BE-618: Rank semantic searches on a quantized embedding column with per-policy-branch reads

[TimDiekmann]

🌟 What is the purpose of this PR?

Top-nav semantic search took 16s+ locally (BE-618). The generic filter path sorted all entities by exact cosine distance before cutting to the limit, and the cross-table permission disjunction forced the planner to materialize the whole visible set.

This PR makes the search index-driven: entities are ranked on a new binary-quantized embedding column via a partial HNSW index, permission policies are decomposed into one conjunctive branch per permit (each individually plannable), and the candidates are re-scored against the full vector before hydration. Measurements and query plans are documented in BE-618.

🔗 Related links

• BE-618 — the investigation
• BE-735 — follow-up merging the per-branch reads into one statement (stacked PR, to be merged together with this one)
• BE-734 — follow-up collapsing structurally identical web permits
• BE-732 — production embedding backfill (only 31k of 726k prod entities have embeddings)

🚫 Blocked by

• Nothing. The BE-735 follow-up PR is stacked on this branch and follows it into main.

🔍 What does this change?

Commit 1 — quantized ranking (2a1ab997):

• Migration V57: generated embedding_bits bit(3072) column (binary_quantize, 392B inline vs 12kB TOASTed vector) on entity_embeddings and entity_type_embeddings, plus a partial HNSW index (bit_hamming_ops, WHERE property IS NULL) on entities; shadow migrations updated
• SQL-AST: BinaryOperator::HammingDistance (<~>), Function::BinaryQuantize, PostgresType::Bit
• SelectCompiler::rank_by_quantized_distance + restrict_embedding_property: ranks on the quantized column with an INNER embeddings join, forbids cursors, overfetches limit × 4 candidates
• search_entities / search_entity_types: ranked key-read + exact <=> rerank with the distance threshold, then hydration with rank restore, in one REPEATABLE READ transaction; SET LOCAL hnsw.ef_search sized to the candidate pool with iterative_scan = relaxed_order
• Filter::CosineDistance removed entirely (compile arm, error variants, Distance pseudo-columns, keys-first gate); snapshot restore switched to explicit column lists (generated columns reject INSERT ... SELECT *)

Commit 2 — policy branches (HEAD):

• Filter::for_policy_branches: splits the permit disjunction at construction (no tree parsing), one branch per permit with all forbids conjoined; duplicate and unmatchable permits produce no branch; for_policies re-expressed over the same parts extraction with byte-identical output
• search_entities_impl moved to knowledge/entity/search.rs and restructured: one candidate key-read per branch, union-dedup, exact rerank over unnest arrays with deterministic tie-breaking, divergence warning if hydration disagrees with the ranking, branch count recorded on the tracing span
• New compile goldens (branch shape, end-to-end branch split) and a property test asserting the branch union selects the same entities as the combined filter across 144 policy configurations

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

⚠️ Known issues

• Behaviour change: results were previously exact over all entities; they are now approximate-then-exact — an entity whose quantized rank is more than limit × 4 positions away from its exact rank can differ. The binary-quantization recall has not been measured yet.
• The per-branch reads run sequentially (N+3 round trips per search) until the stacked BE-735 PR lands.
• The candidate pool counts rows duplicated by to-many filter joins, so the effective pool can fall below limit × 4 and miss a nearer neighbour. Deduplicating before the limit is part of BE-735, which rewrites the reads into one statement.
• Actors with many webs get one branch per web role (BE-734); duplicate policies in prod (BE-696) would each cost a read — identical permits are deduplicated at branch assembly as a stopgap.
• search_entity_types keeps the single-statement shape: entity_type_embeddings has no HNSW index yet and stays small. It also ranks and hydrates without a transaction, unlike the entity search — BE-738, which needs a trait change the compiler does not allow today.

🐾 Next steps

• BE-735: statement-layer UNION + shared parameter registry, rerank composed from the AST, single statement per search
• BE-734: collapse structurally identical web permits into web_id = ANY(...)
• BE-732: production embedding backfill
• Measure binary-quantization recall against exact ranking; the overfetch factor 4 is unmeasured

🛡 What tests cover this?

