H-6664: Introduce Python monorepo infrastructure and formalize python repo-chores

[indietyp]

🌟 What is the purpose of this PR?

This PR establishes a first-class Python workspace for the monorepo, built on a single uv workspace rooted at a new pyproject.toml. All Python packages share one lockfile (uv.lock), one virtual environment, and workspace-level tooling: ruff for linting and formatting, ty for type checking, tach for import-boundary enforcement, and pytest for tests.

A new Python package, libs/@local/repo-chores/python (hash-repo-chores), acts as the Python counterpart to the existing Rust sync-turborepo tooling. It provides a repo-chores CLI with two subcommands:

• sync — enforces workspace invariants: explicit uv workspace membership, uniform requires-python bounds, shared ruff/pytest dev pins, and per-member package.json turbo wiring (@python/<name> with managed lint:ruff, fix:ruff, and test:unit scripts).
• prune — extends the existing turbo prune logic (previously a standalone script at .github/actions/prune-repository/prune.py) to also stub out pruned uv workspace members, keeping the pruned output a valid uv workspace.

CI gains a lint:python step that runs uv sync --locked (lockfile stability), repo-chores sync --check (workspace drift), ruff, ty, and tach. A fix:python step applies ruff autofixes and refreshes the lock. A sync:python mise task regenerates all managed wiring after adding, removing, or renaming a Python package. The lefthook pre-commit hook gains a Python entry that runs ruff on staged .py files.

🔍 What does this change?

• Adds a root pyproject.toml and uv.lock establishing the uv workspace, pinned to Python 3.14, with ruff, ty, tach, and pytest configured workspace-wide.
• Introduces libs/@local/repo-chores/python (hash-repo-chores) with modules for workspace discovery (workspace.py), TOML in-place editing (toml_edit.py), workspace sync (sync.py), turbo pruning (prune.py), and a CLI entry point (cli.py).
• Moves .github/actions/prune-repository/prune.py into libs/@local/repo-chores/python/repo_chores/prune.py, refactoring it to be path-aware, stdlib-only, and callable both directly and via repo-chores prune. Adds stub_missing_uv_members so pruned uv workspace members are stubbed back in alongside Cargo stubs.
• Updates the prune-repository GitHub Action to invoke the script from its new location and to copy pyproject.toml and uv.lock into the pruned output.
• Adds mise tasks lint:python, fix:python, and sync:python wrapping the corresponding uv/repo-chores invocations.
• Adds lint:python and fix:python yarn scripts in the root package.json delegating to the mise tasks.
• Adds lint:ruff and fix:ruff turbo pipeline entries so per-package ruff runs participate in the turbo graph.
• Extends the CI lint workflow to detect and run lint:ruff per package and to run yarn lint:python globally.
• Adds .ruff_cache to .gitignore and .dockerignore.
• Adds a lefthook pre-commit hook that runs ruff check and format on staged Python files.
• Bumps uv from 0.11.18 to 0.11.28 with updated checksums for all platforms.
• Documents the Python workspace setup in README.md and AGENTS.md, including how to add packages, run checks, and interpret the sync tooling.
• Adds a full test suite for hash-repo-chores covering prune logic, workspace discovery, sync check/fix/idempotency, and assemble_package_json.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

• does not modify any publishable blocks or libraries, or modifications do not need publishing

📜 Does this require a change to the docs?

The changes in this PR:

• require changes to docs which are made as part of this PR

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

• affected the execution graph, and the turbo.json's have been updated to reflect this

🛡 What tests cover this?

• New pytest suite in libs/@local/repo-chores/python/tests/ covering test_prune.py, test_sync.py, and test_workspace.py with a shared conftest.py fixture repository.

❓ How to test this?

1. Run mise install to provision uv 0.11.28.
2. Run uv sync to create the virtual environment.
3. Run mise run lint:python and confirm all checks pass.
4. Run mise run fix:python and confirm ruff autofixes apply cleanly.
5. Run mise run sync:python and confirm no drift is reported on a second mise run lint:python.
6. Run uv run --frozen pytest libs/@local/repo-chores/python/tests and confirm all tests pass.

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[cursor]

PR Summary

Medium Risk
Touches CI prune paths and new enforced workspace invariants; failures block merges but do not change runtime app behavior.

Overview
Introduces a root uv workspace (pyproject.toml, uv.lock, Python 3.14) with shared ruff, ty, tach, and pytest configuration, plus mise tasks for lint:python, fix:python, and sync:python wired through root yarn scripts.

Adds hash-repo-chores (libs/@local/repo-chores/python) with sync (membership, requires-python, dev pins, @python/* turbo package.json wiring), lint (bounded registry deps), and prune (moved from the GitHub Action; stubs missing Cargo and uv workspace members after turbo prune). CI runs global yarn lint:python, per-package lint:ruff via turbo, and the prune action copies pyproject.toml / uv.lock. Lefthook runs ruff on staged .py files; uv bumps to 0.11.28.

