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Let class masking run over whole projects without stalling or filling the database - #1404
Class masking over a whole project could not finish. Jobs were cancelled as stalled before they reported any progress, and runs that did get going wrote so much array data that they filled a production database disk. This change makes wide masking runs report progress within seconds and keep memory flat, and it stops each masked row from storing a copy of data the source row already holds, so masking can be run from the interface over whole projects.
List of Changes
A wide masking run reports progress within seconds instead of being cancelled as stalled.
Memory stays flat however wide the scope is; the scope is no longer loaded whole into memory and Redis.
Masked rows no longer store a copy of the source row's logits, which roughly halves what each run writes.
A failed batch read no longer leaves the query cache switched off for the rest of the worker.
Masking no longer fetches the scores column it never reads.
A batch size of zero or less is refused with a clear error.
Review focus
Masked rows with logits = null. The source row keeps its logits (reachable through applied_to), and scores still holds the masked result. top_n() already handles a missing logits, and the interface does not show logits. Please confirm nothing else expects them on masked rows.
Unit tests (ami.ml.post_processing, 79 tests, all pass on an isolated test stack, head 79273c28; later commits are docs only). New in this round: masked rows store no logits while the source keeps them; the query cache is restored after a batch read raises.
Local measurements on 1,000 and 4,530 real classifications from a 29,176-class classifier (tables below). Database writes were replaced by stand-ins in these runs.
6,323 classifications: succeeded in 756 s. First progress after 30 s, longest gap between progress updates 35 s. 4,083 rows masked, all with logits empty; 1,483 occurrences updated.
11 classifications: source rows keep their logits and are demoted; other predictions on masked rows are correct.
Peak memory of the whole worker container was 6.39 GiB. That figure is not isolated to the task, so it does not yet show memory staying flat.
Production, indirectly. A one-off script using the same batched, cache-free read processed about 119,000 masked rows on a production deployment with steady progress. It stopped when the database disk filled with copied arrays, which is what change 3 prevents. That script is not this PR's code path.
Lint: black, isort and flake8 pass. No model change, so no migration.
Not yet tested:
Memory used by the masking task on its own, separate from the rest of the worker.
Write throughput at scale. The timing runs did not include writing results.
Cause and measurements
A production run over 70,279 classifications reported its scope size, then nothing for 16.8 minutes, and was revoked with classifications_checked: 0. An earlier version of this description blamed a database cursor; that was wrong. The measured cause is the query cache (django-cachalot): it calls list() on every .iterator() result so it can store it, so the whole scope is read, parsed and written to Redis before the loop sees a row.
Read-only timing, local copy of production data, batch size 200, 8 GB container:
How it reads
Query cache
Rows
First batch
Total
Peak memory
Left in Redis
.iterator()
on
1,000
23.0 s
23.9 s
3.09 GB
513 MB
.iterator()
on
2,000
45.2 s
46.5 s
5.78 GB
0 (write failed)
id batches
off
4,530
2.18 s
45.3 s
0.67 GB
0
Masking function end to end, writes stubbed:
Version
Rows
First progress
Longest gap
Peak memory
main
4,530
killed at 8 GB
this branch
4,530
12.3 s
2.76 s
0.80 GB
Decisions and what comes next
Decided in this PR:
Masked rows drop logits but keep scores. The logits are an exact copy of the source row's. The masked scores are the only stored record of the mask until scores can be derived from logits.
Pipeline results still store scores. Stopping that needs a fallback that derives them from logits, which is a separate change.
Fix the read pattern here, generalise later. Only class masking uses the batched read for now.
Stop re-runs from adding a second classifier label to the same detection, and clean up existing duplicates. Until then, one masking run can write more than one masked row for such a detection.
A shared batched-read helper for exports and the small size filter, which use the same .iterator() pattern.
Move full logits out of the database into result files, keeping a short top-k list on each classification.
Class masking read its whole scope through one `.iterator()` cursor. `chunk_size`
bounds memory, not latency: the time to the first row scales with the size of the
scope, measured at 4.65s for 200 rows and 61.09s for 2,798 on rows carrying a
29,176-class classifier's `logits` and `scores` (~140 kB each). The first progress
report comes after that first row, so a large run crossed the ten-minute stalled-job
cutoff and was revoked having reported nothing — production job 2834 was revoked with
`classifications_checked: 0` after 16.8 minutes on a 70,279-row scope.
Resolves the scope to a list of ids once and reads it one batch per query. Also
defers `scores`, which masking never reads and which is as large as `logits`, and
selects `detection` and its `occurrence` with the batch instead of two queries per
masked row. Measured on a 2,798-row scope: first batch 3.22s instead of 61.58s, and
91 rows/s instead of the ~3.5 rows/s production was achieving.
