference of a batch without Prometheus: exact, and answers every window. FALLBACK_REFERENCE = "chmv" # The queries' freshness delay: a feed further behind leaves on-time @@ -84,11 +89,13 @@ def max_ingest_lag(arm, mode_dir): return max(t["start_lag_ms"] for t in query_ticks) / 1000 -def disagreements(arm_name, arm, ref): - """The queries whose answers fall outside `arm_name`'s bounds against - the reference arm `ref`.""" +def disagreements(arm_name, arm, ref, checked=lambda name: True): + """The `checked` queries whose answers fall outside `arm_name`'s bounds + against the reference arm `ref`.""" bad = [] for gq, name in arm.names.items(): + if not checked(name): + continue topk = name.startswith("topk") quantile = "quantile" in name values = [] @@ -141,8 +148,24 @@ def rows(batch_dir): why = [] if len(recorded) != len(latencies): why.append(f"{len(recorded) - len(latencies)} failed queries") + pooled = set_name.endswith("-pooled") if arm_name != reference: - why += disagreements(arm_name, arm, arms[reference]) + why += disagreements( + arm_name, + arm, + arms[reference], + lambda name: not (pooled and "quantile" in name), + ) + if pooled and arm_name not in (REFERENCE, "vm", POOLED_REFERENCE): + if POOLED_REFERENCE not in arms: + why.append("pooled quantiles: no raw ClickHouse to check against") + else: + why += disagreements( + arm_name, + arm, + arms[POOLED_REFERENCE], + lambda name: "quantile" in name, + ) out.append( { "set": set_name, diff --git a/asap-tools/experiments/tests/test_collate_batch.py b/asap-tools/experiments/tests/test_collate_batch.py new file mode 100644 index 00000000..e3db3fab --- /dev/null +++ b/asap-tools/experiments/tests/test_collate_batch.py @@ -0,0 +1,47 @@ +"""Tests for collate_batch.py's answer checks.""" + +import os +import sys +import unittest +from types import SimpleNamespace + +sys.path.insert( + 0, + os.path.join( + os.path.dirname(__file__), "..", "post_experiment", "multi_experiment" + ), +) + +import collate_batch # noqa: E402 + +SUM = "sum by (job) (sum_over_time(data[1m]))" +QUANTILE = "avg by (job) (quantile_over_time(0.95, data[100s]))" + + +def arm(sum_value, quantile_value): + """One window of a sum and a quantile query, both for job="a".""" + labels = (("job", "a"),) + return SimpleNamespace( + names={(0, 0): SUM, (0, 1): QUANTILE}, + results={(0, 0, 0): {labels: sum_value}, (0, 1, 0): {labels: quantile_value}}, + ) + + +class DisagreementsTest(unittest.TestCase): + def test_only_checked_queries_count(self): + # A pooled quantile differs from Prometheus's avg-of-series stand-in by + # design; leaving it unchecked keeps the sum's check. + prom, asap = arm(100.0, 1.0), arm(100.0, 2.0) + self.assertEqual(len(collate_batch.disagreements("asap", asap, prom)), 1) + not_quantile = lambda name: "quantile" not in name # noqa: E731 + self.assertEqual( + collate_batch.disagreements("asap", asap, prom, not_quantile), [] + ) + bad_sum = arm(90.0, 2.0) + self.assertEqual( + len(collate_batch.disagreements("asap", bad_sum, prom, not_quantile)), 1 + ) + + +if __name__ == "__main__": + unittest.main() From 40cd9bad2c67c1dc5de243ec98763070c33f9288 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 15:13:38 -0400 Subject: [PATCH 24/29] feat(asap-tools): pooled quantiles' PromQL stand-in is a quantile of quantiles Prometheus and VictoriaMetrics now run quantile by (G) (phi, quantile_over_time(phi, data[100s])), which keeps the tail the pooled quantile reads. Eval tables re-exported (sketch-bench eval/random-pooled); the tree-only plans are unchanged. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../datasets/fake_metric/random_workloads.py | 12 ++++++++---- .../workload-01-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-01.json | 6 +++--- .../workload-02-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-02.json | 6 +++--- .../workload-03-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-03.json | 6 +++--- .../workload-04-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-04.json | 6 +++--- .../workload-05-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-05.json | 6 +++--- .../workload-06-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-06.json | 6 +++--- .../workload-07-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-07.json | 6 +++--- .../workload-08-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-08.json | 6 +++--- .../workload-09-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-09.json | 6 +++--- .../workload-10-pilot/eval_table.json | 8 ++++---- .../seed-862-pooled/workload-10.json | 6 +++--- .../tests/test_random_workload_artifacts.py | 2 +- .../experiments/tests/test_random_workloads.py | 9 ++++++--- 23 files changed, 85 insertions(+), 78 deletions(-) diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads.py b/asap-tools/experiments/datasets/fake_metric/random_workloads.py index ea5868db..f3c21504 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads.py @@ -262,10 +262,14 @@ def sum_query(query_id, grouping, lookback_ms, interval_ms): def quantile_query( query_id, grouping, phi, lookback_ms=QUANTILE_LOOKBACK_MS, pooled=False ): - """A quantile by `grouping`. Pooled, its RQE is the quantile of all of a - group's samples; PromQL can't say that, so the PromQL is the avg of - per-series quantiles either way, a stand-in when pooled.""" - rendered = f"avg by ({grouping_text(grouping)}) (quantile_over_time({phi:.2f}, data[{lookback_ms // SECOND_MS}s]))" + """A quantile by `grouping`: the avg of per-series quantiles, or pooled, + the quantile of all of a group's samples. PromQL can't say the pooled + one, so its PromQL is a stand-in: the quantile of per-series quantiles.""" + window = f"data[{lookback_ms // SECOND_MS}s]" + if pooled: + rendered = f"quantile by ({grouping_text(grouping)}) ({phi:.2f}, quantile_over_time({phi:.2f}, {window}))" + else: + rendered = f"avg by ({grouping_text(grouping)}) (quantile_over_time({phi:.2f}, {window}))" return query( query_id, "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json index c7abe97c..c1db5248 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-01.json": "f6ae7dad0bc8193ccd0c0e5344bf1e3537ce189ab9c61fdb4cf27bd1e588eca2" + "workload-01.json": "6336ace527a5c45bdb3fbd80b97119cbff9c5aad54e37652deb64b480f6329dd" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -107,7 +107,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -126,7 +126,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01.json index a5a6f0ef..9ac11994 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-01.json @@ -128,7 +128,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -159,7 +159,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -190,7 +190,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region, service, job) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/eval_table.json index 501fd592..ce9c07f5 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-02.json": "8764d641ac3aeb8e45b6c5a68732b7cf2671ceefea09d2b008d1d4d66daf1680" + "workload-02.json": "cfbe843a50708f45954525d8842c7336ae46906250819af5fe6ef3acf7b8c3bd" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -105,7 +105,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -125,7 +125,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json index c0c6b0f2..c0e9248f 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-02.json @@ -126,7 +126,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -156,7 +156,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -188,7 +188,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/eval_table.json index c63c2731..8c7f4d5a 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-03.json": "3093dd7247457a0cc677b433a7553bf4bd8f8deecc95a788559cad584eeccacc" + "workload-03.json": "8a4b5ba24ed1fb16dadde706d403e3d84153196991f96661a9855eee27b0e7cf" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -106,7 +106,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.90, data[100s]))", + "client_promql": "quantile by (region, service) (0.90, quantile_over_time(0.90, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -126,7 +126,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.90, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.90, quantile_over_time(0.90, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03.json index 88a2ef6d..9bde8edd 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-03.json @@ -127,7 +127,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -158,7 +158,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.90, data[100s]))", + "promql": "quantile by (region, service) (0.90, quantile_over_time(0.90, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -190,7 +190,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.90, data[100s]))", + "promql": "quantile by (region, service, job) (0.90, quantile_over_time(0.90, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/eval_table.json index cb3b5054..008022fe 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-04.json": "64efa9abfbcec32a16f6bdac1d6ad009ebc1bc67c81758c01821c9610898d9c2" + "workload-04.json": "47de424386e550884f9e6c0edac8b901db3525310d79b6daf85e70f343cd28d4" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -105,7 +105,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region, service) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -125,7 +125,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json index 3a8e0fe6..06e28ad8 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-04.json @@ -126,7 +126,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -156,7 +156,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region, service) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -186,7 +186,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/eval_table.json index 5bc1b5d3..bf7b9700 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-05.json": "21f70c2eaf8d63f965011d13c54320719e74e531bc5cf5536e9f3b3d6a9ca533" + "workload-05.json": "ca0dfc12777d578f43749ca33fdd995667034ccce4d32fae5bc99f7294d1deef" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "client_promql": "quantile by (region, service) (0.99, quantile_over_time(0.99, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -106,7 +106,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.50, quantile_over_time(0.50, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -127,7 +127,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "client_promql": "quantile by (region) (0.90, quantile_over_time(0.90, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json index 76ea80a6..032aa0f4 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-05.json @@ -127,7 +127,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "promql": "quantile by (region, service) (0.99, quantile_over_time(0.99, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -159,7 +159,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "promql": "quantile by (region, service, job) (0.50, quantile_over_time(0.50, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -190,7 +190,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "promql": "quantile by (region) (0.90, quantile_over_time(0.90, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json index 6ea6f7c4..84ae6347 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-06.json": "7adc3fc78cc0d4b87a03ec909e6216c32bbc04c575d4b8d5cefd5c9d77e46899" + "workload-06.json": "bf1e4f42dd3bf8756e4620fe99e27ee9c12b90077a4afe0617412684ad730b89" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.50, quantile_over_time(0.50, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -107,7 +107,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.50, data[100s]))", + "client_promql": "quantile by (region, service) (0.50, quantile_over_time(0.50, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -127,7 +127,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "client_promql": "quantile by (region) (0.90, quantile_over_time(0.90, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06.json index 43fd0476..41b7873d 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-06.json @@ -128,7 +128,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "promql": "quantile by (region, service, job) (0.50, quantile_over_time(0.50, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -160,7 +160,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.50, data[100s]))", + "promql": "quantile by (region, service) (0.50, quantile_over_time(0.50, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -190,7 +190,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "promql": "quantile by (region) (0.90, quantile_over_time(0.90, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/eval_table.json index 41fbd78d..86e8b4ae 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-07.json": "96407a2a5ed3fbd24a16730a4cc8af77175d80da782d72b649c9a3dd28e832ef" + "workload-07.json": "96320b7bd0cafb410ce0ac8192892cb74c0bcf46e9017295dab7b749936be18d" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -107,7 +107,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -128,7 +128,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.50, data[100s]))", + "client_promql": "quantile by (region) (0.50, quantile_over_time(0.50, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json index b983956e..96f41ed4 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-07.json @@ -128,7 +128,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.99, data[100s]))", + "promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -161,7 +161,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region, service, job) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -192,7 +192,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.50, data[100s]))", + "promql": "quantile by (region) (0.50, quantile_over_time(0.50, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/eval_table.json index e67dc850..9a9e6187 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-08.json": "5e79ba0be024855e04f9d30ca818789d27da53b65f86b8b071d636a362a3f13f" + "workload-08.json": "d67a3f18f815cd2d9e07f8e984b1712cdfdcba3c313efc0832acbbb41d4c45b7" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region, service) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -106,7 +106,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -126,7 +126,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08.json index dd2fd1c6..7e5cc01c 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-08.json @@ -127,7 +127,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region, service) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -158,7 +158,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -188,7 +188,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/eval_table.json index d4c66d9e..a7d65816 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-09.json": "7997bf826a84abff7d35d6fb86b96efe3f21d710210c136b0ddfbe2bb23ea6d1" + "workload-09.json": "126fc1c569f5bcdad42717a234140cc8a0c0e80ff90bcf58f09aced8a83291fe" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -107,7 +107,7 @@ ] }, { - "client_promql": "avg by (region, service, job) (quantile_over_time(0.95, data[100s]))", + "client_promql": "quantile by (region, service, job) (0.95, quantile_over_time(0.95, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -128,7 +128,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region, service) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json index e3e15bb8..4bba62b4 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-09.json @@ -128,7 +128,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region, service, job) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -161,7 +161,7 @@ "lookback_ms": 100000, "output_groups": 200, "output_series_upper_bound": 200, - "promql": "avg by (region, service, job) (quantile_over_time(0.95, data[100s]))", + "promql": "quantile by (region, service, job) (0.95, quantile_over_time(0.95, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -193,7 +193,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region, service) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json index f0143366..c0a432dd 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-10.json": "7000c3dd1036282b7908afe3c5fa499ef4e872f9689aaa2f4ca366f07849f30a" + "workload-10.json": "f5857b4fa8da1a624720c8167bef138e715bd89f1efbddbff9dc346fe646eace" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -86,7 +86,7 @@ ] }, { - "client_promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "client_promql": "quantile by (region, service) (0.99, quantile_over_time(0.99, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -106,7 +106,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "client_promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[100s]))", "expr": { "rqe": { "capability": "quantile", @@ -125,7 +125,7 @@ ] }, { - "client_promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "client_promql": "quantile by (region) (0.90, quantile_over_time(0.90, data[100s]))", "expr": { "rqe": { "capability": "quantile", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10.json index 54aedfe7..6c474bf0 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-pooled/workload-10.json @@ -127,7 +127,7 @@ "lookback_ms": 100000, "output_groups": 20, "output_series_upper_bound": 20, - "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "promql": "quantile by (region, service) (0.99, quantile_over_time(0.99, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -157,7 +157,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, @@ -186,7 +186,7 @@ "lookback_ms": 100000, "output_groups": 4, "output_series_upper_bound": 4, - "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "promql": "quantile by (region) (0.90, quantile_over_time(0.90, data[100s]))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/tests/test_random_workload_artifacts.py b/asap-tools/experiments/tests/test_random_workload_artifacts.py index e465dc40..96c19204 100644 --- a/asap-tools/experiments/tests/test_random_workload_artifacts.py +++ b/asap-tools/experiments/tests/test_random_workload_artifacts.py @@ -220,7 +220,7 @@ def test_pooled_queries_reach_asapquery_as_trees_and_prometheus_as_text(self): self.assertEqual(set(side), set(texts)) # The pooled quantile: one leaf at its grouping, under the avg-of-series # stand-in text Prometheus runs. - stand_in = "avg by (region) (quantile_over_time(0.95, data[100s]))" + stand_in = "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))" self.assertEqual(side[stand_in]["rqe"]["grouping"], ["region"]) self.assertNotIn( "query_engine", diff --git a/asap-tools/experiments/tests/test_random_workloads.py b/asap-tools/experiments/tests/test_random_workloads.py index 2bf7a978..6becf235 100644 --- a/asap-tools/experiments/tests/test_random_workloads.py +++ b/asap-tools/experiments/tests/test_random_workloads.py @@ -115,14 +115,17 @@ def test_pooled_workloads_pair_with_the_base_query_for_query(self): self.assertEqual(p_rqe.get("k"), b_rqe.get("k")) if b_rqe["capability"] not in ("quantile", "topk_by_value"): self.assertEqual(pq, bq) + if b_rqe["capability"] == "quantile": + self.assertEqual(pq["grouping"], bq["grouping"]) self.assertNotIn("pooled", base[0]) def test_a_pooled_quantile_is_one_over_each_groups_samples(self): - # PromQL keeps the avg-of-series stand-in; the RQE and ClickHouse - # take the quantile of the group's raw samples. + # PromQL's stand-in is the quantile of per-series quantiles; the RQE + # and ClickHouse take the quantile of the group's raw samples. q = workloads.quantile_query("q", ("region",), 0.95, pooled=True) self.assertEqual( - q["promql"], "avg by (region) (quantile_over_time(0.95, data[100s]))" + q["promql"], + "quantile by (region) (0.95, quantile_over_time(0.95, data[100s]))", ) self.assertTrue(q["rqes"][0]["pooled"]) self.assertIn("quantileExactInclusive(0.95)(value)", q["clickhouse_sql"]) From 9b3801287cd459fa77523043f596bf664447aa3e Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 15:19:59 -0400 Subject: [PATCH 25/29] fix(asap-tools): the batch's Prometheus arm runs PromQL under an RQE config Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- asap-tools/experiments/random_workload_batch.py | 7 ++++++- asap-tools/experiments/tests/test_random_workload_batch.py | 2 ++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/asap-tools/experiments/random_workload_batch.py b/asap-tools/experiments/random_workload_batch.py index b4a35ff6..33d67816 100644 --- a/asap-tools/experiments/random_workload_batch.py +++ b/asap-tools/experiments/random_workload_batch.py @@ -57,7 +57,12 @@ "prom": ( "experiment_run_e2e.py", "e2e", - ["experiment_params.experiment=[{mode:baseline,server:prometheus}]"], + [ + "experiment_params.experiment=[{mode:baseline,server:prometheus}]", + # Prometheus runs the PromQL text, even when ASAPQuery's mode of + # the same config reads RQE trees. + "query_engine.frontend=promql", + ], "baseline", ), "vm": ("experiment_run_e2e.py", "victoriametrics", [], "baseline"), diff --git a/asap-tools/experiments/tests/test_random_workload_batch.py b/asap-tools/experiments/tests/test_random_workload_batch.py index 97213359..edbc46fc 100644 --- a/asap-tools/experiments/tests/test_random_workload_batch.py +++ b/asap-tools/experiments/tests/test_random_workload_batch.py @@ -76,6 +76,8 @@ class Args: self.assertIn( "experiment_params.experiment=[{mode:baseline,server:prometheus}]", cmd ) + # A pooled config's frontend=rqe needs the sketchdb mode left out here. + self.assertIn("query_engine.frontend=promql", cmd) if __name__ == "__main__": From 3ceec9ac5eccef60fb15967e7dea1498a7474be7 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 16:56:56 -0400 Subject: [PATCH 26/29] feat(asap-tools): dashboard-style pooled random workloads --dashboard draws ten pooled queries per workload: a sum roll-up chain at every grouping, a p50/p90/p99 panel, pooled quantiles at the other groupings and pooled top-k by service and by microservice, over windows of 1 s to 20 m. seed-862-dashboard freezes ten, with saved plans. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../fake_metric/random_workload_artifacts.py | 8 +- .../datasets/fake_metric/random_workloads.py | 137 +++- .../workload-01-pilot/eval_table.json | 721 +++++++++++++++++ .../workload-01-pilot/inference_config.yaml | 73 ++ .../workload-01-pilot/plans.json.gz | Bin 0 -> 3032 bytes .../workload-01-pilot/streaming_config.yaml | 103 +++ .../seed-862-dashboard/workload-01.json | 370 +++++++++ .../workload-02-pilot/eval_table.json | 717 +++++++++++++++++ .../workload-02-pilot/inference_config.yaml | 73 ++ .../workload-02-pilot/plans.json.gz | Bin 0 -> 3031 bytes .../workload-02-pilot/streaming_config.yaml | 103 +++ .../seed-862-dashboard/workload-02.json | 366 +++++++++ .../workload-03-pilot/eval_table.json | 717 +++++++++++++++++ .../workload-03-pilot/inference_config.yaml | 73 ++ .../workload-03-pilot/plans.json.gz | Bin 0 -> 3040 bytes .../workload-03-pilot/streaming_config.yaml | 103 +++ .../seed-862-dashboard/workload-03.json | 366 +++++++++ .../workload-04-pilot/eval_table.json | 725 ++++++++++++++++++ .../workload-04-pilot/inference_config.yaml | 73 ++ .../workload-04-pilot/plans.json.gz | Bin 0 -> 3049 bytes .../workload-04-pilot/streaming_config.yaml | 103 +++ .../seed-862-dashboard/workload-04.json | 374 +++++++++ .../workload-05-pilot/eval_table.json | 725 ++++++++++++++++++ .../workload-05-pilot/inference_config.yaml | 73 ++ .../workload-05-pilot/plans.json.gz | Bin 0 -> 3097 bytes .../workload-05-pilot/streaming_config.yaml | 85 ++ .../seed-862-dashboard/workload-05.json | 374 +++++++++ .../workload-06-pilot/eval_table.json | 717 +++++++++++++++++ .../workload-06-pilot/inference_config.yaml | 73 ++ .../workload-06-pilot/plans.json.gz | Bin 0 -> 2824 bytes .../workload-06-pilot/streaming_config.yaml | 102 +++ .../seed-862-dashboard/workload-06.json | 366 +++++++++ .../workload-07-pilot/eval_table.json | 717 +++++++++++++++++ .../workload-07-pilot/inference_config.yaml | 73 ++ .../workload-07-pilot/plans.json.gz | Bin 0 -> 3113 bytes .../workload-07-pilot/streaming_config.yaml | 85 ++ .../seed-862-dashboard/workload-07.json | 366 +++++++++ .../workload-08-pilot/eval_table.json | 725 ++++++++++++++++++ .../workload-08-pilot/inference_config.yaml | 73 ++ .../workload-08-pilot/plans.json.gz | Bin 0 -> 3013 bytes .../workload-08-pilot/streaming_config.yaml | 103 +++ .../seed-862-dashboard/workload-08.json | 374 +++++++++ .../workload-09-pilot/eval_table.json | 717 +++++++++++++++++ .../workload-09-pilot/inference_config.yaml | 73 ++ .../workload-09-pilot/plans.json.gz | Bin 0 -> 2914 bytes .../workload-09-pilot/streaming_config.yaml | 103 +++ .../seed-862-dashboard/workload-09.json | 366 +++++++++ .../workload-10-pilot/eval_table.json | 725 ++++++++++++++++++ .../workload-10-pilot/inference_config.yaml | 73 ++ .../workload-10-pilot/plans.json.gz | Bin 0 -> 3275 bytes .../workload-10-pilot/streaming_config.yaml | 85 ++ .../seed-862-dashboard/workload-10.json | 374 +++++++++ .../multi_experiment/collate_batch.py | 2 +- .../tests/test_random_workload_artifacts.py | 14 +- .../tests/test_random_workloads.py | 40 + 55 files changed, 12796 insertions(+), 12 deletions(-) create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-02-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-02-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-02-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-02-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-02.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/inference_config.yaml create mode 100644 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asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-06-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-06-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-06-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-06.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/plans.json.gz create mode 100644 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asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10.json diff --git a/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py b/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py index f47be7db..8e572deb 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workload_artifacts.py @@ -137,8 +137,12 @@ def workload_set(workload): """The directory of `workload`'s seed, variant and query style: seed-862, seed-862-long, seed-862-x2-pooled.""" variant = workload.get("variant") - pooled = "-pooled" if workload.get("pooled") else "" - return f"seed-{workload['seed']}" + (f"-{variant}" if variant else "") + pooled + style = ( + f"-{workload['style']}" + if "style" in workload + else "-pooled" if workload.get("pooled") else "" + ) + return f"seed-{workload['seed']}" + (f"-{variant}" if variant else "") + style def as_dashboards(groups, dashboards, key): diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads.py b/asap-tools/experiments/datasets/fake_metric/random_workloads.py index f3c21504..3b05374a 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads.py @@ -52,6 +52,37 @@ ) QUANTILES = (0.50, 0.75, 0.90, 0.95, 0.99) TOPK_VALUES = (4, 8) +# The dashboard style's windows and the repeat intervals each allows. +DASHBOARD_INTERVALS = { + SECOND_MS: (SECOND_MS,), + MINUTE_MS: (SECOND_MS, 2 * SECOND_MS), + QUANTILE_LOOKBACK_MS: (SECOND_MS,), + 5 * MINUTE_MS: (5 * SECOND_MS, 10 * SECOND_MS), + 10 * MINUTE_MS: (10 * SECOND_MS, 20 * SECOND_MS), + 20 * MINUTE_MS: (30 * SECOND_MS, MINUTE_MS), +} +DASHBOARD_SUM_WINDOWS = (MINUTE_MS, 5 * MINUTE_MS, 10 * MINUTE_MS, 20 * MINUTE_MS) +DASHBOARD_PANEL_WINDOWS = ( + SECOND_MS, + MINUTE_MS, + QUANTILE_LOOKBACK_MS, + 5 * MINUTE_MS, + 10 * MINUTE_MS, + 20 * MINUTE_MS, +) +DASHBOARD_QUANTILE_WINDOWS = ( + SECOND_MS, + MINUTE_MS, + 5 * MINUTE_MS, + 10 * MINUTE_MS, + 20 * MINUTE_MS, +) +DASHBOARD_TOPK_WINDOWS = (SECOND_MS, MINUTE_MS, 5 * MINUTE_MS, 20 * MINUTE_MS) +DASHBOARD_PANEL_PHIS = (0.50, 0.90, 0.99) +DASHBOARD_TOPK_VALUES = (3, 5, 8) +# A 1 s window holds 10 samples per series: a pooled quantile needs the +# accuracy curves' 1,000 per group, which (region, service, job)'s 500 miss. +DASHBOARD_ONE_SECOND_GROUPINGS = GROUPINGS[:2] BACKFILL_HISTORY_MS = 20 * MINUTE_MS LEAF_SERIES_COUNT = math.prod(LABEL_CARDINALITIES.values()) LEAF_LABELS = tuple(name for name, _, _ in LABEL_HIERARCHY) @@ -260,7 +291,12 @@ def sum_query(query_id, grouping, lookback_ms, interval_ms): def quantile_query( - query_id, grouping, phi, lookback_ms=QUANTILE_LOOKBACK_MS, pooled=False + query_id, + grouping, + phi, + lookback_ms=QUANTILE_LOOKBACK_MS, + pooled=False, + interval_ms=QUANTILE_INTERVAL_MS, ): """A quantile by `grouping`: the avg of per-series quantiles, or pooled, the quantile of all of a group's samples. PromQL can't say the pooled @@ -275,7 +311,7 @@ def quantile_query( "quantile", grouping, lookback_ms, - QUANTILE_INTERVAL_MS, + interval_ms, rendered, "common-promql", phi=phi, @@ -436,6 +472,91 @@ def topk_grouping(): return workload +def generate_dashboard_workload(seed, index, variant="base"): + """One ten-query workload shaped like a dashboard over pooled queries: a + sum roll-up chain (one window at every grouping), a p50/p90/p99 panel by + one grouping, pooled quantiles at the other two, and pooled top-k by + service and by microservice. Each workload draws its windows, intervals, + groupings, phis and k. Sums never use a 1 s window: its two edge samples + make PromQL's closed window and ASAPQuery's left-open one differ by a + tenth.""" + _, label_0_cardinality = VARIANTS[variant] + rng = random.Random(f"{seed}/{index}/dashboard") + + def window(choices, quantile_grouping=None): + w = rng.choice(choices) + while ( + w == SECOND_MS + and quantile_grouping + and quantile_grouping not in DASHBOARD_ONE_SECOND_GROUPINGS + ): + w = rng.choice(choices) + return w, rng.choice(DASHBOARD_INTERVALS[w]) + + sum_w, sum_t = window(DASHBOARD_SUM_WINDOWS) + queries = [ + sum_query(f"q0{i + 1}-sum", grouping, sum_w, sum_t) + for i, grouping in enumerate(GROUPINGS) + ] + panel_grouping = rng.choice(GROUPINGS) + panel_w, panel_t = window(DASHBOARD_PANEL_WINDOWS, panel_grouping) + queries += [ + quantile_query( + f"q0{4 + i}-panel-p{round(phi * 100)}", + panel_grouping, + phi, + panel_w, + pooled=True, + interval_ms=panel_t, + ) + for i, phi in enumerate(DASHBOARD_PANEL_PHIS) + ] + for i, grouping in enumerate(g for g in GROUPINGS if g != panel_grouping): + w, t = window(DASHBOARD_QUANTILE_WINDOWS, grouping) + queries.append( + quantile_query( + f"q0{7 + i}-quantile", + grouping, + rng.choice(QUANTILES), + w, + pooled=True, + interval_ms=t, + ) + ) + for i, grouping in enumerate(GROUPINGS[1:]): + w, t = window(DASHBOARD_TOPK_WINDOWS) + queries.append( + topk_sum_query( + f"q{9 + i:02d}-topk", + grouping, + w, + t, + rng.choice(DASHBOARD_TOPK_VALUES), + pooled=True, + ) + ) + labels = metric_labels(label_0_cardinality) + workload = { + "version": 1, + "id": f"workload-{index:02d}", + "seed": seed, + "labels": labels, + "samples_per_sec_per_series": SAMPLES_PER_SEC_PER_SERIES, + "data_facts": workload_data_facts( + seed, + math.prod(label["cardinality"] for label in