^{Reviewed by Cursor Bugbot for commit 8a662f1. Bugbot is set up for automated code reviews on this repo. Configure here.}

[indietyp]

• H-6664: Introduce Python monorepo infrastructure and formalize python repo-chores #9039 👈 (View in Graphite)
• main

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[codspeed-hq]

Merging this PR will degrade performance by 15.38%

❌ 2 regressed benchmarks
✅ 96 untouched benchmarks

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Benchmark	BASE	HEAD	Efficiency
❌	bit_matrix/dense/iter_row[64]	140.8 ns	170 ns	-17.16%
❌	bit_matrix/dense/iter_row[200]	185.8 ns	215 ns	-13.57%

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

---

_{Comparing bm/h-6664-python-monorepo-infrastructure (8a662f1) with main (0cb749d)¹}

Footnotes

1. No successful run was found on main (0465623) during the generation of this report, so 0cb749d was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

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Copilot was unable to review this pull request because the user who requested the review has reached their quota limit.

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[cursor]

Cursor Bugbot has reviewed your changes and found 1 potential issue.

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^{Reviewed by Cursor Bugbot for commit 8a662f1. Configure here.}

[cursor]

Sync crashes without workspaces.packages

Medium Severity

When repo-chores sync applies a fix for a Python package not covered by Yarn workspace globs, it assigns to data["workspaces"]["packages"] without ensuring those nested keys exist. Coverage checks use safe .get() access, so apply mode can raise KeyError instead of updating package.json.

 

^{Reviewed by Cursor Bugbot for commit 8a662f1. Configure here.}

[indietyp]

If they don't exist, then our workspace yarn file is busted and we have larger problems 😅

[TimDiekmann]

❤️

[github-actions]

