BE-267: Use bump scopes instead of full resets across transformation passes

[indietyp]

🌟 What is the purpose of this PR?

Replaces the ResetAllocator pattern with scoped checkpoints in the HashQL MIR transformation pipeline. Instead of resetting the entire scratch allocator between passes, we now use checkpoint()/rollback() semantics and scoped sub-arenas, which provides more granular memory management, and enables more advanced patterns in the future.

🔍 What does this change?

• Adds Checkpoint type and checkpoint()/rollback() methods to the BumpAllocator trait
• Updates PreInlining pass to use BumpAllocator::scoped() for each sub-pass instead of resetting the allocator
• Updates all transformation passes (CopyPropagation, CfgSimplify, InstSimplify, ForwardSubstitution, AdministrativeReduction, DeadStoreElimination, etc.) to work with scoped allocators
• Updates benchmarks to use the new scoping pattern
• Uses heap-based tracking for changed state in transformation passes

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:

• do not affect the execution graph

🛡 What tests cover this?

• Existing MIR transformation tests and compiletest UI tests

❓ How to test this?

1. Run cargo nextest run --package hashql-mir
2. Run transformation benchmarks: `cargo bench --package

[cursor]

PR Summary

Introduces scoped bump allocation with checkpoints and refactors MIR transforms to use it, reducing full allocator resets and improving temporary memory management.

• Add Checkpoint type and checkpoint/rollback to BumpAllocator; implement in core allocator, Heap, and Scratch
• Update MIR passes to use BumpAllocator instead of ResetAllocator and run sub-steps in scoped arenas: CfgSimplify (now scopes SSA repair/DBE and extracts helpers), AdministrativeReduction, CopyPropagation, DeadBlockElimination, DeadLocalElimination, DeadStoreElimination, InstSimplify, SsaRepair, and PreInlining
• Rework PreInlining to use scoped allocators per sub-pass and track per-body Changed state on the heap
• Benchmarks updated to thread a Scratch through runners and construct passes with new_in(scratch)
• WorkQueue::new_in now preallocates with VecDeque::with_capacity_in(domain_size)
• Minor API adjustments: ForwardSubstitution generic over Allocator (defaults to Global), import cleanups

^{Written by Cursor Bugbot for commit c1ba012. This will update automatically on new commits. Configure here.}

[indietyp]

• BE-304: HashQL: Terminator placement based on capabilities #8359
• BE-303: HashQL: Split MIR basic blocks by target support #8354
• BE-364: Rework symbol representation for speed and size #8343
• BE-360: HashQL: Rework compiletest UI #8340
• BE-302: HashQL: Statement cost analysis for execution target placement #8339
• BE-317: HashQL: Implement TraversalExtraction pass #8331
• BE-315: HashQL: Add GraphReadFilter source variant to MIR #8323
• BE-301: HashQL: Implement size estimation analysis for MIR #8278
• BE-273: HashQL: Profile and benchmark MIR interpreter #8251
• BE-272: HashQL: Add numeric addition and subtraction operators to MIR #8250
• BE-257: HashQL: Implement MIR interpreter with runtime value representation #8245
• BE-271: HashQL: Implement PostInline pass #8240
• BE-269: HashQL: Move PreInline out into Canonicalization pass #8239
• BE-268: HashQL: Rename PreInlining to PreInline for consistency #8238
• BE-270: HashQL: Optimize empty tuples to unit constants in MIR #8237
• BE-197: Implement MIR inline pass for HashQL #8236
• BE-267: Use bump scopes instead of full resets across transformation passes #8234 👈 (View in Graphite)
• BE-266: HashQL: Implement PreInlining pass for MIR optimization #8233
• BE-265: HashQL: Make CopyPropagation aware of local re-assignments #8231
• BE-264: HashQL: Rename SROA to ForwardSubstitution for better semantic clarity #8230
• BE-263: HashQL: add body! macro for declarative MIR construction in tests #8229
• BE-261: HashQL: Implement Administrative Reduction pass for MIR #8227
• BE-262: HashQL: Do not intern empty slices #8223
• BE-260: HashQL: Switch to bump allocator with scoping support #8218
• BE-259: HashQL: Add benchmarks for type system operations #8216
• 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: HashQL: Add InstSimplify pass for MIR instruction simplification and constant folding #8206
• BE-254: Add CodSpeed benchmarking for HashQL MIR transformation passes #8204
• main

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[augmentcode]

🤖 Augment PR Summary

Summary: Refactors the HashQL MIR transformation pipeline to stop doing full scratch allocator resets between passes and instead rely on scoped bump allocation/checkpoints for finer-grained temporary memory management.

