BE-254: Add CodSpeed benchmarking for HashQL MIR transformation passes

[indietyp]

🌟 What is the purpose of this PR?

Add CodSpeed benchmarking support to the repository, enabling performance tracking for Rust code.

Adding inline to the successor iterator gave us another 5-10% in speed, as this happened to be cross crate (hashql-mir -> hashql-core -> hashql-mir).

🔗 Related links

• CodSpeed Documentation

🔍 What does this change?

• Adds a new GitHub workflow for running CodSpeed benchmarks
• Integrates cargo-codspeed tool in mise configuration
• Converts the existing Criterion benchmarks in hashql-mir to use CodSpeed's Criterion compatibility layer
• Adds allocation limit control methods to the Heap implementation
• Adds a build:codspeed script to the hashql-mir 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:

• are internal and do not require a docs change

🕸️ 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?

• The benchmarks themselves serve as tests for the functionality

❓ How to test this?

1. Run turbo run build:codspeed --filter=hashql-mir to verify the benchmark builds
2. Run cargo codspeed run to execute codspeed benchmarks locally

[cursor]

PR Summary

Introduces CodSpeed-based benchmarking and minimal heap API additions to support reliable performance testing.

• CI: new CodSpeed Benchmarks workflow (.github/workflows/codspeed.yml); adds build:codspeed/test:codspeed tasks in turbo.json; installs cargo-codspeed via .config/mise/config.toml
• hashql-mir: converts Criterion benches to codspeed-criterion-compat, refactors bench harness (benches/transform.rs) to use Bencher with per-iteration batching, adds [[bench]] transform and package.json scripts (build:codspeed, test:codspeed)
• hashql-core heap: adds Allocator::set_allocation_limit, Heap::set_allocation_limit, and Scratch::with_capacity; minor #[inline] hints
• Minor perf hinting: add #[inline] on iterator methods in mir (basic_blocks.rs, terminator/mod.rs)

^{Written by Cursor Bugbot for commit 3326b24. This will update automatically on new commits. Configure here.}

[indietyp]

• BE-227: HashQL: Implement call graph analysis for MIR #8214
• BE-258: HashQL: Add Changed enum to track MIR transformation pass modifications #8210
• BE-255: HashQL: Implement Copy Propagation (CP) pass for MIR #8207
• BE-231: Add InstSimplify pass for MIR instruction simplification and constant folding #8206
• BE-254: Add CodSpeed benchmarking for HashQL MIR transformation passes #8204 👈 (View in Graphite)
• main

This stack of pull requests is managed by Graphite. Learn more about stacking.

[codecov]

Codecov Report

❌ Patch coverage is 0% with 11 lines in your changes missing coverage. Please review.
✅ Project coverage is 58.89%. Comparing base (b6338db) to head (3326b24).

Files with missing lines	Patch %	Lines
libs/@local/hashql/core/src/heap/scratch.rs	0.00%	5 Missing ⚠️
libs/@local/hashql/core/src/heap/allocator.rs	0.00%	3 Missing ⚠️
libs/@local/hashql/core/src/heap/mod.rs	0.00%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #8204      +/-   ##
==========================================
- Coverage   58.90%   58.89%   -0.01%     
==========================================
  Files        1193     1193              
  Lines      112723   112734      +11     
  Branches     5013     5013              
==========================================
+ Hits        66394    66396       +2     
- Misses      45571    45580       +9     
  Partials      758      758              

Flag	Coverage Δ
apps.hash-ai-worker-ts	1.32% <ø> (ø)
apps.hash-api	0.00% <ø> (ø)
blockprotocol.type-system	40.84% <ø> (ø)
local.claude-hooks	0.00% <ø> (ø)
local.harpc-client	51.24% <ø> (ø)
local.hash-graph-sdk	10.88% <ø> (ø)
local.hash-isomorphic-utils	0.00% <ø> (ø)
rust.antsi	0.00% <ø> (ø)
rust.error-stack	90.88% <ø> (ø)
rust.harpc-codec	84.70% <ø> (ø)
rust.harpc-net	96.19% <ø> (+0.03%)	⬆️
rust.harpc-tower	66.80% <ø> (ø)
rust.harpc-types	0.00% <ø> (ø)
rust.harpc-wire-protocol	92.23% <ø> (ø)
rust.hash-codec	72.76% <ø> (ø)
rust.hash-graph-api	2.89% <ø> (ø)
rust.hash-graph-authorization	62.47% <ø> (ø)
rust.hash-graph-postgres-store	25.61% <ø> (ø)
rust.hash-graph-store	30.54% <ø> (ø)
rust.hash-graph-temporal-versioning	47.95% <ø> (ø)
rust.hash-graph-types	0.00% <ø> (ø)
rust.hash-graph-validation	83.45% <ø> (ø)
rust.hashql-ast	87.25% <ø> (ø)
rust.hashql-compiletest	46.65% <ø> (ø)
rust.hashql-core	82.32% <0.00%> (-0.05%)	⬇️
rust.hashql-diagnostics	72.43% <ø> (ø)
rust.hashql-eval	68.54% <ø> (ø)
rust.hashql-hir	89.10% <ø> (ø)
rust.hashql-mir	88.18% <ø> (ø)
rust.hashql-syntax-jexpr	94.05% <ø> (ø)

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[codspeed-hq]

CodSpeed Performance Report

Congrats! CodSpeed is installed 🎉

🆕 13 new benchmarks were detected.

