FE-1314: Run experiment runs in parallel across workers

[kube]

🌟 What is the purpose of this PR?

Splits an experiment's runs across Web Workers. One worker per logical core minus one, capped at the run count.

Measured on the SIR example, 2000 runs, 10-core machine: 4.1x at 8 shards, byte-identical output at every shard count.

🔗 Related links

• FE-1314 (internal)
• FE-948 (internal). Sharding divides the quadratic enumeration cost across workers; it does not fix it.

🔍 What does this change?

Three changes keep results identical at any shard count:

Change	Why
Seeds derive from the run's global index	Run i gets the same seed whichever worker owns it
A frame finalises once every still-running shard reports it	Finished shards drop out of the watermark instead of blocking it
Scalar frames carry pre-reduction accumulator state	frameValue is already reduced; a mean of means is not a mean

The metric monoids (empty/merge) recombine the per-shard state.

No SharedArrayBuffer: the app is not cross-origin isolated, and independent runs need no shared memory.

// Defaults to one worker per core minus one.
<ExperimentsProvider workerFactory={createWorker} experimentShardCount={4}>

Adds content/simulation/worker-sharding.mdx.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

• modifies an npm-publishable library and I have added a changeset file(s)

📜 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:

• do not affect the execution graph

⚠️ Known issues

Concurrent experiments each take the same number of workers, so they compete for cores. Progress reports the slowest shard's position.

🐾 Next steps

Stacked above: #9177 place capacity, #9178 backend interface, #9179 WebGPU backend.

🛡 What tests cover this?

shard-plan.test.ts (the split), metrics/merge.test.ts (monoid recombination), experiment.test.ts (watermark, and identical output at every shard count). Benchmarks in benchmarks/sharded-experiment*.mjs.

❓ How to test this?

1. Run an experiment with a few thousand runs. CPU use spans several cores.
2. Re-run with the same seed at experimentShardCount 1, then unset. Distributions match.

[vercel]

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Project	Deployment	Actions	Updated (UTC)
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[semgrep-code-hashintel]

Semgrep found 2 detect-non-literal-regexp findings:

• libs/@local/petrinaut-arch-docs/src/scope.test.ts
  • L42-45 - Triage
  • L55-58 - Triage

RegExp() called with a overrides function argument, this might allow an attacker to cause a Regular Expression Denial-of-Service (ReDoS) within your application as RegExP blocks the main thread. For this reason, it is recommended to use hardcoded regexes instead. If your regex is run on user-controlled input, consider performing input validation or use a regex checking/sanitization library such as https://www.npmjs.com/package/recheck to verify that the regex does not appear vulnerable to ReDoS.

View Dataflow Graph

flowchart LR
    classDef invis fill:white, stroke: none
    classDef default fill:#e7f5ff, color:#1c7fd6, stroke: none

    subgraph File0["<b>libs/@local/petrinaut-arch-docs/src/scope.test.ts</b>"]
        direction LR
        %% Source

        subgraph Source
            direction LR

            v0["<a href=https://github.com/hashintel/hash/blob/be4731ded37213256e4c2f509ba31a8745a24dd7/libs/@local/petrinaut-arch-docs/src/scope.test.ts#L19 target=_blank style='text-decoration:none; color:#1c7fd6'>[Line: 19] overrides</a>"]
        end
        %% Intermediate

        subgraph Traces0[Traces]
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            v2["<a href=https://github.com/hashintel/hash/blob/be4731ded37213256e4c2f509ba31a8745a24dd7/libs/@local/petrinaut-arch-docs/src/scope.test.ts#L19 target=_blank style='text-decoration:none; color:#1c7fd6'>[Line: 19] overrides</a>"]

            v3["<a href=https://github.com/hashintel/hash/blob/be4731ded37213256e4c2f509ba31a8745a24dd7/libs/@local/petrinaut-arch-docs/src/scope.test.ts#L56 target=_blank style='text-decoration:none; color:#1c7fd6'>[Line: 56] pkg</a>"]
        end
            v2 --> v3
        %% Sink

        subgraph Sink
            direction LR

            v1["<a href=https://github.com/hashintel/hash/blob/be4731ded37213256e4c2f509ba31a8745a24dd7/libs/@local/petrinaut-arch-docs/src/scope.test.ts#L55 target=_blank style='text-decoration:none; color:#1c7fd6'>[Line: 55] new RegExp(<br>      sourceRootPattern([pkg({ path: &quot;libs/@scope/a.b&quot; })]),<br>      &quot;u&quot;,<br>    )</a>"]
        end
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    %% Class Assignment
    Source:::invis
    Sink:::invis

