FE-1581: Share the optimizer's Optuna study logic in a pure Python core library by kube · Pull Request #9514 · hashintel/hash

kube · 2026-09-03T00:21:25Z

Summary

Before this PR, the optimizer service in apps/petrinaut-opt held the whole Optuna study logic inside PetrinautOptimizer: description checks, sampler construction, parameter suggestion and the study.optimize driver, next to FastAPI, OpenTelemetry and the subprocess bindings. Nothing else could run that study logic. An optimizer that runs where the simulations run, in the browser, would have had to copy it.

This PR moves the study logic into a pure Python library, @local/petrinaut-optimizer-core, and makes the service depend on it. Library builds the seeded study, maps parameter descriptors to suggestions, produces trial events and summaries, and drives a study by ask and tell against an async evaluator. It keeps a study alive across segments so a host can stop it and continue it on the same sampler history, and it keeps several trials in flight when asked. No threads, OS or network calls, so the same modules load under Pyodide. Service behaviour is unchanged.

Links

Ticket FE-1581

Changes

Python core library

libs/@local/petrinaut-optimizer-core holds the study logic as one module per concept

description.py parses and checks a describe result, study.py builds the study and suggests values, ask_tell.py runs a study against an async evaluator, pyodide_entry.py adapts JavaScript callbacks.
parse_description accepts the CLI's optimization.describe JSON and enforces the service's rules with the same messages

Sampler set, trial cap, seed sign, duplicate identifiers, finite bounds, log-scale domains and integer log steps.
run_study asks, awaits the evaluator, tells, and reports each trial with the running best

An evaluator result of {"objective": x} completes the trial, {"pruned": reason} prunes it, a non-finite objective raises.
Cancellation is read before each ask and after each evaluate.
run_study continues a study and keeps several trials in flight

trials overrides the description's count and continues the same study, so numbering and sampler history carry on.
Up to parallelism trials run as asyncio tasks and are told in completion order.
Summary counts are cumulative over the study.
Stop tells the trials in flight as failed

No trial stays in Optuna's running state, so a continued study samples from told trials only.
create_study scales the TPE sampler's random start-up to the study

Optuna's default of 10 random trials is most of a short study, so no step was ever proposed from results.
The start-up is now a third of the requested trials, at least 2 and at most 10; an explicit n_startup_trials still wins.
create_study turns on the constant-liar TPE when a study runs trials in parallel

Pending trials count as the worst value seen while their evaluation is out, so parallel asks spread instead of repeating.
Browser entry keeps a study handle across calls

create_browser_study builds the study once and returns a StudyHandle.
run_browser_study runs one segment of trials on the handle and adds them to handle.requested.
release_browser_study drops the study.
A stopped segment drops its untold remainder from handle.requested, so a stop at 4 of 8 followed by 4 more reads 8 of 8.
runtime-lock.json pins the Pyodide, Python, Optuna and colorlog versions the browser installs

A test asserts the pinned Optuna and colorlog versions match the library's own lockfile.

Service

PetrinautOptimizer delegates description parsing, study creation and suggestion to the core library

Its threaded study.optimize driver, trial frames, event pump and telemetry stay as they were.
Service dependency on petrinaut-optimizer-core as an editable path source; Optuna bound to >=4.9,<5

Dockerfile copies the library before uv sync, the deploy workflow's image path filter includes it, turbo codegen inputs include its sources.

Architecture

New layer optimizer-core declared on the package and registered in the architecture config
apps/petrinaut-opt README and AGENTS name the shared library

Test coverage

libs/@local/petrinaut-optimizer-core/tests:

Each description rule, suggest mapping per parameter type, seeded determinism, trial event and summary shapes, ask and tell with objectives, pruned outcomes, non-finite rejection, cancellation at both checkpoints, and lockfile parity.
tests/test_ask_tell.py continuation and parallelism:

Parallelism 1 gives the same sequence as a plain ask and tell loop.
A 10-trial study continued by 5 equals a 15-trial study, while a restart repeats the sampler's start-up draws.
Parallel asks run ahead and are told in completion order, a stop tells the trials in flight as failed and a continuation carries their count, an evaluator error cancels the other tasks.
tests/test_study.py start-up trials:

The scaled count per study size, an explicit count winning, the random sampler untouched, and a 6-trial TPE study whose third proposal depends on the objectives told.
tests/test_pyodide_entry.py:

Continued numbering and cumulative counts on one handle, stop then continue, release, one segment at a time, trial cap across segments, parallelism reaching the sampler, proxy unwrapping, description and outcome rejection.
apps/petrinaut-opt/tests:

Existing service suite; the moved suggest and determinism tests now live in the library.

