BE-513: HashQL: Rework dynamic aggregate size estimation

[indietyp]

🌟 What is the purpose of this PR?

The size estimation analysis previously treated all aggregate kinds (structs, tuples, lists, dicts, closures) identically — summing operand footprints and accumulating cardinality as if every aggregate were a flat collection. This was incorrect: a struct or tuple is a single composite value with cardinality 1, while a list or dict is a true collection whose cardinality equals its element count.

This PR introduces type-aware aggregate footprint evaluation. Structs and tuples now correctly report cardinality 1 with units equal to the sum of their fields' materialized sizes. Lists report per-element units (joined across elements) with cardinality equal to the element count. Dicts compute per-pair units (key + value combined) with cardinality equal to the pair count. Closures combine their function pointer and environment footprints into a single scalar value.

To support this, a materialize() method is introduced on Footprint that collapses a footprint's (units, cardinality) pair into a single total information estimate. This is needed when a value with its own cardinality (e.g. a list) is embedded as a field of a composite type — the field's contribution to the parent's units must account for the full information content of the nested value, not just its per-element size.

🔍 What does this change?

• Replaces the single generic RValue::Aggregate handler in eval_rvalue with a dedicated eval_rvalue_aggregate method that dispatches on AggregateKind.
• Struct/Tuple: sums the materialize()d footprints of all operands and sets cardinality to 1.
• List: joins per-element materialized units and sets cardinality to the literal element count.
• Dict: joins per-pair materialized units (key + value combined via saturating_mul_add) and sets cardinality to the pair count.
• Closure: combines function pointer and environment footprints into a single scalar (cardinality 1).
• Opaque: retains the previous behaviour of summing raw footprints.
• Adds Footprint::materialize() which multiplies units by cardinality to produce a total information estimate, with case-specific handling for constant×constant (exact), affine units×constant cardinality (scale coefficients by cardinality upper bound), and affine×affine (element-wise coefficient multiplication as a linear under-approximation).
• Adds Footprint::one(units) constructor for footprints with cardinality exactly 1.
• Adds Estimate::saturating_coeff_mul for element-wise coefficient multiplication between two estimates.
• Adds InformationRange::saturating_mul_cardinality for range-level multiplication of information by cardinality, saturating to unbounded on overflow.
• Adds Eval::into_footprint as a consuming counterpart to Eval::as_ref.
• Fixes the snapshot for struct_aggregate_sums_operands and tuple_aggregate_sums_operands, which previously reported cardinality 2 for a two-field struct/tuple; both now correctly report cardinality 1.

🛡 What tests cover this?

• New integration tests: list_aggregate_per_element_units, dict_aggregate_per_pair_units, tuple_many_fields_cardinality_one, and struct_materializes_list_parameter, each with corresponding snapshot files.
• New unit tests in footprint.rs covering all four materialize() branches: scalar identity, constant×constant, affine units×constant cardinality, constant units×affine cardinality, and both-affine same-parameter.
• New unit tests in range.rs covering saturating_mul_cardinality: exact multiplication, identity by 1, empty inputs, unbounded cardinality, and overflow to unbounded.

❓ How to test this?

1. Run cargo test -p hashql-mir and confirm all tests pass.
2. Review the new and updated snapshots under libs/@local/hashql/mir/tests/ui/pass/size-estimation/ to verify the reported units and cardinality values match the expected semantics for each aggregate kind.

[vercel]

The latest updates on your projects. Learn more about Vercel for GitHub.

Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview, Comment	May 25, 2026 2:08pm

2 Skipped Deployments

Project	Deployment	Actions	Updated (UTC)
hashdotdesign-tokens	Ignored	Preview	May 25, 2026 2:08pm
petrinaut	Skipped		May 25, 2026 2:08pm

[cursor]

PR Summary

Medium Risk
Changes compile-time size bounds used by analysis passes; incorrect estimates could affect downstream optimizations or limits, but scope is localized to size_estimation with broad snapshot/unit test coverage.

Overview
Dynamic size estimation no longer treats every MIR aggregate the same. eval_rvalue_aggregate dispatches on AggregateKind: structs/tuples sum materialized operand information and force cardinality 1; lists join per-element materialized units and set cardinality to the literal element count; dicts join per-pair (key+value) materialized units with cardinality equal to pair count; closures combine fn ptr and env into a single scalar-sized footprint; opaque aggregates still sum raw footprints.