    Traces0:::invis
    File0:::invis

    %% Connections

    Source --> Traces0
    Traces0 --> Sink

Loading

[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 cf/fe-1314-run-experiment-runs-in-parallel-across-workers (be4731d) with cf/fe-1322-arch-docs-site (61ef474)}

[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	$$28.0 \mathrm{ms} \pm 227 \mathrm{μs}\left({\color{gray}-0.367 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.47 \mathrm{ms} \pm 25.4 \mathrm{μs}\left({\color{gray}0.475 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$13.3 \mathrm{ms} \pm 120 \mathrm{μs}\left({\color{gray}3.25 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$44.3 \mathrm{ms} \pm 323 \mathrm{μs}\left({\color{gray}1.16 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.6 \mathrm{ms} \pm 123 \mathrm{μs}\left({\color{gray}2.58 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$25.1 \mathrm{ms} \pm 210 \mathrm{μs}\left({\color{gray}3.43 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$29.5 \mathrm{ms} \pm 248 \mathrm{μs}\left({\color{gray}2.84 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.73 \mathrm{ms} \pm 20.4 \mathrm{μs}\left({\color{gray}0.127 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$14.2 \mathrm{ms} \pm 124 \mathrm{μs}\left({\color{gray}1.54 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.82 \mathrm{ms} \pm 25.9 \mathrm{μs}\left({\color{gray}0.126 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 17.4 \mathrm{μs}\left({\color{gray}-1.029 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$3.39 \mathrm{ms} \pm 22.9 \mathrm{μs}\left({\color{gray}-1.716 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.14 \mathrm{ms} \pm 28.9 \mathrm{μs}\left({\color{gray}-1.726 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.57 \mathrm{ms} \pm 27.3 \mathrm{μs}\left({\color{gray}-0.033 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$4.14 \mathrm{ms} \pm 27.8 \mathrm{μs}\left({\color{gray}-0.532 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.36 \mathrm{ms} \pm 24.5 \mathrm{μs}\left({\color{gray}-2.312 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.44 \mathrm{ms} \pm 24.6 \mathrm{μs}\left({\color{gray}-0.687 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.13 \mathrm{ms} \pm 34.7 \mathrm{μs}\left({\color{gray}-0.512 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.71 \mathrm{ms} \pm 18.6 \mathrm{μs}\left({\color{gray}3.78 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.53 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}0.772 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.68 \mathrm{ms} \pm 19.5 \mathrm{μs}\left({\color{gray}2.96 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$3.01 \mathrm{ms} \pm 22.4 \mathrm{μs}\left({\color{red}5.27 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.74 \mathrm{ms} \pm 17.0 \mathrm{μs}\left({\color{gray}3.76 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$3.02 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{red}6.00 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.04 \mathrm{ms} \pm 22.3 \mathrm{μs}\left({\color{gray}-1.375 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.75 \mathrm{ms} \pm 17.0 \mathrm{μs}\left({\color{gray}0.086 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$2.99 \mathrm{ms} \pm 19.5 \mathrm{μs}\left({\color{gray}2.53 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.44 \mathrm{ms} \pm 22.7 \mathrm{μs}\left({\color{gray}-1.454 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.97 \mathrm{ms} \pm 17.7 \mathrm{μs}\left({\color{gray}-1.651 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$3.30 \mathrm{ms} \pm 21.3 \mathrm{μs}\left({\color{gray}1.15 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.37 \mathrm{ms} \pm 21.6 \mathrm{μs}\left({\color{gray}-1.365 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.91 \mathrm{ms} \pm 17.5 \mathrm{μs}\left({\color{gray}-2.569 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.31 \mathrm{ms} \pm 20.3 \mathrm{μs}\left({\color{gray}-0.901 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$44.1 \mathrm{ms} \pm 284 \mathrm{μs}\left({\color{gray}4.76 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$33.3 \mathrm{ms} \pm 211 \mathrm{μs}\left({\color{gray}-0.507 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$36.0 \mathrm{ms} \pm 243 \mathrm{μs}\left({\color{gray}1.34 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$34.1 \mathrm{ms} \pm 317 \mathrm{μs}\left({\color{red}6.46 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$44.1 \mathrm{ms} \pm 291 \mathrm{μs}\left({\color{red}5.