labels), + BACKFILL_HISTORY_MS, + ), + "queries": queries, + "pooled": True, + "style": "dashboard", + } + if variant != "base": + workload["variant"] = variant + validate_workload(workload) + return workload + + def validate_workload(workload): """Reject manifests that violate the experiment's stable public contract.""" queries = workload["queries"] @@ -493,9 +614,14 @@ def validate_workload(workload): def generate_workloads( - seed, workload_count=WORKLOAD_COUNT, variant="base", pooled=False + seed, workload_count=WORKLOAD_COUNT, variant="base", pooled=False, dashboard=False ): """Generate the deterministic workload suite for one experiment seed.""" + if dashboard: + return [ + generate_dashboard_workload(seed, index, variant) + for index in range(1, workload_count + 1) + ] return [ generate_workload(seed, index, variant, pooled) for index in range(1, workload_count + 1) @@ -510,10 +636,13 @@ def main(): parser.add_argument( "--pooled", action="store_true", help="pooled quantiles and top-k" ) + parser.add_argument( + "--dashboard", action="store_true", help="the dashboard-style pooled template" + ) args = parser.parse_args() args.out.mkdir(parents=True, exist_ok=True) for workload in generate_workloads( - args.seed, variant=args.variant, pooled=args.pooled + args.seed, variant=args.variant, pooled=args.pooled, dashboard=args.dashboard ): path = args.out / f"{workload['id']}.json" path.write_text(json.dumps(workload, indent=2, sort_keys=True) + "\n") diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/eval_table.json new file mode 100644 index 00000000..4770ad5a --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/eval_table.json @@ -0,0 +1,721 @@ +{ + "inputs": { + "workload-01.json": "d33232f131e4b84ac6f5619ba26084072324995263f152981d1167fce0c01c97" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "2d2935a9e1a28d1613c11fdd38530f237e6db99b", + "workloads": [ + { + "dataset": "random/seed=862/workload-01", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 10000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q01-sum/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/q01-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region, service) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/q02-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q03-sum/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region, service, job) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/q03-sum/keys/60s" + ] + }, + { + "client_promql": "quantile by (region, service) (0.50, quantile_over_time(0.50, data[1200s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q04-panel-p50/values/1200s", + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.5 + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/q04-panel-p50/values/1200s" + ] + }, + { + "client_promql": "quantile by (region, service) (0.90, quantile_over_time(0.90, data[1200s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q05-panel-p90/values/1200s", + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.9 + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/q05-panel-p90/values/1200s" + ] + }, + { + "client_promql": "quantile by (region, service) (0.99, quantile_over_time(0.99, data[1200s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q06-panel-p99/values/1200s", + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.99 + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/q06-panel-p99/values/1200s" + ] + }, + { + "client_promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[1s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q07-quantile/values/1s", + "lookback_ms": 1000, + "metric": "data", + "phi": 0.75 + } + }, + "interval_secs": 1, + "rqes": [ + "random/seed=862/workload-01/q07-quantile/values/1s" + ] + }, + { + "client_promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[300s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q08-quantile/values/300s", + "lookback_ms": 300000, + "metric": "data", + "phi": 0.99 + } + }, + "interval_secs": 5, + "rqes": [ + "random/seed=862/workload-01/q08-quantile/values/300s" + ] + }, + { + "client_promql": "topk(3, sum by (region, service) (sum_over_time(data[1m])))", + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/topk3_q09-topk/keys/60s", + "k": 3, + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "rqes": [ + "random/seed=862/workload-01/topk3_q09-topk/keys/60s" + ] + }, + { + "client_promql": "topk(8, sum by (region, service, job) (sum_over_time(data[5m])))", + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/topk8_q10-topk/keys/300s", + "k": 8, + "lookback_ms": 300000, + "metric": "data" + } + }, + "interval_secs": 5, + "rqes": [ + "random/seed=862/workload-01/topk8_q10-topk/keys/300s" + ] + } + ] + }, + "rqes": [ + { + "assumptions": [], + "capability": "sum", + "covered_groups": 4, + "covered_max_N": 1500000.0, + "covered_min_N": 1500000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q01-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 4, + "keys_per_window": null + }, + "lookback_secs": 60.0, + "max_N": 1500000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 1500000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "q01-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "sum", + "covered_groups": 20, + "covered_max_N": 300000.0, + "covered_min_N": 300000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 60.0, + "max_N": 300000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 300000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q02-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "sum", + "covered_groups": 200, + "covered_max_N": 30000.0, + "covered_min_N": 30000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q03-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 60.0, + "max_N": 30000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 30000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q03-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20, + "covered_max_N": 6000000.0, + "covered_min_N": 6000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q04-panel-p50/values/1200s", + "instant": false, + "interval_secs": 30, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 1200.0, + "max_N": 6000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q04-panel-p50", + "range": "1200s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 20, + "covered_max_N": 6000000.0, + "covered_min_N": 6000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q05-panel-p90/values/1200s", + "instant": false, + "interval_secs": 30, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 1200.0, + "max_N": 6000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 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"data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 200, + "covered_max_N": 150000.0, + "covered_min_N": 150000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q08-quantile/values/300s", + "instant": false, + "interval_secs": 5, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 300.0, + "max_N": 150000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 150000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q08-quantile", + "range": "300s", + "stream": "data/dist" + }, + { + "assumptions": [ + "20 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 6000000.0, + "covered_min_N": 6000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-01/topk3_q09-topk/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 6000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk3_q09-topk", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 30000000.0, + "covered_min_N": 30000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-01/topk8_q10-topk/keys/300s", + "instant": false, + "interval_secs": 5, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 300.0, + "max_N": 30000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 30000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk8_q10-topk", + "range": "300s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/inference_config.yaml new file mode 100644 index 00000000..53ee8d1b --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-01-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-01/q01-sum/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-01/q02-sum/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-01/q03-sum/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-01/q04-panel-p50/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 1200 + query_time_aggregations: [] + random/seed=862/workload-01/q05-panel-p90/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 1200 + query_time_aggregations: [] + random/seed=862/workload-01/q06-panel-p99/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 1200 + query_time_aggregations: [] + random/seed=862/workload-01/q07-quantile/values/1s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 1 + query_time_aggregations: [] + 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"query_id": "topk3_q09-topk", + "range": "1s", + "stream": "data/value" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 30000000.0, + "covered_min_N": 30000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-03", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-03/topk3_q10-topk/keys/300s", + "instant": false, + "interval_secs": 10, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 300.0, + "max_N": 30000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 30000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk3_q10-topk", + "range": "300s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/inference_config.yaml new file mode 100644 index 00000000..75c5a0ec --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-03/q01-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-03/q02-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-03/q03-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-03/q04-panel-p50/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/q05-panel-p90/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/q06-panel-p99/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/q07-quantile/values/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/q08-quantile/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/topk3_q09-topk/keys/1s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 1 + query_time_aggregations: [] + random/seed=862/workload-03/topk3_q10-topk/keys/300s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 300 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..0c130fbe70e667cbfbd47d116b63cb0fb18ff691 GIT binary patch literal 3040 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20000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 20000 + windowType: tumbling +- aggregationId: 2 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 10000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 10000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 900 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +- aggregationId: 5 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 900 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 1000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json new file mode 100644 index 00000000..ed4153e2 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json @@ -0,0 +1,366 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, 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1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-04/topk3_q10-topk/keys/300s", + "instant": false, + "interval_secs": 10, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 300.0, + "max_N": 30000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 30000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk3_q10-topk", + "range": "300s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-04-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-04-pilot/inference_config.yaml new file mode 100644 index 00000000..80ef153b --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-04-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-04/q01-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-04/q02-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-04/q03-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-04/q04-panel-p50/values/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-04/q05-panel-p90/values/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-04/q06-panel-p99/values/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-04/q07-quantile/values/300s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-04/q08-quantile/values/300s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-04/topk3_q10-topk/keys/300s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-04/topk8_q09-topk/keys/300s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 30 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-04-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-04-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..bca6a58adb1f79fede9eef4fc772164483a3c0a9 GIT binary patch literal 3049 zcmZvbc|6qlAIGhv(Gp`3mFsK5$PgupMl{2?Gsc-?4a%)`=UhkI7)M5|uyW1eRv|Zq zoXIhcR7S2uZZhuqQLX*$&(o!<7(^fCFAVn;m$NS zqT?~{$k(!568lxf@f{ zGT`2DPR$(8p3NX?%FtAUat_ucZd)x;M)6|Fx;cUN@{OE}Z_@ z@M7lzM@M^Zb(M5$SKx)JOk){M5BSW;pO;i?Lahg;{FlR`LqZ?dr>0q3by0SA)(t{r 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-07/q01-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 20 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-07/q02-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 20 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-07/q03-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 20 + query_time_aggregations: [] + random/seed=862/workload-07/q04-panel-p50/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-07/q05-panel-p90/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-07/q06-panel-p99/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-07/q07-quantile/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 120 + query_time_aggregations: [] + random/seed=862/workload-07/q08-quantile/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-07/topk3_q09-topk/keys/300s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 300 + query_time_aggregations: [] + random/seed=862/workload-07/topk3_q10-topk/keys/1s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 1 + query_time_aggregations: [] diff 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} + ], + "grouping": [], + "id": "random/seed=862/workload-08/topk5_q09-topk/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 6000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk5_q09-topk", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 6000000.0, + "covered_min_N": 6000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-08", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-08/topk8_q10-topk/keys/60s", + "instant": false, + "interval_secs": 1, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 100000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 6000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 6000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk8_q10-topk", + "range": "60s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-08-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-08-pilot/inference_config.yaml new file mode 100644 index 00000000..963704b5 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-08-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-08/q01-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 40 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-08/q02-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 