You will start to see performance impacts in the reports once the benchmarks are run from your default branch.

Detected benchmarks

• complex: 60.2 µs
• linear: 31.1 µs
• diamond: 15.9 µs
• linear: 17.8 µs
• complex: 25.2 µs
• complex: 24.1 µs
• diamond: 44.1 µs
• dead stores: 11.9 µs
• diamond: 20.5 µs
• complex: 19.4 µs
• linear: 8.6 µs
• diamond: 17.6 µs
• linear: 12.2 µs

[vilkinsons]

@indietyp How will this interact with (replace/complement) the existing benchmark runner/comment bot?

[indietyp]

@indietyp How will this interact with (replace/complement) the existing benchmark runner/comment bot?

They are (for now) different because they test various things. The codspeed tests are currently microbenchmarks (meaning they test only a fraction of the system to evaluate, e.g., the performance of a function or, in our case, an analysis pass). In contrast, the existing benchmarks are macro (they test the whole system). I could see a future (needs to be evaluated in tandem with @TimDiekmann and formulated into a plan) to move the existing macro benchmarks to codspeed as well (as it supports macro benchmarks as well).

My hope here is that we get good benchmarking without having to roll our own, which reduces both the complexity and the code/infrastructure we need to maintain. (similarly to what we do with codecov right now, it's not perfect by any means, but it's good enough™ that an in-house solution is just not worth it)

