BE-525: HashQL: Remove implicit type-widening from Integer operations, and make comparison size aware

[indietyp]

🌟 What is the purpose of this PR?

Integer arithmetic in the MIR interpreter previously silently promoted overflowing i128 operations to floating-point results via a Numeric enum. This was semantically incorrect: the language specifies arbitrary-precision integers, and silently widening to f64 loses precision and masks errors. This PR replaces that silent promotion with explicit overflow detection, surfacing a user-facing IntegerOverflow runtime error when addition, subtraction, or negation exceeds the 128-bit range supported by the interpreter.

As a side effect, the Int type's equality, ordering, and hashing semantics are corrected so that booleans (1-bit integers) are treated as a distinct type from integers (128-bit), rather than comparing equal to numeric integers with the same value. This restores type-level correctness for Bool vs Int comparisons throughout the value layer.

🔍 What does this change?

• Adds a RuntimeError::IntegerOverflow variant with an operation field, and a corresponding integer_overflow diagnostic emitted under the RuntimeLimit category with a note that the interpreter supports up to 128-bit integers.
• Replaces the Neg, Add<Int>, and Sub<Int> operator impls on Int (which returned Numeric and silently promoted to f64 on overflow) with checked_neg, checked_add, and checked_sub methods that return Option<Int>.
• Removes the Numeric enum entirely, along with the From<Numeric> for Value conversion and all associated forwarding impls.
• Updates the binary and unary operation dispatch in the interpreter runtime to use the new checked methods and return RuntimeError::IntegerOverflow on None.
• Fixes Int's PartialEq, Ord, and Hash implementations to include the size field, so that Int::from(true) (1-bit) and Int::from(1_i32) (128-bit) are no longer considered equal.
• Fixes PartialEq<Int> for Num, PartialOrd<Int> for Num, and their reverses to return false/None when the Int is a boolean, since booleans are not a subtype of Number.
• Updates Value::Ord to guard the cross-type Integer/Number ordering arms with !this.is_bool() / !other.is_bool().
• Updates InstSimplifyVisitor::eval_un_op to return Option<Int> and skip constant folding for negation of i128::MIN.
• Adds interpreter tests for addition, subtraction, and negation overflow, and an inst_simplify test confirming that neg(i128::MIN) is not folded.
• Updates all unit tests in int.rs and mod.rs to reflect the corrected bool/int distinction.

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?

• New interpreter tests: integer_add_overflow, integer_sub_overflow, integer_neg_overflow — each confirms a RuntimeLimit diagnostic is produced.
• New inst_simplify test: const_fold_neg_overflow — confirms that negation of i128::MIN is left unfolded.
• Updated unit tests in Int and Value covering corrected equality, ordering, hashing, and cross-type Num/Int comparisons.

❓ How to test this?

1. Run cargo test -p hashql-mir and confirm all tests pass.
2. Verify that the new snapshot const_fold_neg_overflow.snap matches the expected unfold output.
3. Confirm that expressions producing integer overflow at runtime emit a diagnostic with category RuntimeLimit and a message referencing the 128-bit limit.

[vercel]

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Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview, Comment	May 29, 2026 4:41pm

2 Skipped Deployments

Project	Deployment	Actions	Updated (UTC)
hashdotdesign-tokens	Ignored	Preview	May 29, 2026 4:41pm
petrinaut	Skipped		May 29, 2026 4:41pm

[cursor]

PR Summary

Medium Risk
Changes core interpreter arithmetic and value equality semantics; well-typed programs may see new overflow errors or different comparison/fold behavior at bool/int boundaries.

Overview
The MIR interpreter stops silently promoting overflowing i128 integer math to f64. Integer add, sub, and neg now use checked operations and surface a user-facing IntegerOverflow runtime error (under RuntimeLimit) when results fall outside the supported 128-bit range.

The Numeric wrapper and overflow-to-float paths are removed. Int equality, ordering, and hashing now treat booleans (1-bit) and integers (128-bit) as distinct even when the numeric bits match; Num cross-comparisons with bools are adjusted accordingly, and Value / inst_simplify constant folding follows size-aware == / != (including folding bool-vs-int comparisons to false / true where appropriate).