40 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-08/q03-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-08/q04-panel-p50/values/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 58 + query_time_aggregations: [] + random/seed=862/workload-08/q05-panel-p90/values/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 58 + query_time_aggregations: [] + random/seed=862/workload-08/q06-panel-p99/values/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 58 + query_time_aggregations: [] + random/seed=862/workload-08/q07-quantile/values/600s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-08/q08-quantile/values/600s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-08/topk5_q09-topk/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 59 + query_time_aggregations: [] + random/seed=862/workload-08/topk8_q10-topk/keys/60s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 59 + 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aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-09/q02-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-09/q03-sum/keys/600s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-09/q04-panel-p50/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 300 + query_time_aggregations: [] + random/seed=862/workload-09/q05-panel-p90/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 300 + query_time_aggregations: [] + random/seed=862/workload-09/q06-panel-p99/values/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 300 + query_time_aggregations: [] + random/seed=862/workload-09/q07-quantile/values/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-09/q08-quantile/values/1200s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-09/topk3_q09-topk/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-09/topk8_q10-topk/keys/1200s: + aggregations: + - aggregation_id: 5 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-09-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..f7008c0dfd5c3e412aaaaf88daf39cc921075728 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-10/q01-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 40 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-10/q02-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 40 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-10/q03-sum/keys/1200s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-10/q04-panel-p50/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-10/q05-panel-p90/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-10/q06-panel-p99/values/100s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 97 + query_time_aggregations: [] + random/seed=862/workload-10/q07-quantile/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 1197 + query_time_aggregations: [] + random/seed=862/workload-10/q08-quantile/values/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 297 + query_time_aggregations: [] + random/seed=862/workload-10/topk3_q10-topk/keys/300s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-10/topk5_q09-topk/keys/300s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 60 + query_time_aggregations: [] diff 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a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/streaming_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/streaming_config.yaml new file mode 100644 index 00000000..53fb9be4 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10-pilot/streaming_config.yaml @@ -0,0 +1,85 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +- aggregationId: 2 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 4000 + windowType: sliding +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 300 + width: 4096 + slideIntervalMs: 5000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 5000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 300 + width: 4096 + slideIntervalMs: 5000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 5000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10.json new file mode 100644 index 00000000..4dcaa051 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-10.json @@ -0,0 +1,374 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 10000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-10", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 50, + "child_of": "job", + "name": "label_0" + } + ], + "pooled": true, + "queries": [ + { + "clickhouse_sql": "SELECT region, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q01-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "sum by (region) (sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region" + ], + "id": "q01-sum/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q03-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q03-sum/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.50)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q04-panel-p50", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "quantile by (region, service, job) (0.50, quantile_over_time(0.50, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q04-panel-p50/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.5, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.90)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q05-panel-p90", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "quantile by (region, service, job) (0.90, quantile_over_time(0.90, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q05-panel-p90/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q06-panel-p99", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q06-panel-p99/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q07-quantile", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "quantile by (region) (0.99, quantile_over_time(0.99, data[1200s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q07-quantile/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.99, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-quantile", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[300s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-quantile/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "data", + "phi": 0.95, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service ORDER BY value DESC, region, service LIMIT 5", + "grouping": [ + "region", + "service" + ], + "id": "q09-topk", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 1, + "output_series_upper_bound": 5, + "promql": "topk(5, sum by (region, service) (sum_over_time(data[5m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-topk/rqe", + "interval_ms": 10000, + "k": 5, + "lookback_ms": 300000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job ORDER BY value DESC, region, service, job LIMIT 3", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-topk", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 1, + "output_series_upper_bound": 3, + "promql": "topk(3, sum by (region, service, job) (sum_over_time(data[5m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-topk/rqe", + "interval_ms": 5000, + "k": 3, + "lookback_ms": 300000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "style": "dashboard", + "version": 1 +} diff --git a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py index b6c2cc9e..5717cbff 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py @@ -148,7 +148,7 @@ def rows(batch_dir): why = [] if len(recorded) != len(latencies): why.append(f"{len(recorded) - len(latencies)} failed queries") - pooled = set_name.endswith("-pooled") + pooled = set_name.endswith(("-pooled", "-dashboard")) if arm_name != reference: why += disagreements( arm_name, diff --git a/asap-tools/experiments/tests/test_random_workload_artifacts.py b/asap-tools/experiments/tests/test_random_workload_artifacts.py index 96c19204..60015744 100644 --- a/asap-tools/experiments/tests/test_random_workload_artifacts.py +++ b/asap-tools/experiments/tests/test_random_workload_artifacts.py @@ -36,11 +36,15 @@ def all_workloads(): - return [ - w - for variant in workloads.VARIANTS - for w in workloads.generate_workloads(seed=862, variant=variant) - ] + workloads.generate_workloads(seed=862, pooled=True) + return ( + [ + w + for variant in workloads.VARIANTS + for w in workloads.generate_workloads(seed=862, variant=variant) + ] + + workloads.generate_workloads(seed=862, pooled=True) + + workloads.generate_workloads(seed=862, dashboard=True) + ) def pilot_dir_of(workload): diff --git a/asap-tools/experiments/tests/test_random_workloads.py b/asap-tools/experiments/tests/test_random_workloads.py index 6becf235..69e7d7e0 100644 --- a/asap-tools/experiments/tests/test_random_workloads.py +++ b/asap-tools/experiments/tests/test_random_workloads.py @@ -146,6 +146,46 @@ def test_a_pooled_topk_ranks_groups_globally(self): self.assertTrue(sql.endswith("LIMIT 4")) self.assertNotIn("label_0", sql) + def test_dashboard_workloads_follow_their_template(self): + for w in workloads.generate_workloads(seed=862, dashboard=True): + with self.subTest(workload=w["id"]): + qs = w["queries"] + self.assertEqual(len(qs), 10) + self.assertTrue(w["pooled"]) + # A sum roll-up chain: one window at every grouping. + sums = qs[:3] + self.assertEqual( + [tuple(q["grouping"]) for q in sums], list(workloads.GROUPINGS) + ) + self.assertEqual(len({q["lookback_ms"] for q in sums}), 1) + # A p50/p90/p99 panel: one grouping and window. + panel = qs[3:6] + self.assertEqual( + [q["rqes"][0]["phi"] for q in panel], + list(workloads.DASHBOARD_PANEL_PHIS), + ) + self.assertEqual( + len({(tuple(q["grouping"]), q["lookback_ms"]) for q in panel}), 1 + ) + # The other quantiles cover the other groupings. + self.assertEqual( + {tuple(q["grouping"]) for q in qs[3:8]}, set(workloads.GROUPINGS) + ) + for q in qs: + rqe = q["rqes"][0] + if rqe["capability"] != "sum": + self.assertTrue(rqe["pooled"]) + if q["lookback_ms"] == workloads.SECOND_MS: + # PromQL's closed 1 s window holds one more sample than + # ASAPQuery's left-open one: a tenth of a sum. + self.assertNotEqual(rqe["capability"], "sum") + if rqe["capability"] == "quantile": + self.assertIn( + tuple(q["grouping"]), + workloads.DASHBOARD_ONE_SECOND_GROUPINGS, + ) + self.assertLessEqual(q["lookback_ms"], 20 * workloads.MINUTE_MS) + def test_manifest_declares_deterministic_feeder_facts(self): facts = workloads.generate_workloads(seed=4)[0]["data_facts"] From 8bb1a91d039826e2a77edde2781c9cc7a78a31af Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 17:18:17 -0400 Subject: [PATCH 27/29] feat(asap-tools): half variant (5,000 series) and cardinality points of the dashboard template seed-862-half freezes the 5,000-series variant with saved plans. The half and x2 dashboard sets freeze workload-01 with its plan, for a cardinality sweep at 5k, 10k and 20k series. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../datasets/fake_metric/random_workloads.py | 2 + .../workload-01-pilot/eval_table.json | 721 ++++++++++++++++++ .../workload-01-pilot/inference_config.yaml | 73 ++ .../workload-01-pilot/plans.json.gz | Bin 0 -> 2995 bytes .../workload-01-pilot/streaming_config.yaml | 120 +++ .../seed-862-half-dashboard/workload-01.json | 371 +++++++++ .../seed-862-half-dashboard/workload-02.json | 367 +++++++++ .../seed-862-half-dashboard/workload-03.json | 367 +++++++++ .../seed-862-half-dashboard/workload-04.json | 375 +++++++++ .../seed-862-half-dashboard/workload-05.json | 375 +++++++++ .../seed-862-half-dashboard/workload-06.json | 367 +++++++++ .../seed-862-half-dashboard/workload-07.json | 367 +++++++++ .../seed-862-half-dashboard/workload-08.json | 375 +++++++++ .../seed-862-half-dashboard/workload-09.json | 367 +++++++++ .../seed-862-half-dashboard/workload-10.json | 375 +++++++++ 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.../seed-862-x2-dashboard/workload-01.json | 371 +++++++++ .../seed-862-x2-dashboard/workload-02.json | 367 +++++++++ .../seed-862-x2-dashboard/workload-03.json | 367 +++++++++ .../seed-862-x2-dashboard/workload-04.json | 375 +++++++++ .../seed-862-x2-dashboard/workload-05.json | 375 +++++++++ .../seed-862-x2-dashboard/workload-06.json | 367 +++++++++ .../seed-862-x2-dashboard/workload-07.json | 367 +++++++++ .../seed-862-x2-dashboard/workload-08.json | 375 +++++++++ .../seed-862-x2-dashboard/workload-09.json | 367 +++++++++ .../seed-862-x2-dashboard/workload-10.json | 375 +++++++++ 79 files changed, 20396 insertions(+) create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/inference_config.yaml create mode 100644 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100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-01-pilot/eval_table.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-01-pilot/inference_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-01-pilot/plans.json.gz create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-01-pilot/streaming_config.yaml create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-01.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-02.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-03.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-04.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-05.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-06.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-07.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-08.