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$50.5 \mathrm{ms} \pm 391 \mathrm{μs}\left({\color{gray}1.86 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$40.6 \mathrm{ms} \pm 285 \mathrm{μs}\left({\color{gray}0.651 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$94.3 \mathrm{ms} \pm 529 \mathrm{μs}\left({\color{gray}3.90 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$34.2 \mathrm{ms} \pm 216 \mathrm{μs}\left({\color{gray}0.882 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$308 \mathrm{ms} \pm 1.09 \mathrm{ms}\left({\color{gray}1.59 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$11.2 \mathrm{ms} \pm 80.1 \mathrm{μs}\left({\color{gray}2.48 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$11.2 \mathrm{ms} \pm 59.5 \mathrm{μs}\left({\color{gray}0.568 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$11.2 \mathrm{ms} \pm 67.5 \mathrm{μs}\left({\color{gray}1.54 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$11.4 \mathrm{ms} \pm 98.8 \mathrm{μs}\left({\color{gray}2.92 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$11.2 \mathrm{ms} \pm 64.1 \mathrm{μs}\left({\color{gray}1.44 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$11.0 \mathrm{ms} \pm 66.9 \mathrm{μs}\left({\color{gray}-0.256 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$11.1 \mathrm{ms} \pm 67.4 \mathrm{μs}\left({\color{gray}-0.514 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$11.2 \mathrm{ms} \pm 76.3 \mathrm{μs}\left({\color{gray}0.764 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$11.6 \mathrm{ms} \pm 93.9 \mathrm{μs}\left({\color{gray}3.41 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$11.2 \mathrm{ms} \pm 71.3 \mathrm{μs}\left({\color{gray}-3.033 \mathrm{\%}}\right) $$	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	$$11.5 \mathrm{ms} \pm 71.1 \mathrm{μs}\left({\color{gray}-1.805 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$11.6 \mathrm{ms} \pm 68.9 \mathrm{μs}\left({\color{gray}-1.861 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$11.7 \mathrm{ms} \pm 86.5 \mathrm{μs}\left({\color{gray}0.087 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$11.6 \mathrm{ms} \pm 72.1 \mathrm{μs}\left({\color{gray}-2.120 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$11.8 \mathrm{ms} \pm 87.5 \mathrm{μs}\left({\color{gray}1.25 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$11.6 \mathrm{ms} \pm 82.7 \mathrm{μs}\left({\color{gray}-0.270 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$12.1 \mathrm{ms} \pm 82.5 \mathrm{μs}\left({\color{gray}2.99 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$11.6 \mathrm{ms} \pm 66.4 \mathrm{μs}\left({\color{gray}-0.153 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$11.5 \mathrm{ms} \pm 71.8 \mathrm{μs}\left({\color{gray}-3.697 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.62 \mathrm{ms} \pm 51.3 \mathrm{μs}\left({\color{gray}-1.300 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$57.0 \mathrm{ms} \pm 528 \mathrm{μs}\left({\color{lightgreen}-9.805 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$109 \mathrm{ms} \pm 634 \mathrm{μs}\left({\color{gray}-4.530 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$64.5 \mathrm{ms} \pm 589 \mathrm{μs}\left({\color{lightgreen}-6.838 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$72.9 \mathrm{ms} \pm 461 \mathrm{μs}\left({\color{lightgreen}-7.994 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$84.5 \mathrm{ms} \pm 601 \mathrm{μs}\left({\color{gray}-4.907 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$87.4 \mathrm{ms} \pm 703 \mathrm{μs}\left({\color{lightgreen}-6.605 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$44.3 \mathrm{ms} \pm 285 \mathrm{μs}\left({\color{gray}-0.431 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$71.5 \mathrm{ms} \pm 480 \mathrm{μs}\left({\color{gray}-2.027 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$48.8 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}-3.040 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$57.2 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{gray}-3.338 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$61.8 \mathrm{ms} \pm 377 \mathrm{μs}\left({\color{gray}0.893 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$62.0 \mathrm{ms} \pm 525 \mathrm{μs}\left({\color{gray}0.330 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$121 \mathrm{ms} \pm 716 \mathrm{μs}\left({\color{red}7.09 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$133 \mathrm{ms} \pm 644 \mathrm{μs}\left({\color{red}6.20 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$20.4 \mathrm{ms} \pm 120 \mathrm{μs}\left({\color{lightgreen}-17.532 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$518 \mathrm{ms} \pm 1.41 \mathrm{ms}\left({\color{gray}2.21 \mathrm{\%}}\right) $$	Flame Graph

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 59.63%. Comparing base (27d9fc0) to head (b71b9ed).
⚠️ Report is 3 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #9162   +/-   ##
=======================================
  Coverage   59.63%   59.63%           
=======================================
  Files        1420     1420           
  Lines      138767   138767           
  Branches     6555     6555           
=======================================
  Hits        82753    82753           
  Misses      54950    54950           
  Partials     1064     1064           

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