Supporting this, Footprint::materialize() folds (units, cardinality) into one information estimate (exact constant×constant, scaled affine×constant, linear under-approx for affine×affine), plus Footprint::one, Estimate::saturating_coeff_mul, InformationRange::saturating_mul_cardinality, and Eval::into_footprint. Tuple/struct snapshots that wrongly used field count as cardinality are corrected; new integration and unit tests lock in list/dict/tuple/struct-nested-list behavior.

^{Reviewed by Cursor Bugbot for commit 7cf9331. Bugbot is set up for automated code reviews on this repo. Configure here.}

[indietyp]

Warning

This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

• BE-513: HashQL: Rework dynamic aggregate size estimation #8697 👈 (View in Graphite)
• 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
• 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.

[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	$$25.9 \mathrm{ms} \pm 129 \mathrm{μs}\left({\color{gray}0.140 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.45 \mathrm{ms} \pm 18.7 \mathrm{μs}\left({\color{gray}0.250 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.2 \mathrm{ms} \pm 66.8 \mathrm{μs}\left({\color{gray}-2.014 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.4 \mathrm{ms} \pm 246 \mathrm{μs}\left({\color{gray}-1.811 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.2 \mathrm{ms} \pm 83.4 \mathrm{μs}\left({\color{gray}-0.606 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.3 \mathrm{ms} \pm 157 \mathrm{μs}\left({\color{gray}-2.113 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.8 \mathrm{ms} \pm 218 \mathrm{μs}\left({\color{gray}-0.072 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.75 \mathrm{ms} \pm 18.9 \mathrm{μs}\left({\color{gray}0.261 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.1 \mathrm{ms} \pm 90.0 \mathrm{μs}\left({\color{gray}-2.505 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.74 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{gray}-2.388 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 11.3 \mathrm{μs}\left({\color{gray}-3.322 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.37 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-2.624 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.09 \mathrm{ms} \pm 24.5 \mathrm{μs}\left({\color{gray}-4.252 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.58 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{gray}-2.150 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.13 \mathrm{ms} \pm 16.7 \mathrm{μs}\left({\color{gray}-2.559 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.39 \mathrm{ms} \pm 21.6 \mathrm{μs}\left({\color{gray}0.338 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.42 \mathrm{ms} \pm 12.5 \mathrm{μs}\left({\color{gray}-1.590 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.12 \mathrm{ms} \pm 17.3 \mathrm{μs}\left({\color{gray}0.481 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.72 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{gray}-3.158 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.70 \mathrm{ms} \pm 10.7 \mathrm{μs}\left({\color{gray}-2.349 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.81 \mathrm{ms} \pm 11.9 \mathrm{μs}\left({\color{gray}-1.100 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$3.07 \mathrm{ms} \pm 11.7 \mathrm{μs}\left({\color{gray}-2.312 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.89 \mathrm{ms} \pm 13.1 \mathrm{μs}\left({\color{gray}-3.391 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$3.17 \mathrm{ms} \pm 10.7 \mathrm{μs}\left({\color{gray}-2.635 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.10 \mathrm{ms} \pm 15.3 \mathrm{μs}\left({\color{gray}0.209 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.81 \mathrm{ms} \pm 11.9 \mathrm{μs}\left({\color{gray}0.911 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.95 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-0.534 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.52 \mathrm{ms} \pm 19.7 \mathrm{μs}\left({\color{gray}-0.375 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.10 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}0.658 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.30 \mathrm{ms} \pm 16.0 \mathrm{μs}\left({\color{gray}-0.153 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.46 \mathrm{ms} \pm 19.2 \mathrm{μs}\left({\color{gray}0.933 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.08 \mathrm{ms} \pm 12.2 \mathrm{μs}\left({\color{gray}-1.025 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.34 \mathrm{ms} \pm 19.5 \mathrm{μs}\left({\color{gray}-0.961 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$44.9 \mathrm{ms} \pm 159 \mathrm{μs}\left({\color{gray}-0.943 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$82.1 \mathrm{ms} \pm 349 \mathrm{μs}\left({\color{gray}-1.345 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$49.7 \mathrm{ms} \pm 206 \mathrm{μs}\left({\color{gray}-4.467 