^{Reviewed by Cursor Bugbot for commit bf3b42f. Bugbot is set up for automated code reviews on this repo. Configure here.}

[indietyp]

• BE-537: hgraph: REST /hashql endpoint with E2E tests #8827
• BE-513: HashQL: Rework dynamic aggregate size estimation #8697
• BE-512: HashQL: Allow cross-backend transitions for SwitchInt terminators #8696
• BE-524: HashQL: Remove island schedule module #8694
• BE-525: HashQL: Remove implicit type-widening from Integer operations, and make comparison size aware #8693 👈 (View in Graphite)
• BE-515: HashQL: Remove OFFSET 0 materialization fence from generated query #8650
• BE-514: HashQL: implement iterative adjustment passes in placement solver #8635
• BE-500, BE-501: HashQL: Unify mixed parameter resolution in data dependency analysis #8607
• BE-494: HashQL: Loop-breaker selection for recursive inlining #8600
• BE-482: HashQL: Remove logical not from MIR and fix postgres boolean lowering #8595
• BE-474: HashQL: Take into account terminator target eligibility in execution backend placement #8587
• BE-455: HashQL: Introduce evaluation orchestrator #8586
• BE-523: HashQL: Suspendable interpreter with structured values and input system #8675
• BE-522: HashQL: Expand stdlib type system with temporal types #8674
• BE-306: HashQL: PostgreSQL translation #8526
• BE-457: HashQL: MIR execution pipeline extensions for postgres compilation #8525
• BE-456: HashQL: Entity type model enrichment and graph store query extensions #8524
• BE-429: HashQL: Add island dependency graph with data requirement resolution #8501
• BE-428: HashQL: Simplify traversal tracking to path recording #8498
• BE-434: HashQL: Refactor entity storage path resolution with centralized schema mapping #8496
• BE-427: Add hashql-macros crate with procedural macros for ID types and symbols #8495
• main

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

[augmentcode]

🤖 Augment PR Summary

Summary: This PR fixes HashQL MIR interpreter integer semantics by removing implicit type-widening on overflow and making comparisons size-aware.

Changes:

• Adds RuntimeError::IntegerOverflow plus a new RuntimeLimit diagnostic for i128-range overflows.
• Updates runtime dispatch for Add/Sub/Neg on Int to use checked arithmetic and surface overflow as an explicit runtime error.
• Removes the Numeric enum and associated conversion plumbing that previously promoted overflowing Int operations into f64.
• Makes Int equality/ordering/hashing include the size field so Bool (1-bit) and Int (128-bit) are distinct at the value layer.
• Adjusts Num↔Int cross-type comparison to treat booleans as neither equal nor orderable with Num.
• Updates Value::Ord to only apply numeric ordering between Integer/Number for non-bool integers.
• Updates constant folding to avoid folding neg(i128::MIN) and adds interpreter + inst_simplify regression tests/snapshots for overflow cases.

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[augmentcode]

Review completed. 1 suggestion posted.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 59.00%. Comparing base (b9e62e0) to head (bf3b42f).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #8693      +/-   ##
==========================================
- Coverage   59.02%   59.00%   -0.03%     
==========================================
  Files        1341     1341              
  Lines      129299   129178     -121     
  Branches     5823     5803      -20     
==========================================
- Hits        76317    76219      -98     
+ Misses      52085    52074      -11     
+ Partials      897      885      -12     

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[codspeed-hq]

Merging this PR will not alter performance

✅ 24 untouched benchmarks
⏩ 56 skipped benchmarks¹

---

_{Comparing bm/be-525-hashql-remove-implicit-type-widening-into-num-for-int (bf3b42f) with main (73a14b4)²}

Footnotes

1. 56 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

2. No successful run was found on main (b9e62e0) during the generation of this report, so 73a14b4 was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

[graphite-app]