[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	$$27.9 \mathrm{ms} \pm 287 \mathrm{μs}\left({\color{gray}-1.642 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.21 \mathrm{ms} \pm 12.5 \mathrm{μs}\left({\color{gray}-0.241 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.2 \mathrm{ms} \pm 70.0 \mathrm{μs}\left({\color{gray}0.293 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.0 \mathrm{ms} \pm 311 \mathrm{μs}\left({\color{gray}-0.881 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.0 \mathrm{ms} \pm 78.2 \mathrm{μs}\left({\color{gray}-0.020 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.5 \mathrm{ms} \pm 154 \mathrm{μs}\left({\color{gray}-0.134 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$31.0 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{lightgreen}-28.562 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.61 \mathrm{ms} \pm 18.7 \mathrm{μs}\left({\color{lightgreen}-82.293 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$14.1 \mathrm{ms} \pm 89.5 \mathrm{μs}\left({\color{lightgreen}-50.645 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.63 \mathrm{ms} \pm 16.6 \mathrm{μs}\left({\color{gray}0.930 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.84 \mathrm{ms} \pm 14.5 \mathrm{μs}\left({\color{gray}-0.057 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.19 \mathrm{ms} \pm 16.2 \mathrm{μs}\left({\color{gray}0.502 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.97 \mathrm{ms} \pm 28.9 \mathrm{μs}\left({\color{gray}0.150 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.37 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{gray}-0.853 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.96 \mathrm{ms} \pm 21.1 \mathrm{μs}\left({\color{gray}0.503 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.13 \mathrm{ms} \pm 24.3 \mathrm{μs}\left({\color{gray}2.51 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.29 \mathrm{ms} \pm 16.2 \mathrm{μs}\left({\color{gray}1.20 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.83 \mathrm{ms} \pm 24.1 \mathrm{μs}\left({\color{gray}-0.053 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.59 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{red}9.40 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.50 \mathrm{ms} \pm 9.39 \mathrm{μs}\left({\color{red}7.47 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.60 \mathrm{ms} \pm 11.5 \mathrm{μs}\left({\color{red}5.96 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.80 \mathrm{ms} \pm 13.2 \mathrm{μs}\left({\color{red}6.03 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.68 \mathrm{ms} \pm 10.5 \mathrm{μs}\left({\color{red}7.43 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.87 \mathrm{ms} \pm 13.4 \mathrm{μs}\left({\color{red}5.99 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.87 \mathrm{ms} \pm 11.5 \mathrm{μs}\left({\color{red}5.21 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.59 \mathrm{ms} \pm 10.9 \mathrm{μs}\left({\color{red}7.67 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.73 \mathrm{ms} \pm 11.5 \mathrm{μs}\left({\color{red}6.42 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.23 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{gray}4.70 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.83 \mathrm{ms} \pm 13.3 \mathrm{μs}\left({\color{red}7.19 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.02 \mathrm{ms} \pm 14.2 \mathrm{μs}\left({\color{red}5.24 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.10 \mathrm{ms} \pm 10.7 \mathrm{μs}\left({\color{gray}3.71 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.79 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{red}5.79 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.02 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{red}5.48 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$38.9 \mathrm{ms} \pm 174 \mathrm{μs}\left({\color{gray}0.097 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$76.5 \mathrm{ms} \pm 462 \mathrm{μs}\left({\color{gray}0.280 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$43.8 \mathrm{ms} \pm 194 \mathrm{μs}\left({\color{gray}0.014 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.6 \mathrm{ms} \pm 194 \mathrm{μs}\left({\color{gray}-0.072 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$54.5 \mathrm{ms} \pm 330 \mathrm{μs}\left({\color{gray}3.77 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.6 \mathrm{ms} \pm 191 \mathrm{μs}\left({\color{gray}0.204 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$423 \mathrm{ms} \pm 928 \mathrm{μs}\left({\color{gray}2.02 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$91.3 \mathrm{ms} \pm 379 \mathrm{μs}\left({\color{gray}-3.420 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$84.2 \mathrm{ms} \pm 365 \mathrm{μs}\left({\color{gray}-1.495 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$261 \mathrm{ms} \pm 760 \mathrm{μs}\left({\color{lightgreen}-7.285 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.9 \mathrm{ms} \pm 69.7 \mathrm{μs}\left({\color{gray}1.02 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$15.0 \mathrm{ms} \pm 68.2 \mathrm{μs}\left({\color{gray}1.85 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.2 \mathrm{ms} \pm 68.1 \mathrm{μs}\left({\color{gray}1.72 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$14.8 \mathrm{ms} \pm 65.1 \mathrm{μs}\left({\color{gray}-0.852 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$17.9 \mathrm{ms} \pm 94.7 \mathrm{μs}\left({\color{gray}1.28 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.5 \mathrm{ms} \pm 61.4 \mathrm{μs}\left({\color{gray}-2.670 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.8 \mathrm{ms} \pm 69.5 \mathrm{μs}\left({\color{gray}0.984 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.8 \mathrm{ms} \pm 72.0 \mathrm{μs}\left({\color{gray}1.06 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.3 \mathrm{ms} \pm 92.8 \mathrm{μs}\left({\color{gray}0.922 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.5 \mathrm{ms} \pm 164 \mathrm{μs}\left({\color{gray}-0.104 \mathrm{\%}}\right) $$	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	$$29.5 \mathrm{ms} \pm 270 \mathrm{μs}\left({\color{gray}-0.587 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$30.0 \mathrm{ms} \pm 269 \mathrm{μs}\left({\color{gray}-0.503 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$29.1 \mathrm{ms} \pm 232 \mathrm{μs}\left({\color{gray}-2.532 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$30.5 \mathrm{ms} \pm 300 \mathrm{μs}\left({\color{gray}3.19 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$29.5 \mathrm{ms} \pm 298 \mathrm{μs}\left({\color{gray}-2.340 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$29.6 \mathrm{ms} \pm 270 \mathrm{μs}\left({\color{gray}1.31 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$30.6 \mathrm{ms} \pm 234 \mathrm{μs}\left({\color{gray}0.532 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$30.3 \mathrm{ms} \pm 255 \mathrm{μs}\left({\color{gray}2.03 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$29.3 \mathrm{ms} \pm 281 \mathrm{μs}\left({\color{gray}-1.408 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.10 \mathrm{ms} \pm 31.5 \mathrm{μs}\left({\color{gray}1.31 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$46.9 \mathrm{ms} \pm 233 \mathrm{μs}\left({\color{gray}-0.171 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$94.8 \mathrm{ms} \pm 349 \mathrm{μs}\left({\color{gray}0.545 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$53.0 \mathrm{ms} \pm 301 \mathrm{μs}\left({\color{gray}0.039 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$60.9 \mathrm{ms} \pm 344 \mathrm{μs}\left({\color{gray}0.526 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$69.1 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}0.535 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$75.3 \mathrm{ms} \pm 395 \mathrm{μs}\left({\color{gray}-0.061 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$50.7 \mathrm{ms} \pm 249 \mathrm{μs}\left({\color{gray}0.503 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$77.9 \mathrm{ms} \pm 373 \mathrm{μs}\left({\color{gray}0.417 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$57.6 \mathrm{ms} \pm 274 \mathrm{μs}\left({\color{gray}0.531 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$65.3 \mathrm{ms} \pm 379 \mathrm{μs}\left({\color{gray}0.633 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$67.4 \mathrm{ms} \pm 340 \mathrm{μs}\left({\color{gray}0.249 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$67.3 \mathrm{ms} \pm 279 \mathrm{μs}\left({\color{gray}0.661 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$134 \mathrm{ms} \pm 506 \mathrm{μs}\left({\color{gray}2.52 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$130 \mathrm{ms} \pm 428 \mathrm{μs}\left({\color{gray}-1.861 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$40.3 \mathrm{ms} \pm 317 \mathrm{μs}\left({\color{lightgreen}-61.131 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$583 \mathrm{ms} \pm 993 \mathrm{μs}\left({\color{lightgreen}-5.863 \mathrm{\%}}\right) $$	Flame Graph