How to test

cd libs/@local/petrinaut-optimizer-core && uv sync && uv run pytest
cd apps/petrinaut-opt && uv sync && uv run pytest

Expect both suites green and openapi/openapi.json unchanged after yarn codegen

vercel · 2026-09-03T00:21:31Z

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codecov · 2026-09-03T00:26:14Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 60.89%. Comparing base (eea4e1f) to head (52b4835).
⚠️ Report is 17 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #9514   +/-   ##
=======================================
  Coverage   60.89%   60.89%           
=======================================
  Files        1461     1461           
  Lines      146725   146725           
  Branches     6744     6744           
=======================================
  Hits        89343    89343           
  Misses      56264    56264           
  Partials     1118     1118

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codspeed-hq · 2026-09-03T00:30:07Z

Merging this PR will degrade performance by 15.38%

⚠️

6 benchmarks measured no execution time

Nothing ran under measurement, usually because the compiler removed the code under test. These results are not comparable, so they count as unchanged.

Preventing compiler optimizations

⚠️

Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

❌ 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%
⚠️	`as_constant`	< 1 ns	< 1 ns	N/A
⚠️	`constant_equal`	< 1 ns	< 1 ns	N/A
⚠️	`constant_not_equal`	< 1 ns	< 1 ns	N/A
⚠️	`access`	< 1 ns	< 1 ns	N/A
⚠️	`runtime_equal`	< 1 ns	< 1 ns	N/A
⚠️	`runtime_not_equal`	< 1 ns	< 1 ns	N/A

Tip

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

_{Comparing claude/web-optimizer-python-core (52b4835) with main (4854d5f)¹}

No successful run was found on main (757cae9) during the generation of this report, so 4854d5f was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