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-09.json create mode 100644 asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-10.json diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads.py b/asap-tools/experiments/datasets/fake_metric/random_workloads.py index 3b05374a..a12333ac 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads.py @@ -100,6 +100,8 @@ "base": (1, 50), # Lookbacks x3: 1m -> 3m, ..., 20m -> 60m; quantiles 100s -> 300s. "long": (3, 50), + # half the series: 5,000 at 10/s, 50,000 samples/s. + "half": (1, 25), # x2 series: 20,000 at 10/s, 200,000 samples/s. "x2": (1, 100), # x5 series: 50,000 at 10/s, 500,000 samples/s. diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/eval_table.json new file mode 100644 index 00000000..3e4eefc6 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/eval_table.json @@ -0,0 +1,721 @@ +{ + "inputs": { + "workload-01.json": "4b8dea60b9b5e9347fce80dc45a833f9a572a318d49eee54aa47917861048850" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "2d2935a9e1a28d1613c11fdd38530f237e6db99b", + "workloads": [ + { + "dataset": "random/seed=862/workload-01", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 5000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q01-sum/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/q01-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q02-sum/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region, service) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/q02-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q03-sum/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region, service, job) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-01/q03-sum/keys/60s" + ] + }, + { + "client_promql": "quantile by (region, service) (0.50, quantile_over_time(0.50, data[1200s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q04-panel-p50/values/1200s", + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.5 + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/q04-panel-p50/values/1200s" + ] + }, + { + "client_promql": "quantile by (region, service) (0.90, quantile_over_time(0.90, data[1200s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q05-panel-p90/values/1200s", + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.9 + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/q05-panel-p90/values/1200s" + ] + }, + { + "client_promql": "quantile by (region, service) (0.99, quantile_over_time(0.99, data[1200s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/q06-panel-p99/values/1200s", + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.99 + } + }, + "interval_secs": 30, + "rqes": [ + "random/seed=862/workload-01/q06-panel-p99/values/1200s" + ] + }, + { + "client_promql": "quantile by (region) (0.75, quantile_over_time(0.75, data[1s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q07-quantile/values/1s", + "lookback_ms": 1000, + "metric": "data", + "phi": 0.75 + } + }, + "interval_secs": 1, + "rqes": [ + "random/seed=862/workload-01/q07-quantile/values/1s" + ] + }, + { + "client_promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[300s]))", + "expr": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/q08-quantile/values/300s", + "lookback_ms": 300000, + "metric": "data", + "phi": 0.99 + } + }, + "interval_secs": 5, + "rqes": [ + "random/seed=862/workload-01/q08-quantile/values/300s" + ] + }, + { + "client_promql": "topk(3, sum by (region, service) (sum_over_time(data[1m])))", + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-01/topk3_q09-topk/keys/60s", + "k": 3, + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "rqes": [ + "random/seed=862/workload-01/topk3_q09-topk/keys/60s" + ] + }, + { + "client_promql": "topk(8, sum by (region, service, job) (sum_over_time(data[5m])))", + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-01/topk8_q10-topk/keys/300s", + "k": 8, + "lookback_ms": 300000, + "metric": "data" + } + }, + "interval_secs": 5, + "rqes": [ + "random/seed=862/workload-01/topk8_q10-topk/keys/300s" + ] + } + ] + }, + "rqes": [ + { + "assumptions": [], + "capability": "sum", + "covered_groups": 4, + "covered_max_N": 750000.0, + "covered_min_N": 750000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-01/q01-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 4, + 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"dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-01/topk3_q09-topk/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 3000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk3_q09-topk", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "200 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 1, + "covered_max_N": 15000000.0, + "covered_min_N": 15000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-01", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [], + "id": "random/seed=862/workload-01/topk8_q10-topk/keys/300s", + "instant": false, + "interval_secs": 5, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 1, + "keys_per_window": 1000 + }, + "lookback_secs": 300.0, + "max_N": 15000000.0, + "max_covered_share": 1.0, + "metric": "data", + "min_N": 15000000.0, + "min_covered_share": 1.0, + "queries_per_instance": 1, + "query_id": "topk8_q10-topk", + "range": "300s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/inference_config.yaml new file mode 100644 index 00000000..5ed04c0d --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half-dashboard/workload-01-pilot/inference_config.yaml @@ -0,0 +1,73 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: [] +rqe_configs: + random/seed=862/workload-01/q01-sum/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + parameter: null + random/seed=862/workload-01/q02-sum/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-01/q03-sum/keys/60s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-01/q04-panel-p50/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-01/q05-panel-p90/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-01/q06-panel-p99/values/1200s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 40 + query_time_aggregations: [] + random/seed=862/workload-01/q07-quantile/values/1s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 1 + query_time_aggregations: [] + random/seed=862/workload-01/q08-quantile/values/300s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + 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+cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 58 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region, service) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +rqe_configs: + random/seed=862/workload-01/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-01/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-01/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-01/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-01/q05-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-01-pilot/streaming_config.yaml @@ -0,0 +1,89 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - label_0 + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 25000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 20000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 20000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 80 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 3000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 1024 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-01.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-01.json new file mode 100644 index 00000000..28706495 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-01.json @@ -0,0 +1,363 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 5000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-01", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": 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"instant": false, + "interval_secs": 60, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 3000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02-pilot/inference_config.yaml new file mode 100644 index 00000000..7c6baf27 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02-pilot/inference_config.yaml @@ -0,0 +1,122 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 58 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +rqe_configs: + random/seed=862/workload-02/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-02/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-02/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-02/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02-pilot/streaming_config.yaml @@ -0,0 +1,89 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - label_0 + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 25000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 5000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 5000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 80 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 3000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 80 + width: 1024 + slideIntervalMs: 60000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 60000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02.json new file mode 100644 index 00000000..35038c75 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-02.json @@ -0,0 +1,361 @@ 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"name": "label_0" + } + ], + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 5000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 5000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + 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4, + "lookback_ms": 60000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 80, + "promql": "topk by (region, service) (4, sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 5) * 5 - 300) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 5) * 5 - 300)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 5) * 5 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 5) * 5 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q08-topk-count", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, increase(data_total[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 5000, + "k": 4, + "lookback_ms": 300000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 5000, + "lookback_ms": 300000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, service, job, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, service, job", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "half", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/eval_table.json new file mode 100644 index 00000000..b1323a84 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/eval_table.json @@ -0,0 +1,520 @@ +{ + "inputs": { + "workload-03.json": "9d76c7f5c5714b50827a3bc7271737c9c290406b1c91bc6f20137722ea4535a9" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "2d2935a9e1a28d1613c11fdd38530f237e6db99b", + "workloads": [ + { + "dataset": "random/seed=862/workload-03", + "e2e": { + "metrics": { + "data": { + "labels": [ 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1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/inference_config.yaml new file mode 100644 index 00000000..f2cb7f61 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/inference_config.yaml @@ -0,0 +1,118 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[1m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[1m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[1m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query: topk by (region) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +rqe_configs: + random/seed=862/workload-03/q01-sum-fine/keys/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-03/q02-sum-coarse/keys/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-03/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-03/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-03/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query_time_aggregations: [] + random/seed=862/workload-03/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-03-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..69cbeefa7fceb88b2e30d80e7f60ccb55541d706 GIT binary patch literal 2307 zcmY+?dpy(o9|!Q6l}V><9Cz6yM--_XqRnM4b7^x+ZbjuM6pk#nv05y2=0|et)`sF;1@AHq>FSh4e$IHCEY&4}<|@ZI(ZAl<-^| zP0}-*^qyNXR*jkcn*ki;>WJ9ljR@yn&SG)a{@cp?`$A7>KTNH4#TB<$J7uJL#W<;l zQ) zFN)XJ3d3W2K1O@u_7?WVVx2albdIyfL!wbDJ~}Tle0?gE6AN1cR)g#_>BXLzsrAg> zde@24P5#(?^s4}V7*dPk?2dfznKtF$i_^-!l3sWry`q9!>o0pD@M{`x>3vB3ppX=6rQ ze#EpfgptJqtEMU{`&I?&GX?iuJfuUJ&%ZA8bXq*%FQ?dYzo6Umu2!60)A7hq=ZuHq z|1wX76dN4x8}s*MY;TZl-IKdAwUT1Jl)|eO^+QzM)la1#9D} zx*HXSfpe6v0q3;_QCd^)+y++GXDwt*#Y?O3rixsF2&H7;P%=F?$n#}KYoYfCcMW-# zZC@$xmA43g7+<$s;W4f#v)Ef%^E&TLrX&1f!0+P(z2kP;l+|B|=WNP7-u*Y<43<85 zL%P$}oTQwsV)0}3ua?SAN`lOW!#`y3- 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* (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 1) * 1 - 60) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 1) * 1 - 60)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 1) * 1 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 1) * 1 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-topk-count", + "interval_ms": 1000, + "lookback_ms": 60000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, increase(data_total[1m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 1000, + "k": 8, + "lookback_ms": 60000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 30) * 30 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 30) * 30 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 30) * 30 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 30) * 30 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region" + ], + "id": "q09-rate/rqe", + "interval_ms": 30000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 1) * 1 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality", + "interval_ms": 1000, + "lookback_ms": 60000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 1000, + "lookback_ms": 60000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, service, job, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -60, NOW()) AND NOW() GROUP BY region, service, job", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "half", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/eval_table.json new file mode 100644 index 00000000..86ec00a7 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/eval_table.json @@ -0,0 +1,521 @@ +{ + "inputs": { + "workload-04.json": "08c2be289054ca9175f23b0e71b5d03829a930dbbac80672ba8e39ee673a5617" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "2d2935a9e1a28d1613c11fdd38530f237e6db99b", + "workloads": [ + { + "dataset": "random/seed=862/workload-04", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 5000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-04/q01-sum-fine/keys/600s", + "lookback_ms": 600000, + "metric": "data" + } + }, + "interval_secs": 10, + "promql": "sum by (region, service, job) (sum_over_time(data[10m]))", + "rqes": [ + "random/seed=862/workload-04/q01-sum-fine/keys/600s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-04/q02-sum-coarse/keys/600s", + 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"interval_secs": 2, + "promql": "topk by (region, service) (4, sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-04/topk4_q06-topk-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-04/topk4_q07-topk-sum/keys/1200s", + "k": 4, + "lookback_ms": 1200000, + "metric": "data", + "topk_by": [ + "region" + ] + } + }, + "interval_secs": 30, + "promql": "topk by (region) (4, sum_over_time(data[20m]))", + "rqes": [ + "random/seed=862/workload-04/topk4_q07-topk-sum/keys/1200s" + ] + } + ] + }, + "rqes": [ + { + "assumptions": [], + "capability": "sum", + "covered_groups": 200, + "covered_max_N": 150000.0, + "covered_min_N": 150000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-04", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ 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"label_set": { + "arrival_rate_per_sec": 50000, + "groups": 5000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 0.0002, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 0.0002, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [ + "250 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 150000.0, + "covered_min_N": 150000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-04", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-04/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 150000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 150000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 4, + "covered_max_N": 15000000.0, + "covered_min_N": 15000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-04", + "families": [ + { + "family": "topk", + "grid_K": 1250, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-04/topk4_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 4, + "keys_per_window": 1250 + }, + "lookback_secs": 1200.0, + "max_N": 15000000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 15000000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/inference_config.yaml new file mode 100644 index 00000000..9fa451c5 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/inference_config.yaml @@ -0,0 +1,115 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 40 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 29 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +rqe_configs: + random/seed=862/workload-04/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-04/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-04/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-04/q05-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-04/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 29 + query_time_aggregations: [] + random/seed=862/workload-04/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-04-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..067eaa720f15923ec5f613557fe837ac77497c4d GIT binary patch literal 2322 zcmY+?dpHy70|)Tgl%Z@cCB-}iaG&-=$$HUSI<7&5Rxp3k4I-hN70zw6$B z8KRRlt8YJ=XWrEFKy$~pWlG}2CAHpz9UT{BFezpiTA+Wq98oTuiqH)?Wn&61(<{(Z+{ZNh)`l{bq$;ul6xW2UU@2^I>M>~)|8fM4` zW_s9HQ>$@b#tX4~8!NFFtQO`n8X2LQ--G9W3`eZ7lizW<9`nQXPuBvJ759$+>3`z8mvYH9%GJt!iB`8;XFidX*ch@LWreihp+YrT zAEuC*t;Hx7!