Merge activity

• May 29, 4:34 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	$$19.6 \mathrm{ms} \pm 114 \mathrm{μs}\left({\color{gray}-0.143 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.64 \mathrm{ms} \pm 11.1 \mathrm{μs}\left({\color{gray}0.011 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$9.57 \mathrm{ms} \pm 66.1 \mathrm{μs}\left({\color{gray}-1.723 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$33.2 \mathrm{ms} \pm 218 \mathrm{μs}\left({\color{gray}-0.212 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$11.9 \mathrm{ms} \pm 93.4 \mathrm{μs}\left({\color{red}7.26 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$19.9 \mathrm{ms} \pm 139 \mathrm{μs}\left({\color{red}6.14 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$20.1 \mathrm{ms} \pm 113 \mathrm{μs}\left({\color{gray}-0.976 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.82 \mathrm{ms} \pm 11.7 \mathrm{μs}\left({\color{gray}-1.779 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$10.4 \mathrm{ms} \pm 68.6 \mathrm{μs}\left({\color{gray}-4.002 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$2.88 \mathrm{ms} \pm 15.5 \mathrm{μs}\left({\color{gray}0.225 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.29 \mathrm{ms} \pm 9.97 \mathrm{μs}\left({\color{gray}-0.943 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$2.57 \mathrm{ms} \pm 12.5 \mathrm{μs}\left({\color{gray}0.085 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$3.92 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{gray}-0.393 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.72 \mathrm{ms} \pm 12.0 \mathrm{μs}\left({\color{gray}-1.549 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.15 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{gray}-0.308 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$3.33 \mathrm{ms} \pm 17.5 \mathrm{μs}\left({\color{gray}-0.408 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.66 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{gray}-0.314 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.10 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}-0.235 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.00 \mathrm{ms} \pm 9.51 \mathrm{μs}\left({\color{gray}0.349 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$1.94 \mathrm{ms} \pm 11.0 \mathrm{μs}\left({\color{gray}1.45 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$1.97 \mathrm{ms} \pm 7.41 \mathrm{μs}\left({\color{gray}-1.834 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.19 \mathrm{ms} \pm 12.1 \mathrm{μs}\left({\color{gray}-0.618 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.03 \mathrm{ms} \pm 9.57 \mathrm{μs}\left({\color{gray}0.987 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.18 \mathrm{ms} \pm 8.94 \mathrm{μs}\left({\color{gray}-0.203 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.30 \mathrm{ms} \pm 12.2 \mathrm{μs}\left({\color{gray}-0.086 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.10 \mathrm{ms} \pm 9.65 \mathrm{μs}\left({\color{gray}-0.286 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.28 \mathrm{ms} \pm 9.49 \mathrm{μs}\left({\color{gray}-0.998 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$2.59 \mathrm{ms} \pm 13.1 \mathrm{μs}\left({\color{gray}-0.165 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.28 \mathrm{ms} \pm 10.8 \mathrm{μs}\left({\color{gray}0.230 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$2.52 \mathrm{ms} \pm 10.9 \mathrm{μs}\left({\color{gray}-0.788 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$2.57 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{gray}0.369 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.27 \mathrm{ms} \pm 10.5 \mathrm{μs}\left({\color{gray}-0.152 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$2.53 \mathrm{ms} \pm 10.9 \mathrm{μs}\left({\color{gray}-0.481 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$42.0 \mathrm{ms} \pm 164 \mathrm{μs}\left({\color{gray}-0.331 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$36.0 \mathrm{ms} \pm 165 \mathrm{μs}\left({\color{gray}-0.253 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$39.8 \mathrm{ms} \pm 172 \mathrm{μs}\left({\color{gray}0.568 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$34.9 \mathrm{ms} \pm 145 \mathrm{μs}\left({\color{gray}-0.583 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$48.9 \mathrm{ms} \pm 182 \mathrm{μs}\left({\color{gray}-0.859 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$46.9 \mathrm{ms} \pm 183 \mathrm{μs}\left({\color{gray}-4.310 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$43.0 \mathrm{ms} \pm 157 \mathrm{μs}\left({\color{gray}-2.892 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$74.7 \mathrm{ms} \pm 377 \mathrm{μs}\left({\color{lightgreen}-6.530 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$35.6 \mathrm{ms} \pm 155 \mathrm{μs}\left({\color{lightgreen}-7.231 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$224 \mathrm{ms} \pm 627 \mathrm{μs}\left({\color{gray}-0.260 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.8 \mathrm{ms} \pm 111 \mathrm{μs}\left({\color{gray}-3.235 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$15.1 \mathrm{ms} \pm 59.1 \mathrm{μs}\left({\color{lightgreen}-6.512 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.5 \mathrm{ms} \pm 76.8 \mathrm{μs}\left({\color{gray}-2.975 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$14.8 \mathrm{ms} \pm 65.9 \mathrm{μs}\left({\color{gray}-3.094 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$19.5 \mathrm{ms} \pm 86.5 \mathrm{μs}\left({\color{gray}-0.989 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.8 \mathrm{ms} \pm 56.5 \mathrm{μs}\left({\color{gray}-4.344 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.9 \mathrm{ms} \pm 77.6 \mathrm{μs}\left({\color{gray}-4.071 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$15.0 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}-3.790 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.5 \mathrm{ms} \pm 82.2 \mathrm{μs}\left({\color{gray}-2.996 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$20.3 \mathrm{ms} \pm 114 \mathrm{μs}\left({\color{gray}-1.572 \mathrm{\%}}\right) $$	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	$$26.0 \mathrm{ms} \pm 200 \mathrm{μs}\left({\color{gray}-3.262 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$26.2 \mathrm{ms} \pm 182 \mathrm{μs}\left({\color{gray}-2.724 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$27.5 \mathrm{ms} \pm 226 \mathrm{μs}\left({\color{gray}2.17 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$27.5 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{gray}4.50 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$26.4 \mathrm{ms} \pm 222 \mathrm{μs}\left({\color{gray}1.07 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$27.4 \mathrm{ms} \pm 196 \mathrm{μs}\left({\color{gray}2.10 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$26.6 \mathrm{ms} \pm 209 \mathrm{μs}\left({\color{gray}-0.668 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$26.4 \mathrm{ms} \pm 181 \mathrm{μs}\left({\color{gray}-1.682 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$26.8 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{gray}0.779 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$6.49 \mathrm{ms} \pm 31.2 \mathrm{μs}\left({\color{gray}-0.547 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$70.8 \mathrm{ms} \pm 433 \mathrm{μs}\left({\color{gray}-2.131 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$117 \mathrm{ms} \pm 541 \mathrm{μs}\left({\color{gray}3.79 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$75.9 \mathrm{ms} \pm 372 \mathrm{μs}\left({\color{gray}-2.808 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$84.9 \mathrm{ms} \pm 381 \mathrm{μs}\left({\color{gray}-2.449 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$91.0 \mathrm{ms} \pm 418 \mathrm{μs}\left({\color{gray}1.23 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$99.4 \mathrm{ms} \pm 524 \mathrm{μs}\left({\color{gray}2.25 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$79.0 \mathrm{ms} \pm 343 \mathrm{μs}\left({\color{gray}0.710 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$105 \mathrm{ms} \pm 354 \mathrm{μs}\left({\color{gray}3.43 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$85.3 \mathrm{ms} \pm 356 \mathrm{μs}\left({\color{gray}0.648 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$92.9 \mathrm{ms} \pm 336 \mathrm{μs}\left({\color{gray}1.99 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$94.3 \mathrm{ms} \pm 408 \mathrm{μs}\left({\color{gray}1.80 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$95.5 \mathrm{ms} \pm 432 \mathrm{μs}\left({\color{gray}2.33 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$130 \mathrm{ms} \pm 1.87 \mathrm{ms}\left({\color{lightgreen}-9.170 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$117 \mathrm{ms} \pm 387 \mathrm{μs}\left({\color{lightgreen}-24.427 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$30.8 \mathrm{ms} \pm 139 \mathrm{μs}\left({\color{lightgreen}-63.186 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$454 \mathrm{ms} \pm 970 \mathrm{μs}\left({\color{lightgreen}-5.555 \mathrm{\%}}\right) $$	Flame Graph