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$51.7 \mathrm{ms} \pm 402 \mathrm{μs}\left({\color{gray}-1.778 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$61.0 \mathrm{ms} \pm 320 \mathrm{μs}\left({\color{gray}-3.859 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$46.1 \mathrm{ms} \pm 165 \mathrm{μs}\left({\color{gray}-2.942 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$418 \mathrm{ms} \pm 922 \mathrm{μs}\left({\color{gray}2.89 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$99.1 \mathrm{ms} \pm 444 \mathrm{μs}\left({\color{gray}-4.292 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$82.1 \mathrm{ms} \pm 279 \mathrm{μs}\left({\color{gray}-4.075 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$283 \mathrm{ms} \pm 677 \mathrm{μs}\left({\color{gray}-2.868 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$19.4 \mathrm{ms} \pm 77.4 \mathrm{μs}\left({\color{lightgreen}-6.205 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$20.0 \mathrm{ms} \pm 79.4 \mathrm{μs}\left({\color{gray}-4.623 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.1 \mathrm{ms} \pm 93.7 \mathrm{μs}\left({\color{lightgreen}-5.245 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$19.8 \mathrm{ms} \pm 105 \mathrm{μs}\left({\color{gray}-4.935 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$24.7 \mathrm{ms} \pm 110 \mathrm{μs}\left({\color{gray}-2.978 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$19.2 \mathrm{ms} \pm 68.4 \mathrm{μs}\left({\color{lightgreen}-6.265 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.2 \mathrm{ms} \pm 83.0 \mathrm{μs}\left({\color{lightgreen}-5.926 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$19.3 \mathrm{ms} \pm 85.4 \mathrm{μs}\left({\color{gray}-4.160 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$19.8 \mathrm{ms} \pm 124 \mathrm{μs}\left({\color{lightgreen}-6.563 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$25.2 \mathrm{ms} \pm 169 \mathrm{μs}\left({\color{lightgreen}-7.918 \mathrm{\%}}\right) $$	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	$$34.0 \mathrm{ms} \pm 258 \mathrm{μs}\left({\color{gray}-1.856 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$32.0 \mathrm{ms} \pm 229 \mathrm{μs}\left({\color{lightgreen}-12.385 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$33.3 \mathrm{ms} \pm 262 \mathrm{μs}\left({\color{gray}-4.687 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$32.8 \mathrm{ms} \pm 275 \mathrm{μs}\left({\color{lightgreen}-10.776 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$33.5 \mathrm{ms} \pm 255 \mathrm{μs}\left({\color{gray}-1.767 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$33.5 \mathrm{ms} \pm 247 \mathrm{μs}\left({\color{gray}0.287 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$33.2 \mathrm{ms} \pm 303 \mathrm{μs}\left({\color{lightgreen}-10.137 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$33.9 \mathrm{ms} \pm 240 \mathrm{μs}\left({\color{gray}-0.465 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$34.3 \mathrm{ms} \pm 303 \mathrm{μs}\left({\color{gray}-0.896 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.34 \mathrm{ms} \pm 30.9 \mathrm{μs}\left({\color{gray}-0.701 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$89.3 \mathrm{ms} \pm 307 \mathrm{μs}\left({\color{gray}-0.651 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$143 \mathrm{ms} \pm 622 \mathrm{μs}\left({\color{gray}2.38 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$96.2 \mathrm{ms} \pm 400 \mathrm{μs}\left({\color{gray}-0.816 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$105 \mathrm{ms} \pm 397 \mathrm{μs}\left({\color{gray}0.309 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$113 \mathrm{ms} \pm 409 \mathrm{μs}\left({\color{gray}0.302 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$124 \mathrm{ms} \pm 1.18 \mathrm{ms}\left({\color{gray}4.09 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$100 \mathrm{ms} \pm 380 \mathrm{μs}\left({\color{gray}-1.262 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$130 \mathrm{ms} \pm 430 \mathrm{μs}\left({\color{gray}1.88 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$107 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}-0.819 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$115 \mathrm{ms} \pm 379 \mathrm{μs}\left({\color{gray}-0.601 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$117 \mathrm{ms} \pm 385 \mathrm{μs}\left({\color{gray}0.459 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$118 \mathrm{ms} \pm 464 \mathrm{μs}\left({\color{gray}1.11 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$146 \mathrm{ms} \pm 426 \mathrm{μs}\left({\color{red}6.26 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$157 \mathrm{ms} \pm 425 \mathrm{μs}\left({\color{red}6.08 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$74.5 \mathrm{ms} \pm 3.45 \mathrm{ms}\left({\color{lightgreen}-31.040 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$540 \mathrm{ms} \pm 760 \mathrm{μs}\left({\color{lightgreen}-10.835 \mathrm{\%}}\right) $$	Flame Graph