|Yr;b0T=@+?$in2J($hw=dkI`uc`exDV`RQ|t1yRtuyRk&;&(ks^fM z1I;7Edks>fpKE(!Atk3CNxz)Fa@P?(R#k0|pEYz0To;LTTk#o%pB(LBU%#&|H!vnb z84q=IBHpk3R%b!0XtUjwu`n9&eAXk|-cs{Cx&M-y<5TN#Y@a<9pJpA#>8x;1Us6b4 z^N!PC71PM;XO-G&q|}cX6PGg;3z$`l-zGiISqLM&1{&ACKJ8ejXOVk{?r6t<5FSf( 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<= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + 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"label_set": { + "arrival_rate_per_sec": 50000, + "groups": 4, + "keys_per_window": 1250 + }, + "lookback_secs": 1200.0, + "max_N": 15000000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 15000000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "topk4_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/inference_config.yaml new file mode 100644 index 00000000..c8fe2b23 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/inference_config.yaml @@ -0,0 +1,122 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 20 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 57 + query: topk by (region, service) (8, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.50, data[100s])) + planned_subquery: quantile_over_time(0.5, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +rqe_configs: + random/seed=862/workload-05/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-05/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-05/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-05/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-05/q05-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-05/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 20 + query_time_aggregations: [] + random/seed=862/workload-05/topk8_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 57 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/plans.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..f47d2c0ad70e2d92f384b31458e5252edebb6230 GIT binary patch literal 2491 zcmZY7c`y|GAHZ?-_{A!Z`)E~?MG=y#9Bat2)GBgh9Z{C7V{>dm=s|^D*0P&BVY6~3 zQn?B_axYf2j>m0R?qjW0e$vm(ZKH_Lj&O=woh{N0WJ)FFJ6%f8i 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a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/streaming_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/streaming_config.yaml new file mode 100644 index 00000000..e503becb --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05-pilot/streaming_config.yaml @@ -0,0 +1,89 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - label_0 + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 25000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 10000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 10000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 80 + width: 4096 + slideIntervalMs: 60000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 60000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 4000 + windowType: sliding +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05.json new file mode 100644 index 00000000..0135f2ca --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-05.json @@ -0,0 +1,361 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 5000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-05", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.50)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.5 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.90)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q05-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q06-topk-sum", + "interval_ms": 1000, + "lookback_ms": 60000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, sum_over_time(data[1m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q06-topk-sum/rqe", + "interval_ms": 1000, + "k": 8, + "lookback_ms": 60000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q07-topk-sum", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region" + ], + "id": "q07-topk-sum/rqe", + "interval_ms": 60000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 10) * 10 - 300) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 10) * 10 - 300)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 10) * 10 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 10) * 10 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-topk-count", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, increase(data_total[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 10000, + "k": 8, + "lookback_ms": 300000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 60) * 60 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 60) * 60 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 60) * 60 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 60) * 60 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 32, + "promql": "topk by (region) (8, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, service, job, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, service, job", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "half", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/eval_table.json new file mode 100644 index 00000000..662c165a --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/eval_table.json @@ -0,0 +1,527 @@ +{ + "inputs": { + "workload-06.json": "ef07dd771c52d3d2811b6058d1797d1c1537b8aefebd6abe1245a508cf04989b" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "2d2935a9e1a28d1613c11fdd38530f237e6db99b", + "workloads": [ + { + "dataset": "random/seed=862/workload-06", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { + "dist": { + "pareto": 2.0 + }, + "value": { + "keys": 5000, + "zipf": 1.1 + } + } + } + }, + "queries": [ + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-06/q01-sum-fine/keys/300s", + "lookback_ms": 300000, + "metric": "data" + } + }, + "interval_secs": 5, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + "random/seed=862/workload-06/q01-sum-fine/keys/300s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-06/q02-sum-coarse/keys/300s", + "lookback_ms": 300000, + "metric": "data" + } + }, + "interval_secs": 5, + "promql": "sum by (region, service) (sum_over_time(data[5m]))", + "rqes": [ + "random/seed=862/workload-06/q02-sum-coarse/keys/300s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service", + "job" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.5 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service, job) (quantile_over_time(0.50, data[100s]))", + "rqes": [ + "random/seed=862/workload-06/q03-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.5 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service) (quantile_over_time(0.50, data[100s]))", + "rqes": [ + "random/seed=862/workload-06/q03-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/q05-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region) (quantile_over_time(0.90, data[100s]))", + "rqes": [ + "random/seed=862/workload-06/q05-quantile/values/100s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/topk4_q06-topk-sum/keys/60s", + "k": 4, + "lookback_ms": 60000, + "metric": "data", + "topk_by": [ + "region", + "service" + ] + } + }, + "interval_secs": 2, + "promql": "topk by (region, service) (4, sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-06/topk4_q06-topk-sum/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "topk_by_value", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/topk8_q07-topk-sum/keys/1200s", + "k": 8, + "lookback_ms": 1200000, + "metric": "data", + "topk_by": [ + "region", + "service" + ] + } + }, + "interval_secs": 60, + "promql": "topk by (region, service) (8, sum_over_time(data[20m]))", + "rqes": [ + "random/seed=862/workload-06/topk8_q07-topk-sum/keys/1200s" + ] + } + ] + }, + "rqes": [ + { + "assumptions": [], + "capability": "sum", + "covered_groups": 200, + "covered_max_N": 75000.0, + "covered_min_N": 75000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-06", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service", + "job" + ], + "id": "random/seed=862/workload-06/q01-sum-fine/keys/300s", + "instant": false, + "interval_secs": 5, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 300.0, + "max_N": 75000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 75000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q01-sum-fine", + "range": "300s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "sum", + "covered_groups": 20, + "covered_max_N": 750000.0, + "covered_min_N": 750000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-06", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-06/q02-sum-coarse/keys/300s", + "instant": false, + "interval_secs": 5, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 300.0, + "max_N": 750000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 750000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q02-sum-coarse", + "range": "300s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 5000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-06", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/q03-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 5000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 0.0002, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 0.0002, + "queries_per_instance": 1, + "query_id": "q03-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 5000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-06", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-06/q05-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 5000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 0.0002, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 0.0002, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [ + "250 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 150000.0, + "covered_min_N": 150000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-06", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-06/topk4_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 60.0, + "max_N": 150000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 150000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk4_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "250 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 3000000.0, + "covered_min_N": 3000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-06", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-06/topk8_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 60, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 3000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/inference_config.yaml new file mode 100644 index 00000000..872029b1 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/inference_config.yaml @@ -0,0 +1,117 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 29 + query: topk by (region, service) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.50, data[100s])) + planned_subquery: quantile_over_time(0.5, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.50, data[100s])) + planned_subquery: quantile_over_time(0.5, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +rqe_configs: + random/seed=862/workload-06/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: [] + random/seed=862/workload-06/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 60 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-06/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-06/q05-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-06/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 29 + query_time_aggregations: [] + random/seed=862/workload-06/topk8_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-06-pilot/plans.json.gz new file mode 100644 index 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"arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 3000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-07-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-07-pilot/inference_config.yaml new file mode 100644 index 00000000..113a835b --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-07-pilot/inference_config.yaml @@ -0,0 +1,123 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query: topk by (region) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service, job) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.50, data[100s])) + planned_subquery: quantile_over_time(0.5, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +rqe_configs: + random/seed=862/workload-07/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-07/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-07/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-07/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + 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0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 25000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 20000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 20000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 1024 + slideIntervalMs: 2000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: tumbling +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + grouping: + - region + - service + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 60000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 60000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-07.