github-actions · 2026-09-06T01:19:44Z

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	$$27.3 \mathrm{ms} \pm 231 \mathrm{μs}\left({\color{gray}-1.922 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.49 \mathrm{ms} \pm 27.8 \mathrm{μs}\left({\color{gray}1.81 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$12.8 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}1.48 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.2 \mathrm{ms} \pm 405 \mathrm{μs}\left({\color{gray}-2.344 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.2 \mathrm{ms} \pm 170 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$24.4 \mathrm{ms} \pm 206 \mathrm{μs}\left({\color{gray}-0.592 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$28.6 \mathrm{ms} \pm 222 \mathrm{μs}\left({\color{gray}-0.229 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.77 \mathrm{ms} \pm 26.9 \mathrm{μs}\left({\color{gray}1.80 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.5 \mathrm{ms} \pm 104 \mathrm{μs}\left({\color{gray}-1.445 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.86 \mathrm{ms} \pm 32.9 \mathrm{μs}\left({\color{gray}1.09 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{gray}0.418 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$3.42 \mathrm{ms} \pm 21.3 \mathrm{μs}\left({\color{gray}1.42 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.24 \mathrm{ms} \pm 34.4 \mathrm{μs}\left({\color{gray}-0.566 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.60 \mathrm{ms} \pm 25.7 \mathrm{μs}\left({\color{gray}0.977 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$4.24 \mathrm{ms} \pm 33.5 \mathrm{μs}\left({\color{gray}1.29 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.56 \mathrm{ms} \pm 40.3 \mathrm{μs}\left({\color{gray}1.62 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.47 \mathrm{ms} \pm 21.1 \mathrm{μs}\left({\color{gray}1.84 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.19 \mathrm{ms} \pm 34.0 \mathrm{μs}\left({\color{gray}2.16 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.72 \mathrm{ms} \pm 16.6 \mathrm{μs}\left({\color{gray}-1.154 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.69 \mathrm{ms} \pm 20.0 \mathrm{μs}\left({\color{gray}0.570 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.79 \mathrm{ms} \pm 17.6 \mathrm{μs}\left({\color{gray}-0.823 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$3.06 \mathrm{ms} \pm 19.9 \mathrm{μs}\left({\color{gray}-0.725 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.86 \mathrm{ms} \pm 17.7 \mathrm{μs}\left({\color{gray}-0.444 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$3.18 \mathrm{ms} \pm 26.9 \mathrm{μs}\left({\color{gray}0.419 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.08 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}-0.631 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.78 \mathrm{ms} \pm 20.2 \mathrm{μs}\left({\color{gray}0.822 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$2.94 \mathrm{ms} \pm 17.0 \mathrm{μs}\left({\color{gray}-2.026 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.53 \mathrm{ms} \pm 24.5 \mathrm{μs}\left({\color{gray}-0.647 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.07 \mathrm{ms} \pm 18.7 \mathrm{μs}\left({\color{gray}0.837 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$3.32 \mathrm{ms} \pm 22.3 \mathrm{μs}\left({\color{gray}0.215 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.44 \mathrm{ms} \pm 18.8 \mathrm{μs}\left({\color{gray}-1.880 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.06 \mathrm{ms} \pm 21.9 \mathrm{μs}\left({\color{gray}1.22 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.39 \mathrm{ms} \pm 22.7 \mathrm{μs}\left({\color{gray}1.47 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$31.7 \mathrm{ms} \pm 203 \mathrm{μs}\left({\color{gray}0.286 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$70.4 \mathrm{ms} \pm 466 \mathrm{μs}\left({\color{gray}-0.700 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$35.1 \mathrm{ms} \pm 206 \mathrm{μs}\left({\color{gray}0.637 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$39.3 \mathrm{ms} \pm 264 \mathrm{μs}\left({\color{gray}1.41 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$41.3 \mathrm{ms} \pm 332 \mathrm{μs}\left({\color{gray}0.084 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$33.3 \mathrm{ms} \pm 194 \mathrm{μs}\left({\color{gray}0.431 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$424 \mathrm{ms} \pm 1.34 \mathrm{ms}\left({\color{gray}-0.429 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$90.2 \mathrm{ms} \pm 645 \mathrm{μs}\left({\color{gray}-0.324 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$78.6 \mathrm{ms} \pm 409 \mathrm{μs}\left({\color{gray}0.860 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$307 \mathrm{ms} \pm 1.44 \mathrm{ms}\left({\color{red}12.7 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$10.2 \mathrm{ms} \pm 75.8 \mathrm{μs}\left({\color{gray}0.594 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$10.4 \mathrm{ms} \pm 74.3 \mathrm{μs}\left({\color{gray}-0.187 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$10.4 \mathrm{ms} \pm 73.0 \mathrm{μs}\left({\color{gray}-0.283 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$10.4 \mathrm{ms} \pm 79.4 \mathrm{μs}\left({\color{gray}0.786 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$10.5 \mathrm{ms} \pm 61.3 \mathrm{μs}\left({\color{gray}-0.189 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$10.3 \mathrm{ms} \pm 57.9 \mathrm{μs}\left({\color{gray}0.394 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$10.3 \mathrm{ms} \pm 67.4 \mathrm{μs}\left({\color{gray}0.081 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$10.3 \mathrm{ms} \pm 49.3 \mathrm{μs}\left({\color{gray}-0.289 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$10.3 \mathrm{ms} \pm 71.7 \mathrm{μs}\left({\color{gray}-0.131 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$10.5 \mathrm{ms} \pm 83.8 \mathrm{μs}\left({\color{gray}0.936 \mathrm{\%}}\right) $$	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`	$$10.7 \mathrm{ms} \pm 68.5 \mathrm{μs}\left({\color{gray}-1.251 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$10.7 \mathrm{ms} \pm 61.6 \mathrm{μs}\left({\color{gray}-0.225 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$10.6 \mathrm{ms} \pm 68.2 \mathrm{μs}\left({\color{gray}-1.340 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$10.7 \mathrm{ms} \pm 65.5 \mathrm{μs}\left({\color{gray}0.374 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$10.7 \mathrm{ms} \pm 66.6 \mathrm{μs}\left({\color{gray}0.161 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$10.8 \mathrm{ms} \pm 75.4 \mathrm{μs}\left({\color{gray}0.916 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$10.8 \mathrm{ms} \pm 88.7 \mathrm{μs}\left({\color{gray}0.675 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$10.7 \mathrm{ms} \pm 73.1 \mathrm{μs}\left({\color{gray}-0.136 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$10.8 \mathrm{ms} \pm 65.0 \mathrm{μs}\left({\color{gray}0.458 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$7.89 \mathrm{ms} \pm 46.9 \mathrm{μs}\left({\color{gray}-0.654 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$55.3 \mathrm{ms} \pm 413 \mathrm{μs}\left({\color{gray}1.20 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$109 \mathrm{ms} \pm 734 \mathrm{μs}\left({\color{gray}1.75 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$61.3 \mathrm{ms} \pm 435 \mathrm{μs}\left({\color{gray}0.154 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$69.7 \mathrm{ms} \pm 362 \mathrm{μs}\left({\color{gray}-0.412 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$79.8 \mathrm{ms} \pm 619 \mathrm{μs}\left({\color{gray}1.41 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$85.8 \mathrm{ms} \pm 574 \mathrm{μs}\left({\color{gray}-0.542 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$45.0 \mathrm{ms} \pm 249 \mathrm{μs}\left({\color{gray}0.133 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$72.3 \mathrm{ms} \pm 543 \mathrm{μs}\left({\color{gray}0.409 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$51.1 \mathrm{ms} \pm 458 \mathrm{μs}\left({\color{gray}-0.167 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$59.5 \mathrm{ms} \pm 377 \mathrm{μs}\left({\color{gray}-0.447 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$61.9 \mathrm{ms} \pm 432 \mathrm{μs}\left({\color{gray}0.053 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$61.5 \mathrm{ms} \pm 382 \mathrm{μs}\left({\color{gray}-0.747 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$113 \mathrm{ms} \pm 613 \mathrm{μs}\left({\color{gray}-1.148 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$124 \mathrm{ms} \pm 676 \mathrm{μs}\left({\color{gray}-1.230 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$22.2 \mathrm{ms} \pm 203 \mathrm{μs}\left({\color{gray}-3.337 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$529 \mathrm{ms} \pm 1.40 \mathrm{ms}\left({\color{gray}-1.554 \mathrm{\%}}\right) $$	Flame Graph