`total` now comes from the resolved id list rather than a separate `.count()`, so the
count and the iteration can no longer disagree — the source of log lines such as
"Re-scored 3912 of 3643 classifications".
This stops the reaping; it does not make a large run fast. The run is write-bound,
and the remaining costs are recorded in
docs/claude/planning/class-masking-batch-fetch-latency.md.
Co-Authored-By: Claude <noreply@anthropic.com>
…planning doc [skip ci]
Repo is public; generalize the specific production project ID, job ID,
and deployment name that identified this branch's motivating incident.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016pXcpCTxzSnJ32zA6T5RBP
The class masking flow now resolves scope IDs before processing, reads classifications in fixed-size batches, defers unused scores, and eagerly loads related records. New tests validate the query shape. Documentation records the latency findings and query pattern.
Changes: Class masking batch fetching
Layer / File(s)
Summary
Latency analysis and batching design docs/claude/planning/class-masking-batch-fetch-latency.md
The planning document records iterator fetch latency, the primary-key batching design, measured alternatives, and excluded follow-ups.
The function resolves scope IDs, derives total from them, fetches fixed-size batches with defer("scores") and select_related(...), and flushes each batch.
sequenceDiagram
participant MaskingFunction
participant Database
participant BatchTransaction
MaskingFunction->>Database: Resolve scope primary keys
loop Each batch
MaskingFunction->>Database: Fetch classifications by primary keys
MaskingFunction->>BatchTransaction: Apply masking and flush batch
BatchTransaction-->>MaskingFunction: Batch committed
end
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Merge Risk: Low · up to 79273
The batched class-masking read path looks sound and safe to merge. One documentation fix remains: the query-patterns guide should recommend the exception-safe no_query_cache() helper instead of cachalot_disabled().
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 62.50% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
Status
Explanation
Title check
The title clearly and concisely describes the primary change: preventing whole-project class-masking runs from stalling or exhausting database storage.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Description check
The description follows the template well. It has a Summary, a List of Changes, Review focus, and a Testing section that covers unit tests, local measurements, end-to-end runs, and lint. It also has D…
Full details: Description check
Explanation
The description follows the template well. It has a Summary, a List of Changes, Review focus, and a Testing section that covers unit tests, local measurements, end-to-end runs, and lint. It also has Decisions and Next steps, and a Related line listing #1376, #1368, #1377, #1513, #1519. It does not use the template's exact headings: it has no 'Related Issues' section with a Closes/Fixes/Relates keyword, no 'Deployment Notes' section (it says there is no migration, which partly covers this), and the checklist is missing. The core content is present and detailed, so the check passes. Two gaps are worth noting. The description says the query cache helper is byte-identical to the one in #1519, which is useful for merge ordering. The author also lists untested items (memory of the task alone, write throughput at scale), which is honest and appropriate.
Full details: Docstring Coverage
Explanation
Docstring coverage is 62.50% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 8 functions across 3 files. (2 skipped: 2 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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🟢 Approval recommended
The batching change is localized, aligns with the stated production failure mode, and is backed by new tests that pin the intended query/heartbeat behavior.
Pull request overview
This PR prevents wide class-masking jobs from being revoked as “stalled” before they can emit their first progress heartbeat by changing the masking scope read pattern to batch-by-PK fetches (and avoiding unnecessary column fetches / per-row relationship lookups), plus adds targeted regression tests and documents the query anti-pattern.
Changes:
Replace cursor-backed QuerySet.iterator() scope reads with: (1) pre-resolving scope PKs, then (2) fetching classifications in fixed-size PK batches.
Reduce per-batch payload and query count by deferring the unused scores column and select_related-loading detection / detection__occurrence.
Add tests to pin the intended query shape (batched reads, deferred column, no per-row detection lookups) and document the iterator latency pitfall.
File summaries
File
Description
docs/claude/reference/query-patterns.md
Adds a documented anti-pattern and the recommended PK-batching approach for wide-row querysets where time-to-first-row matters.
New planning write-up capturing measurements, root cause analysis, and the chosen fix approach.
ami/ml/post_processing/class_masking.py
Implements PK list resolution + per-batch fetch with defer("scores") and select_related(...) to ensure timely progress heartbeats and avoid per-row queries.
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Actionable comments posted: 3
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
In `@ami/ml/post_processing/class_masking.py`:
- Line 133: Validate that batch_size is greater than zero before invoking
on_setup or entering the batching loop in the surrounding masking function;
reject non-positive values explicitly so the function cannot report
classifications as checked without processing them.
In `@ami/ml/post_processing/tests/test_class_masking.py`:
- Line 702: Update the SQL matcher in the masking test to reject lazy per-row
lookups for both main_detection and main_occurrence, while preserving the
existing detection-ID query check.