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-07.json new file mode 100644 index 00000000..3e0d635e --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-07.json @@ -0,0 +1,359 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 5000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-07", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 20) * 20 - 600, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": 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argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 60) * 60 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 60) * 60, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q09-rate", + "interval_ms": 60000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 160, + "promql": "topk by (region, service) (8, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-rate/rqe", + "interval_ms": 60000, + "k": 8, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/eval_table.json new file mode 100644 index 00000000..572f4040 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/eval_table.json @@ -0,0 +1,518 @@ +{ + "inputs": { + "workload-08.json": "7017f61d3e8cdf9590a733215201bbef7deae0fc98772513ca880799b6ec118a" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 1, + "sketch_bench_revision": "2d2935a9e1a28d1613c11fdd38530f237e6db99b", + "workloads": [ + { + "dataset": "random/seed=862/workload-08", + "e2e": { + "metrics": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "samples_per_sec_per_series": 10, + "type": "gauge", + "values": { 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"job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/inference_config.yaml new file mode 100644 index 00000000..7b53cdf2 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/inference_config.yaml @@ -0,0 +1,116 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[5m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[5m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[5m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[5m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query: topk by (region) (4, sum_over_time(data[1m])) + planned_subquery: topk by (region) (4, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region) (4, sum_over_time(data[20m])) + planned_subquery: topk by (region) (4, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +rqe_configs: + random/seed=862/workload-08/q01-sum-fine/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-08/q02-sum-coarse/keys/300s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-08/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-08/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query_time_aggregations: [] + random/seed=862/workload-08/topk4_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 59 + query_time_aggregations: [] + random/seed=862/workload-08/topk4_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query_time_aggregations: [] diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/plans.json.gz b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/plans.json.gz new file mode 100644 index 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a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/streaming_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/streaming_config.yaml new file mode 100644 index 00000000..bb94015b --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08-pilot/streaming_config.yaml @@ -0,0 +1,89 @@ +aggregations: +- aggregationId: 1 + aggregationSubType: '' + aggregationType: DDSketch + labels: + aggregated: [] + grouping: + - job + - label_0 + - region + - service + rollup: [] + metric: data + parameters: + alpha: 0.1 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 20000 + windowType: sliding +- aggregationId: 2 + aggregationSubType: sum + aggregationType: MultipleSum + labels: + aggregated: + - job + - region + - service + grouping: [] + rollup: [] + metric: data + parameters: {} + slideIntervalMs: 10000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 10000 + windowType: tumbling +- aggregationId: 3 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 120 + width: 1024 + slideIntervalMs: 1000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 2000 + windowType: sliding +- aggregationId: 4 + aggregationSubType: sum + aggregationType: CountMinSketchWithHeap + labels: + aggregated: + - job + - label_0 + - service + grouping: + - region + rollup: [] + metric: data + parameters: + depth: 3 + heapsize: 160 + width: 4096 + slideIntervalMs: 30000 + spatialFilter: '' + table_name: null + value_column: null + windowSizeMs: 30000 + windowType: tumbling +metrics: + data: + - job + - label_0 + - region + - service diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08.json new file mode 100644 index 00000000..114f9651 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-08.json @@ -0,0 +1,351 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 5000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-08", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ], + "queries": [ + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q01-sum-fine", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + 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value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q04-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q05-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "avg by (region) (quantile_over_time(0.95, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q05-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.95 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 60, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0 ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q06-topk-sum", + "interval_ms": 1000, + 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argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 8 BY region", + "grouping": [ + "region" + ], + "id": "q08-topk-count", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 4, + "output_series_upper_bound": 32, + "promql": "topk by (region) (8, increase(data_total[5m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_count", + "grouping_labels": [ + "region" + ], + "id": "q08-topk-count/rqe", + "interval_ms": 10000, + "k": 8, + "lookback_ms": 300000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, label_0, delta * (sampled + to_start + to_end) / sampled / 1200 AS value FROM (SELECT region, service, job, label_0, last_v - first_v AS delta, first_v, last_t - first_t AS sampled, sampled / (n - 1) AS avg_step, if(first_t - (intDiv({{ end_time }}, 30) * 30 - 1200) >= 1.1 * avg_step, avg_step / 2, first_t - (intDiv({{ end_time }}, 30) * 30 - 1200)) AS to_start_clamped, if(delta > 0 AND first_v >= 0, least(to_start_clamped, sampled * first_v / delta), to_start_clamped) AS to_start, if(intDiv({{ end_time }}, 30) * 30 - last_t >= 1.1 * avg_step, avg_step / 2, intDiv({{ end_time }}, 30) * 30 - last_t) AS to_end FROM (SELECT region, service, job, label_0, argMin(value, ts) AS first_v, argMax(value, ts) AS last_v, toFloat64(min(ts)) AS first_t, toFloat64(max(ts)) AS last_t, count() AS n FROM data_total WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service, job, label_0 HAVING n >= 2)) ORDER BY value DESC, region, service, job, label_0 LIMIT 4 BY region", + "grouping": [ + "region" + ], + "id": "q09-rate", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 16, + "promql": "topk by (region) (4, rate(data_total[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "rate", + "grouping_labels": [ + "region" + ], + "id": "q09-rate/rqe", + "interval_ms": 30000, + "k": 4, + "lookback_ms": 1200000, + "metric": "data_total" + } + ], + "sql": null, + "support": "counter-gated-promql" + }, + { + "clickhouse_sql": "SELECT region, uniqExact(user_id) AS value FROM events WHERE ts > toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q10-cardinality", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": null, + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "cardinality", + "grouping_labels": [ + "region" + ], + "id": "q10-cardinality/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "events", + "value": "user_id" + } + ], + "sql": "SELECT region, COUNT(DISTINCT user_id) FROM events WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region", + "support": "sql-only" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "variant": "half", + "version": 1 +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-09-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-09-pilot/eval_table.json new file mode 100644 index 00000000..64f53dc3 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-09-pilot/eval_table.json @@ -0,0 +1,529 @@ +{ + "inputs": { + "workload-09.json": "bd7b796d6235008bbe405c50f71acfed2034b706fa77e975fae93dadcb7ac701" + }, + "plan": "ProjectASAP/ASAPQuery#879", + "schema_version": 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"random/seed=862/workload-09/q01-sum-fine/keys/60s" + ] + }, + { + "expr": { + "rqe": { + "capability": "sum", + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-09/q02-sum-coarse/keys/60s", + "lookback_ms": 60000, + "metric": "data" + } + }, + "interval_secs": 2, + "promql": "sum by (region, service) (sum_over_time(data[1m]))", + "rqes": [ + "random/seed=862/workload-09/q02-sum-coarse/keys/60s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + "region", + "service", + "job" + ], + "of": { + "rqe": { + "capability": "quantile", + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-09/q03-quantile/values/100s", + "lookback_ms": 100000, + "metric": "data", + "phi": 0.75 + } + } + }, + "interval_secs": 1, + "promql": "avg by (region, service, job) (quantile_over_time(0.75, data[100s]))", + "rqes": [ + "random/seed=862/workload-09/q03-quantile/values/100s" + ] + }, + { + "expr": { + "agg": "avg", + "by": [ + 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"grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-09/topk8_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 30, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 3000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-09-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-09-pilot/inference_config.yaml new file mode 100644 index 00000000..6a52df8a --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-09-pilot/inference_config.yaml @@ -0,0 +1,119 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[1m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[1m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[1m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 57 + query: topk by (region, service) (8, sum_over_time(data[1m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 40 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service, job) (quantile_over_time(0.95, data[100s])) + planned_subquery: quantile_over_time(0.95, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + - job + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 81 + query: avg by (region, service) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +rqe_configs: + random/seed=862/workload-09/q01-sum-fine/keys/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-09/q02-sum-coarse/keys/60s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-09/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + 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"keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 200, + "keys_per_window": null + }, + "lookback_secs": 600.0, + "max_N": 150000.0, + "max_covered_share": 0.005, + "metric": "data", + "min_N": 150000.0, + "min_covered_share": 0.005, + "queries_per_instance": 1, + "query_id": "q01-sum-fine", + "range": "600s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "sum", + "covered_groups": 20, + "covered_max_N": 1500000.0, + "covered_min_N": 1500000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-10", + "families": [ + { + "family": "exact-sum", + "grid_K": null, + "grid_param": null, + "sketch": "exact-sum", + "target": 0.0 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-10/q02-sum-coarse/keys/600s", + "instant": false, + "interval_secs": 20, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": null + }, + "lookback_secs": 600.0, + "max_N": 1500000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 1500000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "q02-sum-coarse", + "range": "600s", + "stream": "data/value" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 5000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-10", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-10/q03-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 5000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 0.0002, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 0.0002, + "queries_per_instance": 1, + "query_id": "q03-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 5000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-10", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-10/q04-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 5000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 0.0002, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 0.0002, + "queries_per_instance": 1, + "query_id": "q04-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "quantile", + "covered_groups": 5000, + "covered_max_N": 1000.0, + "covered_min_N": 1000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-10", + "families": [ + { + "family": "kll", + "grid_K": null, + "grid_param": 2.0, + "sketch": "kll-percall", + "target": 0.01 + }, + { + "family": "dd", + "grid_K": null, + "grid_param": 2.0, + "sketch": "dd", + "target": 0.01 + } + ], + "grouping": [ + "region", + "service", + "job", + "label_0" + ], + "id": "random/seed=862/workload-10/q05-quantile/values/100s", + "instant": false, + "interval_secs": 1, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 5000, + "keys_per_window": null + }, + "lookback_secs": 100.0, + "max_N": 1000.0, + "max_covered_share": 0.0002, + "metric": "data", + "min_N": 1000.0, + "min_covered_share": 0.0002, + "queries_per_instance": 1, + "query_id": "q05-quantile", + "range": "100s", + "stream": "data/dist" + }, + { + "assumptions": [], + "capability": "topk", + "covered_groups": 4, + "covered_max_N": 750000.0, + "covered_min_N": 750000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-10", + "families": [ + { + "family": "topk", + "grid_K": 1250, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region" + ], + "id": "random/seed=862/workload-10/topk8_q06-topk-sum/keys/60s", + "instant": false, + "interval_secs": 2, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 4, + "keys_per_window": 1250 + }, + "lookback_secs": 60.0, + "max_N": 750000.0, + "max_covered_share": 0.25, + "metric": "data", + "min_N": 750000.0, + "min_covered_share": 0.25, + "queries_per_instance": 1, + "query_id": "topk8_q06-topk-sum", + "range": "60s", + "stream": "data/value" + }, + { + "assumptions": [ + "250 keys per heap read at the curves' 1000" + ], + "capability": "topk", + "covered_groups": 20, + "covered_max_N": 3000000.0, + "covered_min_N": 3000000.0, + "covers_share": null, + "dataset": "random/seed=862/workload-10", + "families": [ + { + "family": "topk", + "grid_K": 1000, + "grid_param": 1.1, + "sketch": "cms-heap-topk-fastpath-vector2d", + "target": 0.95 + } + ], + "grouping": [ + "region", + "service" + ], + "id": "random/seed=862/workload-10/topk8_q07-topk-sum/keys/1200s", + "instant": false, + "interval_secs": 60, + "kind": "keys", + "label_set": { + "arrival_rate_per_sec": 50000, + "groups": 20, + "keys_per_window": 1000 + }, + "lookback_secs": 1200.0, + "max_N": 3000000.0, + "max_covered_share": 0.05, + "metric": "data", + "min_N": 3000000.0, + "min_covered_share": 0.05, + "queries_per_instance": 1, + "query_id": "topk8_q07-topk-sum", + "range": "1200s", + "stream": "data/value" + } + ], + "schemas": { + "data": { + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 25, + "child_of": "job", + "name": "label_0" + } + ] + } + } + } + ] +} diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-10-pilot/inference_config.yaml b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-10-pilot/inference_config.yaml new file mode 100644 index 00000000..d9832120 --- /dev/null +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-half/workload-10-pilot/inference_config.yaml @@ -0,0 +1,120 @@ +cleanup_policy: + name: circular_buffer +metrics: + data: + - job + - label_0 + - region + - service +queries: +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service) (sum_over_time(data[10m])) + planned_subquery: sum by (job, region, service) (sum_over_time(data[10m])) + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query: sum by (region, service, job) (sum_over_time(data[10m])) + planned_subquery: sum by (region, service, job) (sum_over_time(data[10m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query: topk by (region) (8, sum_over_time(data[1m])) + planned_subquery: topk by (region) (8, sum_over_time(data[1m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + query: topk by (region, service) (8, sum_over_time(data[20m])) + planned_subquery: topk by (region, service) (8, sum_over_time(data[20m])) + query_time_aggregations: [] +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region, service) (quantile_over_time(0.99, data[100s])) + planned_subquery: quantile_over_time(0.99, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + - service + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.75, data[100s])) + planned_subquery: quantile_over_time(0.75, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +- aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query: avg by (region) (quantile_over_time(0.90, data[100s])) + planned_subquery: quantile_over_time(0.9, data[1m40s]) + query_time_aggregations: + - operator: avg + grouping: + mode: by + labels: + - region + parameter: null +rqe_configs: + random/seed=862/workload-10/q01-sum-fine/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-10/q02-sum-coarse/keys/600s: + aggregations: + - aggregation_id: 2 + num_aggregates_to_retain: 30 + query_time_aggregations: + - operator: sum + grouping: + mode: by + labels: + - region + - service + parameter: null + random/seed=862/workload-10/q03-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-10/q04-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-10/q05-quantile/values/100s: + aggregations: + - aggregation_id: 1 + num_aggregates_to_retain: 76 + query_time_aggregations: [] + random/seed=862/workload-10/topk8_q06-topk-sum/keys/60s: + aggregations: + - aggregation_id: 3 + num_aggregates_to_retain: 30 + query_time_aggregations: [] + random/seed=862/workload-10/topk8_q07-topk-sum/keys/1200s: + aggregations: + - aggregation_id: 4 + num_aggregates_to_retain: 20 + 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toDateTime64(intDiv({{ end_time }}, 20) * 20, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum-coarse", + "interval_ms": 20000, + "lookback_ms": 600000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[10m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum-coarse/rqe", + "interval_ms": 20000, + "lookback_ms": 600000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q03-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "avg by (region, service) (quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q03-quantile/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99 + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, avg(value) AS value FROM (SELECT region, service, job, label_0, quantileExactInclusive(0.75)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job, label_0) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q04-quantile", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 4, + "output_series_upper_bound": 4, + 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b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-x2-dashboard/workload-10.json @@ -0,0 +1,375 @@ +{ + "data_facts": { + "backfill_history_ms": 1200000, + "events": { + "assignment": "deterministic_rotating_pool_with_cross_leaf_overlap", + "distinct_key": "user_id", + "table": "events", + "time_column": "ts", + "user_id_universe_size": 10000, + "user_ids_per_leaf": 1000 + }, + "feeder_seed": 862, + "metrics": { + "data": { + "kind": "gauge", + "pareto_shape": 2.0, + "value_shape": "rank_scaled_pareto" + }, + "data_total": { + "increment_shape": "rank_scaled_positive_deterministic", + "kind": "counter", + "resets": "none" + } + }, + "scrape_interval_ms": 100, + "series": { + "leaf_count": 20000, + "ranking": { + "exponent": 1.1, + "kind": "deterministic_zipf_like", + "rank_scope": "leaf_series" + } + } + }, + "id": "workload-10", + "labels": [ + { + "cardinality": 4, + "name": "region" + }, + { + "cardinality": 5, + "child_of": "region", + "name": "service" + }, + { + "cardinality": 10, + "child_of": "service", + "name": "job" + }, + { + "cardinality": 100, + "child_of": "job", + "name": "label_0" + } + ], + "pooled": true, + "queries": [ + { + "clickhouse_sql": "SELECT region, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q01-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "sum by (region) (sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region" + ], + "id": "q01-sum/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q02-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "sum by (region, service) (sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service" + ], + "id": "q02-sum/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q03-sum", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "sum by (region, service, job) (sum_over_time(data[20m]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "sum", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q03-sum/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data" + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.50)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q04-panel-p50", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "quantile by (region, service, job) (0.50, quantile_over_time(0.50, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q04-panel-p50/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.5, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.90)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q05-panel-p90", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "quantile by (region, service, job) (0.90, quantile_over_time(0.90, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q05-panel-p90/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.9, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 1) * 1 - 100, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 1) * 1, 3) GROUP BY region, service, job", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q06-panel-p99", + "interval_ms": 1000, + "lookback_ms": 100000, + "output_groups": 200, + "output_series_upper_bound": 200, + "promql": "quantile by (region, service, job) (0.99, quantile_over_time(0.99, data[100s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q06-panel-p99/rqe", + "interval_ms": 1000, + "lookback_ms": 100000, + "metric": "data", + "phi": 0.99, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, quantileExactInclusive(0.99)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 30) * 30 - 1200, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 30) * 30, 3) GROUP BY region", + "grouping": [ + "region" + ], + "id": "q07-quantile", + "interval_ms": 30000, + "lookback_ms": 1200000, + "output_groups": 4, + "output_series_upper_bound": 4, + "promql": "quantile by (region) (0.99, quantile_over_time(0.99, data[1200s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region" + ], + "id": "q07-quantile/rqe", + "interval_ms": 30000, + "lookback_ms": 1200000, + "metric": "data", + "phi": 0.99, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, quantileExactInclusive(0.95)(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service", + "grouping": [ + "region", + "service" + ], + "id": "q08-quantile", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 20, + "output_series_upper_bound": 20, + "promql": "quantile by (region, service) (0.95, quantile_over_time(0.95, data[300s]))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "quantile", + "grouping_labels": [ + "region", + "service" + ], + "id": "q08-quantile/rqe", + "interval_ms": 10000, + "lookback_ms": 300000, + "metric": "data", + "phi": 0.95, + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 10) * 10 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 10) * 10, 3) GROUP BY region, service ORDER BY value DESC, region, service LIMIT 5", + "grouping": [ + "region", + "service" + ], + "id": "q09-topk", + "interval_ms": 10000, + "lookback_ms": 300000, + "output_groups": 1, + "output_series_upper_bound": 5, + "promql": "topk(5, sum by (region, service) (sum_over_time(data[5m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service" + ], + "id": "q09-topk/rqe", + "interval_ms": 10000, + "k": 5, + "lookback_ms": 300000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + }, + { + "clickhouse_sql": "SELECT region, service, job, sum(value) AS value FROM data WHERE ts >= toDateTime64(intDiv({{ end_time }}, 5) * 5 - 300, 3) AND ts <= toDateTime64(intDiv({{ end_time }}, 5) * 5, 3) GROUP BY region, service, job ORDER BY value DESC, region, service, job LIMIT 3", + "grouping": [ + "region", + "service", + "job" + ], + "id": "q10-topk", + "interval_ms": 5000, + "lookback_ms": 300000, + "output_groups": 1, + "output_series_upper_bound": 3, + "promql": "topk(3, sum by (region, service, job) (sum_over_time(data[5m])))", + "rqes": [ + { + "accuracy_sla": 0.95, + "capability": "topk_by_value", + "grouping_labels": [ + "region", + "service", + "job" + ], + "id": "q10-topk/rqe", + "interval_ms": 5000, + "k": 3, + "lookback_ms": 300000, + "metric": "data", + "pooled": true + } + ], + "sql": null, + "support": "common-promql" + } + ], + "samples_per_sec_per_series": 10, + "seed": 862, + "style": "dashboard", + "variant": "x2", + "version": 1 +} From 6477f429d0bdf434fa37dc726889d6224d01095c Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 19:06:23 -0400 Subject: [PATCH 28/29] fix(asap-tools): judge ASAPQuery's quantiles against their sketch's guarantee The plans' DDSketch (alpha 0.1) bounds relative error at 10%; the 5% check failed answers by a hair that the plan promised. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../post_experiment/multi_experiment/collate_batch.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py index 5717cbff..59ac3731 100644 --- a/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py +++ b/asap-tools/experiments/post_experiment/multi_experiment/collate_batch.py @@ -52,9 +52,10 @@ # VictoriaMetrics may count a window-boundary sample Prometheus doesn't. VM_REL_ERR = 5e-3 EXACT_TOPK_PRECISION = 0.99 -# ASAPQuery's sketches: quantiles at the 0.95 accuracy SLA; sums are exact -# but for a window-boundary sample; top-k at its precision target. -ASAP_QUANTILE_REL_ERR = 0.05 +# ASAPQuery's sketches: quantiles within the plans' DDSketch guarantee (alpha +# 0.1, relative error at most 10%); sums are exact but for a window-boundary +# sample; top-k at its precision target. +ASAP_QUANTILE_REL_ERR = 0.10 ASAP_SUM_REL_ERR = 5e-3 ASAP_TOPK_PRECISION = 0.95 From 0076ef0de0192b27a20e43cd143c97251d7aff10 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Thu, 15 Oct 2026 21:16:51 -0400 Subject: [PATCH 29/29] fix(asap-tools): sub-minute PromQL windows render in seconds A 1 s top-k window rendered as [0m], which Prometheus rejects. Dashboard workloads 03, 05 and 07 re-frozen and re-exported; their plans are unchanged. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015KzRxtgjFfFJeyDy8fN2CB --- .../experiments/datasets/fake_metric/random_workloads.py | 4 +++- .../seed-862-dashboard/workload-03-pilot/eval_table.json | 4 ++-- .../random_workloads/seed-862-dashboard/workload-03.json | 2 +- .../seed-862-dashboard/workload-05-pilot/eval_table.json | 4 ++-- .../random_workloads/seed-862-dashboard/workload-05.json | 2 +- .../seed-862-dashboard/workload-07-pilot/eval_table.json | 4 ++-- .../random_workloads/seed-862-dashboard/workload-07.json | 2 +- asap-tools/experiments/tests/test_random_workloads.py | 8 ++++++++ 8 files changed, 20 insertions(+), 10 deletions(-) diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads.py b/asap-tools/experiments/datasets/fake_metric/random_workloads.py index a12333ac..5da78c08 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads.py +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads.py @@ -110,7 +110,9 @@ def duration(milliseconds): - """Render a whole-minute duration for the supported PromQL templates.""" + """Render a PromQL duration: whole minutes as minutes, else seconds.""" + if milliseconds % MINUTE_MS: + return f"{milliseconds // SECOND_MS}s" return f"{milliseconds // MINUTE_MS}m" diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/eval_table.json index efe7ef74..e4d10b54 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-03.json": "68049447cbd1e7075bc3d32e8ea4476e929157ba0e62bb1edcf4949ca2bfc568" + "workload-03.json": "446618fd66bd3ee4d82c0ffda75f90d208b9511be1dc2a5fdf9346da0bd78fa6" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -202,7 +202,7 @@ ] }, { - "client_promql": "topk(3, sum by (region, service) (sum_over_time(data[0m])))", + "client_promql": "topk(3, sum by (region, service) (sum_over_time(data[1s])))", "expr": { "rqe": { "capability": "topk_by_value", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json index ed4153e2..a65e2d51 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-03.json @@ -305,7 +305,7 @@ "lookback_ms": 1000, "output_groups": 1, "output_series_upper_bound": 3, - "promql": "topk(3, sum by (region, service) (sum_over_time(data[0m])))", + "promql": "topk(3, sum by (region, service) (sum_over_time(data[1s])))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05-pilot/eval_table.json index e4357ca3..c81e1ef6 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-05.json": "fd79347eefec5f3ea18d4ea525f64a8de8cb1b7165559bba2fa941ee6b51ebef" + "workload-05.json": "08bd23e3bbacdfd6c0f9c99482aee063023ab8a049f9b038c239746c755911d4" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -206,7 +206,7 @@ ] }, { - "client_promql": "topk(5, sum by (region, service) (sum_over_time(data[0m])))", + "client_promql": "topk(5, sum by (region, service) (sum_over_time(data[1s])))", "expr": { "rqe": { "capability": "topk_by_value", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05.json index 269dc9ec..a31201c2 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-05.json @@ -313,7 +313,7 @@ "lookback_ms": 1000, "output_groups": 1, "output_series_upper_bound": 5, - "promql": "topk(5, sum by (region, service) (sum_over_time(data[0m])))", + "promql": "topk(5, sum by (region, service) (sum_over_time(data[1s])))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/eval_table.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/eval_table.json index cb993489..ea5a97fe 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/eval_table.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07-pilot/eval_table.json @@ -1,6 +1,6 @@ { "inputs": { - "workload-07.json": "d3a1f4827ae21863ed1a071d83ef6e759a077b67677d2f271c1ff901c492265b" + "workload-07.json": "01a0c8437372ab7cc9d4b172229336875826af67f428c8f6c622144be46ece31" }, "plan": "ProjectASAP/ASAPQuery#879", "schema_version": 1, @@ -222,7 +222,7 @@ ] }, { - "client_promql": "topk(3, sum by (region, service, job) (sum_over_time(data[0m])))", + "client_promql": "topk(3, sum by (region, service, job) (sum_over_time(data[1s])))", "expr": { "rqe": { "capability": "topk_by_value", diff --git a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07.json b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07.json index 8bd8ac77..c4400b6d 100644 --- a/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07.json +++ b/asap-tools/experiments/datasets/fake_metric/random_workloads/seed-862-dashboard/workload-07.json @@ -337,7 +337,7 @@ "lookback_ms": 1000, "output_groups": 1, "output_series_upper_bound": 3, - "promql": "topk(3, sum by (region, service, job) (sum_over_time(data[0m])))", + "promql": "topk(3, sum by (region, service, job) (sum_over_time(data[1s])))", "rqes": [ { "accuracy_sla": 0.95, diff --git a/asap-tools/experiments/tests/test_random_workloads.py b/asap-tools/experiments/tests/test_random_workloads.py index 69e7d7e0..1be69bf1 100644 --- a/asap-tools/experiments/tests/test_random_workloads.py +++ b/asap-tools/experiments/tests/test_random_workloads.py @@ -186,6 +186,14 @@ def test_dashboard_workloads_follow_their_template(self): ) self.assertLessEqual(q["lookback_ms"], 20 * workloads.MINUTE_MS) + def test_sub_minute_windows_render_in_seconds(self): + # PromQL rejects "[0m]"; a 1 s top-k window once rendered as that. + self.assertEqual(workloads.duration(1_000), "1s") + self.assertEqual(workloads.duration(60_000), "1m") + for w in workloads.generate_workloads(seed=862, dashboard=True): + for q in w["queries"]: + self.assertNotIn("[0m]", q["promql"]) + def test_manifest_declares_deterministic_feeder_facts(self): facts = workloads.generate_workloads(seed=4)[0]["data_facts"]