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kube changed the title ~~Share the optimizer's Optuna study logic in a pure Python core library~~ FE-1581: Share the optimizer's Optuna study logic in a pure Python core library Sep 4, 2026

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kube added 5 commits September 6, 2026 02:06

Share the optimizer's Optuna study logic in a pure Python core library

eba78bf

Format the optimizer service pyproject for taplo

5510e2e

Keep the Optuna study alive across segments and run trials in parallel

df468a8

Check in the optimizer core library's turbo task dependencies

8ea621c

Scale the TPE start-up trials to the study's step count

52b4835

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FE-1581: Share the optimizer's Optuna study logic in a pure Python core library - #9514

FE-1581: Share the optimizer's Optuna study logic in a pure Python core library#9514
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github-actions Bot commented Sep 6, 2026

@rust/hash-graph-benches – Integrations

policy_resolution_large

policy_resolution_medium

policy_resolution_none

policy_resolution_small

read_scaling_complete

read_scaling_linkless

representative_read_entity

representative_read_entity_type

representative_read_multiple_entities

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Summary

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Python core library

Service

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Merging this PR will degrade performance by 15.38%

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github-actions Bot commented Sep 6, 2026

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

policy_resolution_medium

policy_resolution_none

policy_resolution_small

read_scaling_complete

read_scaling_linkless

representative_read_entity

representative_read_entity_type

representative_read_multiple_entities

scenarios

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