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.

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[cursor]

PR Summary

Medium Risk
Changes compile-time size/cardinality semantics for MIR aggregates and nested footprints; incorrect bounds could affect downstream planning, though behavior is heavily snapshot- and unit-tested.

Overview
Reworks dynamic MIR size estimation so aggregate RValues are handled by AggregateKind, not one generic “sum everything” rule.

Structs and tuples now treat the value as a single composite: cardinality 1, units = sum of each field’s materialized information (Footprint::materialize()). Lists use a join of per-element materialized units and set cardinality to the literal element count. Dicts join key+value materialized pair sizes and set cardinality to the pair count. Closures fold fn pointer and env into one scalar footprint; opaque aggregates keep summing raw footprints.

Adds materialize() (units × cardinality, with constant/affine cases), Footprint::one, Estimate::saturating_coeff_mul, InformationRange::saturating_mul_cardinality, and Eval::into_footprint. Fixes snapshots so struct/tuple no longer report cardinality 2 for two fields. New unit and UI snapshot tests cover lists, dicts, nested list-in-struct, and materialize edge cases.

^{Reviewed by Cursor Bugbot for commit c5bf4ee. Bugbot is set up for automated code reviews on this repo. Configure here.}

[indietyp]

• 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

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[Copilot]

Pull request overview

This PR updates HashQL MIR’s dynamic size-estimation analysis to be aggregate-kind aware, fixing the previously incorrect behavior where all aggregates were treated like collections (cardinality accumulating with operand count). It also introduces a Footprint::materialize() helper to collapse (units, cardinality) into a single “total information” estimate when embedding nested values inside composite aggregates.

Changes:

• Reworks dynamic aggregate evaluation to dispatch by AggregateKind (struct/tuple vs list vs dict vs closure vs opaque) with correct cardinality semantics.
• Adds Footprint::materialize(), Footprint::one(...), Estimate::saturating_coeff_mul, and InformationRange::saturating_mul_cardinality to support nested/typed aggregate estimation.
• Expands and updates tests + snapshots to validate corrected cardinalities and per-element/per-pair unit semantics.

Reviewed changes

Copilot reviewed 11 out of 11 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
libs/@local/hashql/mir/src/pass/analysis/size_estimation/dynamic.rs	Implements kind-specific aggregate footprint evaluation (struct/tuple scalarize, list/dict collection semantics, closure scalarize, opaque legacy behavior).
libs/@local/hashql/mir/src/pass/analysis/size_estimation/footprint.rs	Adds Footprint::one and Footprint::materialize() plus unit tests for the new materialization behavior.
libs/@local/hashql/mir/src/pass/analysis/size_estimation/estimate.rs	Adds Estimate::saturating_coeff_mul to support coefficient-wise multiplication used by materialize().
libs/@local/hashql/mir/src/pass/analysis/size_estimation/range.rs	Adds InformationRange::saturating_mul_cardinality and unit tests for range-level multiplication behavior.
libs/@local/hashql/mir/src/pass/analysis/size_estimation/tests.rs	Adds integration tests covering list/dict units semantics and tuple/struct cardinality fixes.
libs/@local/hashql/mir/tests/ui/pass/size-estimation/*.snap	Adds new snapshots and updates existing ones to reflect corrected cardinalities and new test coverage.