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$26.7 \mathrm{ms} \pm 209 \mathrm{μs}\left({\color{gray}-3.694 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.37 \mathrm{ms} \pm 22.4 \mathrm{μs}\left({\color{gray}-0.748 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$12.0 \mathrm{ms} \pm 94.7 \mathrm{μs}\left({\color{lightgreen}-7.880 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.1 \mathrm{ms} \pm 398 \mathrm{μs}\left({\color{gray}-2.627 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.0 \mathrm{ms} \pm 139 \mathrm{μs}\left({\color{gray}-0.886 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$23.5 \mathrm{ms} \pm 198 \mathrm{μs}\left({\color{gray}-3.149 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$27.8 \mathrm{ms} \pm 180 \mathrm{μs}\left({\color{gray}-3.213 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.67 \mathrm{ms} \pm 21.5 \mathrm{μs}\left({\color{gray}-1.050 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.2 \mathrm{ms} \pm 127 \mathrm{μs}\left({\color{lightgreen}-7.633 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.76 \mathrm{ms} \pm 26.3 \mathrm{μs}\left({\color{gray}-1.010 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 20.5 \mathrm{μs}\left({\color{gray}1.14 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$3.35 \mathrm{ms} \pm 18.6 \mathrm{μs}\left({\color{gray}0.728 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.16 \mathrm{ms} \pm 31.3 \mathrm{μs}\left({\color{gray}-0.832 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.53 \mathrm{ms} \pm 21.4 \mathrm{μs}\left({\color{gray}-0.430 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$4.13 \mathrm{ms} \pm 30.0 \mathrm{μs}\left({\color{gray}0.204 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.45 \mathrm{ms} \pm 30.3 \mathrm{μs}\left({\color{gray}0.060 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.40 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{gray}-0.173 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.23 \mathrm{ms} \pm 31.1 \mathrm{μs}\left({\color{gray}0.206 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.67 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{gray}-0.394 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.67 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{gray}0.024 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.78 \mathrm{ms} \pm 15.8 \mathrm{μs}\left({\color{gray}1.23 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$3.02 \mathrm{ms} \pm 17.3 \mathrm{μs}\left({\color{gray}-0.332 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.81 \mathrm{ms} \pm 17.5 \mathrm{μs}\left({\color{gray}0.474 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$3.10 \mathrm{ms} \pm 16.7 \mathrm{μs}\left({\color{gray}0.202 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.07 \mathrm{ms} \pm 17.7 \mathrm{μs}\left({\color{gray}-0.184 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.74 \mathrm{ms} \pm 14.3 \mathrm{μs}\left({\color{gray}-0.120 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$2.91 \mathrm{ms} \pm 15.8 \mathrm{μs}\left({\color{gray}-0.041 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.50 \mathrm{ms} \pm 21.8 \mathrm{μs}\left({\color{gray}1.58 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.97 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{gray}-1.310 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$3.25 \mathrm{ms} \pm 17.2 \mathrm{μs}\left({\color{gray}-0.203 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.39 \mathrm{ms} \pm 24.4 \mathrm{μs}\left({\color{gray}-0.863 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{gray}0.161 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.32 \mathrm{ms} \pm 16.4 \mathrm{μs}\left({\color{gray}-0.366 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$32.1 \mathrm{ms} \pm 172 \mathrm{μs}\left({\color{gray}-0.403 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$71.8 \mathrm{ms} \pm 481 \mathrm{μs}\left({\color{gray}2.35 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$35.8 \mathrm{ms} \pm 185 \mathrm{μs}\left({\color{gray}-0.071 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$39.8 \mathrm{ms} \pm 226 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$42.4 \mathrm{ms} \pm 249 \mathrm{μs}\left({\color{gray}4.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$34.4 \mathrm{ms} \pm 192 \mathrm{μs}\left({\color{gray}3.54 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$415 \mathrm{ms} \pm 1.29 \mathrm{ms}\left({\color{gray}0.457 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$89.5 \mathrm{ms} \pm 591 \mathrm{μs}\left({\color{gray}-0.359 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$79.0 \mathrm{ms} \pm 464 \mathrm{μs}\left({\color{gray}1.43 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$268 \mathrm{ms} \pm 850 \mathrm{μs}\left({\color{gray}-1.319 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$10.9 \mathrm{ms} \pm 83.9 \mathrm{μs}\left({\color{gray}2.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$11.0 \mathrm{ms} \pm 70.1 \mathrm{μs}\left({\color{gray}2.72 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$11.2 \mathrm{ms} \pm 63.4 \mathrm{μs}\left({\color{gray}4.11 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$10.9 \mathrm{ms} \pm 55.7 \mathrm{μs}\left({\color{gray}1.48 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$11.0 \mathrm{ms} \pm 68.3 \mathrm{μs}\left({\color{gray}2.76 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$10.8 \mathrm{ms} \pm 53.4 \mathrm{μs}\left({\color{gray}2.09 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$11.2 \mathrm{ms} \pm 101 \mathrm{μs}\left({\color{gray}4.66 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$10.8 \mathrm{ms} \pm 88.5 \mathrm{μs}\left({\color{gray}0.259 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$11.0 \mathrm{ms} \pm 71.4 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$10.9 \mathrm{ms} \pm 63.9 \mathrm{μs}\left({\color{gray}-0.185 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/block/v/1	$$11.5 \mathrm{ms} \pm 77.5 \mathrm{μs}\left({\color{gray}1.48 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$11.5 \mathrm{ms} \pm 77.3 \mathrm{μs}\left({\color{gray}-1.280 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$11.3 \mathrm{ms} \pm 61.9 \mathrm{μs}\left({\color{gray}0.945 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$11.5 \mathrm{ms} \pm 92.8 \mathrm{μs}\left({\color{gray}0.570 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$11.4 \mathrm{ms} \pm 76.6 \mathrm{μs}\left({\color{gray}2.28 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$11.1 \mathrm{ms} \pm 48.5 \mathrm{μs}\left({\color{gray}-0.910 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$11.3 \mathrm{ms} \pm 65.2 \mathrm{μs}\left({\color{gray}0.895 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$11.4 \mathrm{ms} \pm 71.8 \mathrm{μs}\left({\color{gray}-0.895 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$11.2 \mathrm{ms} \pm 58.9 \mathrm{μs}\left({\color{gray}-3.113 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.30 \mathrm{ms} \pm 44.3 \mathrm{μs}\left({\color{gray}0.741 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$58.5 \mathrm{ms} \pm 455 \mathrm{μs}\left({\color{gray}4.40 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$110 \mathrm{ms} \pm 598 \mathrm{μs}\left({\color{gray}3.12 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$65.2 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{gray}3.72 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$74.7 \mathrm{ms} \pm 486 \mathrm{μs}\left({\color{gray}4.60 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$83.4 \mathrm{ms} \pm 494 \mathrm{μs}\left({\color{gray}3.87 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$88.3 \mathrm{ms} \pm 489 \mathrm{μs}\left({\color{gray}2.85 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$45.8 \mathrm{ms} \pm 270 \mathrm{μs}\left({\color{gray}2.68 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$72.0 \mathrm{ms} \pm 323 \mathrm{μs}\left({\color{gray}-2.727 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$51.0 \mathrm{ms} \pm 277 \mathrm{μs}\left({\color{gray}-0.683 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$59.7 \mathrm{ms} \pm 366 \mathrm{μs}\left({\color{gray}-1.286 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$62.1 \mathrm{ms} \pm 428 \mathrm{μs}\left({\color{gray}0.433 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$62.2 \mathrm{ms} \pm 401 \mathrm{μs}\left({\color{gray}-0.208 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$122 \mathrm{ms} \pm 531 \mathrm{μs}\left({\color{gray}-2.321 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$133 \mathrm{ms} \pm 641 \mathrm{μs}\left({\color{gray}-2.947 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$23.5 \mathrm{ms} \pm 144 \mathrm{μs}\left({\color{gray}-3.975 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$529 \mathrm{ms} \pm 1.06 \mathrm{ms}\left({\color{lightgreen}-5.179 \mathrm{\%}}\right) $$	Flame Graph