In `@docs/claude/planning/class-masking-batch-fetch-latency.md`:
- Line 8: Update the opening text in class-masking-batch-fetch-latency.md so the
leading `#1376` reference no longer triggers markdownlint MD018, using a
descriptive prefix such as “Issue” or escaping the hash while preserving the
issue reference.
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Reviewing files that changed from the base of the PR and between 498e62f and 48d1b64.
Resolves the class_masking.py conflict by keeping the batched scope read from
this branch and carrying over main's additions to the loop: the job recorded on
new classifications (#1471) and the per-occurrence algorithm results written in
each flush (#1461).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
…king batches
The scope built by ClassMaskingTask already selects detection and its
occurrence (#1368), so the batch read no longer repeats it. The regression test
now runs against the task's own scope and also rejects lazy occurrence loads.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
A batch size of zero or less skipped the loop and reported every
classification as checked while masking none.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
django-cachalot calls list() on a chunked fetch so it can store the result,
which is what made .iterator() read, parse and pickle the whole scope before
yielding its first row. Measured on a local production copy (29,176-class
logits): first row after 12.2 s at 500 rows and 23.0 s at 1,000, peak memory
about 2.8 MB per row, and a 4,530-row run was killed at an 8 GB cap.
The batched read avoids that, but each batch was still cached, leaving about
1.3 GB in Redis for 4,530 rows that nothing reads again. Batches are now read
with the cache bypassed; writes still invalidate cached tables.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
… cause [skip ci]
Replaces the unconfirmed WITH HOLD cursor theory with the measured mechanism
and the before/after read-path benchmark.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
…the query cache on error
Masked classifications no longer store a copy of the source row's logits.
The source row keeps them, reachable through applied_to, and the copies
filled a production database disk during a wide masking run.
The per-batch read now uses no_query_cache(), which restores the query
cache state even when the read raises. cachalot_disabled() alone leaves
the cache off for the rest of the worker thread after an exception. The
helper is byte-identical to the one in #1519 so the two merge cleanly.
Also rewrites the batching test docstring to name the actual cause (the
query cache turning .iterator() into a whole-scope read).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
mihow
changed the title
Stop wide class-masking jobs from being cancelled before they finish reading their scope
Let class masking run over whole projects without stalling or filling the database
Oct 28, 2026
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @docs/claude/reference/query-patterns.md:
- Line 335: Update the batch-read pattern in the query-pattern documentation to
use no_query_cache() instead of cachalot_disabled(), so the prior cache state is
restored if a batch read raises; leave the surrounding guidance unchanged.
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…e [skip ci]
The investigation lives in the PR description. The reference section keeps
the rule, the reason and the pattern.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01G3oXx6YMU9J6KAjTdkSE7R
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class-maskingLimiting predictions to a species list (class masking) and running it from the app
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mihow
added
the
class-masking
Summary
Class masking over a whole project could not finish. Jobs were cancelled as stalled before they reported any progress, and runs that did get going wrote so much array data that they filled a production database disk. This change makes wide masking runs report progress within seconds and keep memory flat, and it stops each masked row from storing a copy of data the source row already holds, so masking can be run from the interface over whole projects.
List of Changes
scorescolumn it never reads.Review focus
logits = null. The source row keeps its logits (reachable throughapplied_to), andscoresstill holds the masked result.top_n()already handles a missinglogits, and the interface does not show logits. Please confirm nothing else expects them on masked rows.ami/utils/query_cache.pyis byte-identical to the helper in Keep the query cache on in workers after a pipeline result task fails #1519, so whichever merges second has no conflict.Testing
Tested:
ami.ml.post_processing, 79 tests, all pass on an isolated test stack, head79273c28; later commits are docs only). New in this round: masked rows store no logits while the source keeps them; the query cache is restored after a batch read raises.logitsempty; 1,483 occurrences updated.Not yet tested:
Cause and measurements
A production run over 70,279 classifications reported its scope size, then nothing for 16.8 minutes, and was revoked with
classifications_checked: 0. An earlier version of this description blamed a database cursor; that was wrong. The measured cause is the query cache (django-cachalot): it callslist()on every.iterator()result so it can store it, so the whole scope is read, parsed and written to Redis before the loop sees a row.Read-only timing, local copy of production data, batch size 200, 8 GB container:
.iterator().iterator()Masking function end to end, writes stubbed:
Decisions and what comes next
Decided in this PR:
logitsbut keepscores. The logits are an exact copy of the source row's. The maskedscoresare the only stored record of the mask until scores can be derived from logits.scores. Stopping that needs a fallback that derives them from logits, which is a separate change.Planned next, in order:
scoresthat equal softmax(logits) and derive them on read; masking then reads probabilities from storedscores, or fromlogitswhenscoresare not stored. Tested end to end with this PR on a development server, in both sync and async processing modes..iterator()pattern.Follow-up to #1376. Related: #1368, #1377, #1513, #1519.
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