Changes:

• Adds a Checkpoint type plus BumpAllocator::checkpoint()/rollback(), implemented for Allocator, AllocatorScope, Heap, and Scratch
• Updates major MIR passes (e.g. CfgSimplify, CopyPropagation, DSE, SSARepair, AdministrativeReduction) to accept BumpAllocator and use alloc.scoped(...) instead of calling reset()
• Refactors CfgSimplify internals into helper functions and runs sub-passes under allocator scopes
• Adjusts PreInlining to scope each sub-pass and uses a heap-backed per-body changed-state slice for the fixpoint loop
• Updates transformation benchmarks to thread a reusable Scratch through each iteration and construct passes with new_in(scratch)
• Minor allocation improvement: WorkQueue now pre-allocates its VecDeque with domain-sized capacity

Technical Notes: The intent is to enable more granular allocator usage patterns (checkpoint/rollback + sub-arenas) while keeping pass-local allocations short-lived and composable.

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[augmentcode]

Review completed. 1 suggestions posted.

Comment augment review to trigger a new review at any time.

[codspeed-hq]

CodSpeed Performance Report

Merging this PR will not alter performance

_{Comparing bm/be-267-hashql-do-not-reset-the-scratch-space-but-instead-use-scopes (c1ba012) with main (40bee1c)}

Summary

✅ 17 untouched benchmarks
🗄️ 12 archived benchmarks run¹

Footnotes

1. 12 benchmarks were run, but are now archived. If they were deleted in another branch, consider rebasing to remove them from the report. Instead if they were added back, click here to restore them. ↩

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 58.68%. Comparing base (fd458f2) to head (c1ba012).
⚠️ Report is 6 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #8234      +/-   ##
==========================================
- Coverage   59.24%   58.68%   -0.57%     
==========================================
  Files        1191     1191              
  Lines      113313   111702    -1611     
  Branches     4975     4946      -29     
==========================================
- Hits        67134    65547    -1587     
+ Misses      45403    45387      -16     
+ Partials      776      768       -8     

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[graphite-app]

Merge activity

• Jan 17, 5:22 PM UTC: Graphite rebased this pull request, because this pull request is set to merge when ready.