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[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	$$26.1 \mathrm{ms} \pm 168 \mathrm{μs}\left({\color{gray}0.353 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.39 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{gray}-3.319 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.4 \mathrm{ms} \pm 95.0 \mathrm{μs}\left({\color{gray}1.25 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.1 \mathrm{ms} \pm 318 \mathrm{μs}\left({\color{gray}-0.431 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.8 \mathrm{ms} \pm 126 \mathrm{μs}\left({\color{gray}-2.747 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$24.5 \mathrm{ms} \pm 186 \mathrm{μs}\left({\color{gray}0.904 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.9 \mathrm{ms} \pm 174 \mathrm{μs}\left({\color{gray}-0.001 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.72 \mathrm{ms} \pm 20.7 \mathrm{μs}\left({\color{gray}-4.077 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.5 \mathrm{ms} \pm 121 \mathrm{μs}\left({\color{gray}0.085 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.71 \mathrm{ms} \pm 24.0 \mathrm{μs}\left({\color{gray}-1.594 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.96 \mathrm{ms} \pm 11.1 \mathrm{μs}\left({\color{gray}-1.729 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.30 \mathrm{ms} \pm 16.1 \mathrm{μs}\left({\color{gray}-1.683 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.04 \mathrm{ms} \pm 26.9 \mathrm{μs}\left({\color{gray}-2.262 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.53 \mathrm{ms} \pm 20.5 \mathrm{μs}\left({\color{gray}-0.818 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.10 \mathrm{ms} \pm 25.3 \mathrm{μs}\left({\color{gray}-1.367 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.38 \mathrm{ms} \pm 26.4 \mathrm{μs}\left({\color{gray}0.636 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.46 \mathrm{ms} \pm 19.0 \mathrm{μs}\left({\color{gray}-1.777 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.04 \mathrm{ms} \pm 22.9 \mathrm{μs}\left({\color{gray}-0.562 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.63 \mathrm{ms} \pm 14.5 \mathrm{μs}\left({\color{gray}0.867 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.54 \mathrm{ms} \pm 12.5 \mathrm{μs}\left({\color{gray}1.51 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.62 \mathrm{ms} \pm 22.3 \mathrm{μs}\left({\color{gray}2.57 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.89 \mathrm{ms} \pm 14.5 \mathrm{μs}\left({\color{gray}2.05 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.70 \mathrm{ms} \pm 12.7 \mathrm{μs}\left({\color{gray}1.60 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.88 \mathrm{ms} \pm 16.7 \mathrm{μs}\left({\color{gray}1.01 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.02 \mathrm{ms} \pm 21.2 \mathrm{μs}\left({\color{gray}1.87 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.77 \mathrm{ms} \pm 13.9 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$3.02 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{gray}3.75 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.38 \mathrm{ms} \pm 18.3 \mathrm{μs}\left({\color{gray}-0.782 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}1.02 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.30 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}0.641 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.33 \mathrm{ms} \pm 16.3 \mathrm{μs}\left({\color{gray}2.46 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.03 \mathrm{ms} \pm 26.9 \mathrm{μs}\left({\color{gray}2.24 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.31 \mathrm{ms} \pm 16.3 \mathrm{μs}\left({\color{gray}1.96 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$54.9 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}-0.399 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$46.8 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{gray}0.830 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$51.9 \mathrm{ms} \pm 332 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$44.9 \mathrm{ms} \pm 216 \mathrm{μs}\left({\color{gray}0.588 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$64.6 \mathrm{ms} \pm 401 \mathrm{μs}\left({\color{gray}4.72 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$63.9 \mathrm{ms} \pm 318 \mathrm{μs}\left({\color{gray}2.29 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$57.3 \mathrm{ms} \pm 290 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$105 \mathrm{ms} \pm 454 \mathrm{μs}\left({\color{gray}3.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$47.2 \mathrm{ms} \pm 264 \mathrm{μs}\left({\color{gray}2.08 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$274 \mathrm{ms} \pm 932 \mathrm{μs}\left({\color{lightgreen}-5.664 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$19.6 \mathrm{ms} \pm 92.4 \mathrm{μs}\left({\color{gray}0.702 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$20.3 \mathrm{ms} \pm 131 \mathrm{μs}\left({\color{gray}0.663 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.3 \mathrm{ms} \pm 78.4 \mathrm{μs}\left({\color{gray}0.341 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$19.6 \mathrm{ms} \pm 124 \mathrm{μs}\left({\color{gray}0.888 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.4 \mathrm{ms} \pm 157 \mathrm{μs}\left({\color{gray}2.35 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$19.6 \mathrm{ms} \pm 93.4 \mathrm{μs}\left({\color{gray}2.70 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.4 \mathrm{ms} \pm 154 \mathrm{μs}\left({\color{gray}-0.321 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$19.6 \mathrm{ms} \pm 95.2 \mathrm{μs}\left({\color{gray}1.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$20.3 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}1.43 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$26.2 \mathrm{ms} \pm 179 \mathrm{μs}\left({\color{gray}-2.852 \mathrm{\%}}\right) $$	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	$$34.6 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}2.22 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$34.9 \mathrm{ms} \pm 279 \mathrm{μs}\left({\color{gray}0.820 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$33.4 \mathrm{ms} \pm 257 \mathrm{μs}\left({\color{lightgreen}-8.816 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$34.7 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}-2.663 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$35.3 \mathrm{ms} \pm 313 \mathrm{μs}\left({\color{gray}2.94 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$35.2 \mathrm{ms} \pm 337 \mathrm{μs}\left({\color{gray}-4.077 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$34.8 \mathrm{ms} \pm 319 \mathrm{μs}\left({\color{gray}0.160 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$34.9 \mathrm{ms} \pm 288 \mathrm{μs}\left({\color{gray}0.729 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$35.4 \mathrm{ms} \pm 271 \mathrm{μs}\left({\color{gray}-1.711 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.52 \mathrm{ms} \pm 41.2 \mathrm{μs}\left({\color{gray}0.284 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$93.2 \mathrm{ms} \pm 566 \mathrm{μs}\left({\color{gray}1.58 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$147 \mathrm{ms} \pm 599 \mathrm{μs}\left({\color{gray}0.703 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$97.0 \mathrm{ms} \pm 637 \mathrm{μs}\left({\color{gray}-0.306 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$109 \mathrm{ms} \pm 654 \mathrm{μs}\left({\color{gray}-0.514 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$116 \mathrm{ms} \pm 666 \mathrm{μs}\left({\color{gray}0.664 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$127 \mathrm{ms} \pm 789 \mathrm{μs}\left({\color{gray}1.38 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$102 \mathrm{ms} \pm 766 \mathrm{μs}\left({\color{gray}-0.719 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$133 \mathrm{ms} \pm 876 \mathrm{μs}\left({\color{gray}0.529 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$109 \mathrm{ms} \pm 527 \mathrm{μs}\left({\color{gray}-0.280 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 715 \mathrm{μs}\left({\color{gray}0.101 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$120 \mathrm{ms} \pm 606 \mathrm{μs}\left({\color{gray}-0.806 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 649 \mathrm{μs}\left({\color{gray}0.038 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$183 \mathrm{ms} \pm 1.95 \mathrm{ms}\left({\color{gray}-3.163 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$149 \mathrm{ms} \pm 600 \mathrm{μs}\left({\color{lightgreen}-26.082 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$41.2 \mathrm{ms} \pm 206 \mathrm{μs}\left({\color{gray}2.64 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$565 \mathrm{ms} \pm 982 \mathrm{μs}\left({\color{gray}-1.431 \mathrm{\%}}\right) $$	Flame Graph