• tests/graph/integration/postgres/semantic_search.rs (new): 7 tests against constructed ground truth (exact cosine distances 0/0.5/1/2) — ranking with rank restore, distance threshold, limit, actor isolation, cross-branch deduplication via entity-scoped policies, draft handling, request filters
• filter::tests::policy_conversion: 15 unit tests incl. a property test comparing for_policy_branches against for_policies over a synthetic universe
• Compile goldens pinning the ranked statement shapes, parameters, and the branch split end to end
• libs/@local/graph/postgres-store/tests/semantic_search/main.rs: ignored perf harness against a seeded database (measures, does not assert)

❓ How to test this?

1. Run the integration tests: cargo nextest run --package hash-graph-integration --test postgres semantic_search (requires a freshly migrated database)
2. With a seeded database: start the graph (cargo run --bin hash-graph -- server) and POST /entities/search with an embedding, maximumSemanticDistance and limit — results are ordered by ascending distance
3. EXPLAIN ANALYZE on a branch statement shows the HNSW index driving broad branches and conventional index scans on selective ones

📹 Demo

Plans and measurement details are documented in the Linear issues.

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[codecov]

Codecov Report

❌ Patch coverage is 62.39669% with 273 lines in your changes missing coverage. Please review.
✅ Project coverage is 59.61%. Comparing base (639beae) to head (16d8f47).
⚠️ Report is 4 commits behind head on main.

Files with missing lines	Patch %	Lines
...tore/src/store/postgres/knowledge/entity/search.rs	0.00%	196 Missing ⚠️
.../graph/postgres-store/src/snapshot/entity/batch.rs	0.00%	24 Missing ⚠️
libs/@local/graph/store/src/filter/mod.rs	95.82%	15 Missing and 2 partials ⚠️
...s-store/src/snapshot/ontology/entity_type/batch.rs	0.00%	15 Missing ⚠️
...s-store/src/store/postgres/ontology/entity_type.rs	0.00%	11 Missing ⚠️
...rc/store/postgres/query/ast/expression/function.rs	50.00%	4 Missing ⚠️
...h/postgres-store/src/store/postgres/query/table.rs	33.33%	4 Missing ⚠️
...gres-store/src/store/postgres/query/compile/mod.rs	97.95%	0 Missing and 1 partial ⚠️
...es-store/src/store/postgres/query/postgres_type.rs	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #9124      +/-   ##
==========================================
+ Coverage   59.55%   59.61%   +0.06%     
==========================================
  Files        1408     1409       +1     
  Lines      137734   138283     +549     
  Branches     6418     6424       +6     
==========================================
+ Hits        82028    82438     +410     
- Misses      54711    54843     +132     
- Partials      995     1002       +7     

Flag	Coverage Δ
apps.hash-ai-worker-ts	1.99% <ø> (ø)
apps.hash-api	12.09% <ø> (ø)
local.hash-backend-utils	2.55% <ø> (ø)
local.hash-graph-sdk	10.02% <ø> (ø)
local.hash-isomorphic-utils	6.37% <ø> (+0.62%)	⬆️
rust.hash-graph-api	7.37% <ø> (ø)
rust.hash-graph-postgres-store	29.33% <19.74%> (-0.34%)	⬇️
rust.hashql-compiletest	28.39% <ø> (ø)
rust.hashql-eval	79.82% <ø> (ø)

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[cursor]

PR Summary

Medium Risk
Changes core semantic search and authorization filtering paths with approximate-then-exact ranking; behaviour can differ from prior full scans, and search cost scales with policy branch count until follow-up work lands.

Overview
Semantic entity search is reworked so ranking can use Postgres HNSW instead of scanning all embeddings under a single permission disjunction. Migration V57 (and shadow migrations) add a stored embedding_bits column (binary_quantize on 3072-d vectors) on entity_embeddings and entity_type_embeddings, plus a partial HNSW index on combined entity embeddings (property IS NULL). Snapshot restore omits generated columns and the HNSW index on staging tables.

The query layer drops Filter::CosineDistance and distance pseudo-columns. SelectCompiler::rank_by_quantized_distance and restrict_embedding_property rank on Hamming distance over embedding_bits, with a 4× candidate overfetch before exact cosine rerank. Entity search_entities runs in a read-only transaction: one quantized candidate read per Filter::for_policy_branches permit branch, union/dedup, full-vector rerank, then hydration with rank restored; HNSW scan settings are tuned per transaction. search_entity_types uses the same quantize-then-rerank pattern without branch splitting.