[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	$$26.3 \mathrm{ms} \pm 207 \mathrm{μs}\left({\color{gray}-2.882 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.21 \mathrm{ms} \pm 13.6 \mathrm{μs}\left({\color{gray}-4.142 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.2 \mathrm{ms} \pm 71.6 \mathrm{μs}\left({\color{lightgreen}-8.641 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.4 \mathrm{ms} \pm 273 \mathrm{μs}\left({\color{gray}-2.505 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.2 \mathrm{ms} \pm 91.7 \mathrm{μs}\left({\color{gray}-4.768 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$24.1 \mathrm{ms} \pm 173 \mathrm{μs}\left({\color{gray}-1.077 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.8 \mathrm{ms} \pm 160 \mathrm{μs}\left({\color{lightgreen}-38.598 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.66 \mathrm{ms} \pm 16.6 \mathrm{μs}\left({\color{lightgreen}-82.419 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$11.8 \mathrm{ms} \pm 89.4 \mathrm{μs}\left({\color{lightgreen}-62.852 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.68 \mathrm{ms} \pm 20.0 \mathrm{μs}\left({\color{gray}-1.459 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.90 \mathrm{ms} \pm 11.0 \mathrm{μs}\left({\color{gray}0.924 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.25 \mathrm{ms} \pm 13.7 \mathrm{μs}\left({\color{gray}0.431 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.13 \mathrm{ms} \pm 22.6 \mathrm{μs}\left({\color{gray}1.17 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.48 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}1.94 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.03 \mathrm{ms} \pm 19.2 \mathrm{μs}\left({\color{gray}0.582 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.26 \mathrm{ms} \pm 19.4 \mathrm{μs}\left({\color{gray}-0.473 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.42 \mathrm{ms} \pm 17.1 \mathrm{μs}\left({\color{gray}3.13 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.94 \mathrm{ms} \pm 24.6 \mathrm{μs}\left({\color{gray}-1.073 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.59 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{red}7.42 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.52 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{red}6.44 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.65 \mathrm{ms} \pm 11.5 \mathrm{μs}\left({\color{red}7.85 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.84 \mathrm{ms} \pm 15.5 \mathrm{μs}\left({\color{gray}4.45 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.70 \mathrm{ms} \pm 9.94 \mathrm{μs}\left({\color{red}6.08 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.93 \mathrm{ms} \pm 15.0 \mathrm{μs}\left({\color{red}5.95 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.98 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{red}5.71 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.66 \mathrm{ms} \pm 10.0 \mathrm{μs}\left({\color{red}6.53 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.85 \mathrm{ms} \pm 14.0 \mathrm{μs}\left({\color{red}8.09 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.33 \mathrm{ms} \pm 20.4 \mathrm{μs}\left({\color{red}6.40 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.89 \mathrm{ms} \pm 11.4 \mathrm{μs}\left({\color{red}5.44 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.12 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{red}5.31 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.20 \mathrm{ms} \pm 13.4 \mathrm{μs}\left({\color{red}5.55 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.86 \mathrm{ms} \pm 13.0 \mathrm{μs}\left({\color{red}6.07 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.10 \mathrm{ms} \pm 13.0 \mathrm{μs}\left({\color{red}6.77 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$39.0 \mathrm{ms} \pm 172 \mathrm{μs}\left({\color{gray}0.352 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$75.7 \mathrm{ms} \pm 480 \mathrm{μs}\left({\color{gray}-2.460 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$43.4 \mathrm{ms} \pm 201 \mathrm{μs}\left({\color{gray}-2.222 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$46.2 \mathrm{ms} \pm 225 \mathrm{μs}\left({\color{gray}-0.212 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$53.5 \mathrm{ms} \pm 256 \mathrm{μs}\left({\color{gray}-0.366 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.3 \mathrm{ms} \pm 188 \mathrm{μs}\left({\color{gray}-3.189 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$413 \mathrm{ms} \pm 916 \mathrm{μs}\left({\color{gray}-0.474 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$94.9 \mathrm{ms} \pm 368 \mathrm{μs}\left({\color{gray}-2.941 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$85.1 \mathrm{ms} \pm 395 \mathrm{μs}\left({\color{gray}-0.405 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$283 \mathrm{ms} \pm 752 \mathrm{μs}\left({\color{lightgreen}-9.501 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.6 \mathrm{ms} \pm 65.1 \mathrm{μs}\left({\color{gray}-4.934 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$14.8 \mathrm{ms} \pm 82.4 \mathrm{μs}\left({\color{gray}-2.844 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.3 \mathrm{ms} \pm 74.9 \mathrm{μs}\left({\color{gray}-0.956 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$14.9 \mathrm{ms} \pm 65.2 \mathrm{μs}\left({\color{gray}-1.985 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$17.7 \mathrm{ms} \pm 98.1 \mathrm{μs}\left({\color{gray}-3.318 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.6 \mathrm{ms} \pm 67.2 \mathrm{μs}\left({\color{gray}-0.101 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.7 \mathrm{ms} \pm 61.1 \mathrm{μs}\left({\color{gray}-1.524 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.8 \mathrm{ms} \pm 69.6 \mathrm{μs}\left({\color{gray}-2.190 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.4 \mathrm{ms} \pm 118 \mathrm{μs}\left({\color{gray}-2.968 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$23.6 \mathrm{ms} \pm 168 \mathrm{μs}\left({\color{gray}1.60 \mathrm{\%}}\right) $$	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	$$31.3 \mathrm{ms} \pm 279 \mathrm{μs}\left({\color{gray}2.56 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$30.4 \mathrm{ms} \pm 307 \mathrm{μs}\left({\color{gray}-2.513 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$29.7 \mathrm{ms} \pm 285 \mathrm{μs}\left({\color{lightgreen}-5.057 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$29.2 \mathrm{ms} \pm 333 \mathrm{μs}\left({\color{gray}-4.596 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$31.5 \mathrm{ms} \pm 287 \mathrm{μs}\left({\color{gray}2.15 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$30.1 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}-4.354 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$28.8 \mathrm{ms} \pm 259 \mathrm{μs}\left({\color{gray}-4.596 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$29.3 \mathrm{ms} \pm 288 \mathrm{μs}\left({\color{lightgreen}-5.052 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$29.5 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{lightgreen}-6.983 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.04 \mathrm{ms} \pm 37.3 \mathrm{μs}\left({\color{gray}-2.547 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$48.4 \mathrm{ms} \pm 214 \mathrm{μs}\left({\color{gray}-1.137 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$94.0 \mathrm{ms} \pm 489 \mathrm{μs}\left({\color{gray}-3.154 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$53.2 \mathrm{ms} \pm 287 \mathrm{μs}\left({\color{gray}-2.553 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$61.3 \mathrm{ms} \pm 369 \mathrm{μs}\left({\color{gray}-2.405 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$68.7 \mathrm{ms} \pm 332 \mathrm{μs}\left({\color{gray}-3.361 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$74.9 \mathrm{ms} \pm 639 \mathrm{μs}\left({\color{gray}-3.946 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$49.7 \mathrm{ms} \pm 261 \mathrm{μs}\left({\color{gray}-4.782 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$76.9 \mathrm{ms} \pm 367 \mathrm{μs}\left({\color{gray}-4.577 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$57.5 \mathrm{ms} \pm 380 \mathrm{μs}\left({\color{gray}-2.463 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$65.1 \mathrm{ms} \pm 444 \mathrm{μs}\left({\color{gray}-2.162 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$66.7 \mathrm{ms} \pm 290 \mathrm{μs}\left({\color{gray}-4.544 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$66.9 \mathrm{ms} \pm 366 \mathrm{μs}\left({\color{gray}-3.640 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$140 \mathrm{ms} \pm 705 \mathrm{μs}\left({\color{gray}4.23 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$138 \mathrm{ms} \pm 459 \mathrm{μs}\left({\color{gray}0.175 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$40.3 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{lightgreen}-61.997 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$600 \mathrm{ms} \pm 1.32 \mathrm{ms}\left({\color{gray}3.85 \mathrm{\%}}\right) $$	Flame Graph