Filter::for_policy_branches splits permits into conjunctive branches (each permit plus all forbids); for_policies is refactored to share the same parts extraction. Integration and compile tests cover ranking, policies, and snapshot inserts.

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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	$$26.4 \mathrm{ms} \pm 217 \mathrm{μs}\left({\color{gray}0.609 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.84 \mathrm{ms} \pm 16.4 \mathrm{μs}\left({\color{gray}0.979 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$11.9 \mathrm{ms} \pm 73.6 \mathrm{μs}\left({\color{gray}0.544 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$38.6 \mathrm{ms} \pm 314 \mathrm{μs}\left({\color{gray}-0.478 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$11.7 \mathrm{ms} \pm 76.3 \mathrm{μs}\left({\color{gray}-1.251 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$21.4 \mathrm{ms} \pm 214 \mathrm{μs}\left({\color{gray}0.050 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$27.1 \mathrm{ms} \pm 196 \mathrm{μs}\left({\color{gray}-1.757 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.14 \mathrm{ms} \pm 16.0 \mathrm{μs}\left({\color{gray}0.355 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$12.7 \mathrm{ms} \pm 92.2 \mathrm{μs}\left({\color{gray}-1.751 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.15 \mathrm{ms} \pm 21.9 \mathrm{μs}\left({\color{gray}-0.841 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.45 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}0.581 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$2.78 \mathrm{ms} \pm 18.6 \mathrm{μs}\left({\color{gray}1.61 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.46 \mathrm{ms} \pm 42.6 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.91 \mathrm{ms} \pm 17.5 \mathrm{μs}\left({\color{gray}-0.080 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$3.47 \mathrm{ms} \pm 19.8 \mathrm{μs}\left({\color{gray}1.18 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$3.75 \mathrm{ms} \pm 22.4 \mathrm{μs}\left({\color{gray}0.287 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.87 \mathrm{ms} \pm 18.5 \mathrm{μs}\left({\color{gray}2.07 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.38 \mathrm{ms} \pm 23.6 \mathrm{μs}\left({\color{gray}-0.206 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.15 \mathrm{ms} \pm 11.8 \mathrm{μs}\left({\color{gray}0.627 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.03 \mathrm{ms} \pm 11.3 \mathrm{μs}\left({\color{gray}0.044 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.11 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{gray}-0.709 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.36 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}0.028 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.18 \mathrm{ms} \pm 12.9 \mathrm{μs}\left({\color{gray}-0.506 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.33 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}0.780 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.48 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}0.643 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.22 \mathrm{ms} \pm 11.0 \mathrm{μs}\left({\color{gray}-0.388 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$2.45 \mathrm{ms} \pm 19.7 \mathrm{μs}\left({\color{gray}4.59 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$2.79 \mathrm{ms} \pm 18.5 \mathrm{μs}\left({\color{gray}0.204 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.42 \mathrm{ms} \pm 16.5 \mathrm{μs}\left({\color{gray}0.640 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$2.64 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{gray}1.95 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$2.71 \mathrm{ms} \pm 16.3 \mathrm{μs}\left({\color{gray}-0.556 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.39 \mathrm{ms} \pm 13.3 \mathrm{μs}\left({\color{gray}1.39 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$2.70 \mathrm{ms} \pm 22.1 \mathrm{μs}\left({\color{gray}3.64 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$37.1 \mathrm{ms} \pm 208 \mathrm{μs}\left({\color{gray}-0.655 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$29.0 \mathrm{ms} \pm 246 \mathrm{μs}\left({\color{gray}-0.448 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$30.4 \mathrm{ms} \pm 156 \mathrm{μs}\left({\color{gray}-1.183 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$28.1 \mathrm{ms} \pm 183 \mathrm{μs}\left({\color{gray}3.03 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$37.1 \mathrm{ms} \pm 281 \mathrm{μs}\left({\color{gray}2.21 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$44.3 \mathrm{ms} \pm 179 \mathrm{μs}\left({\color{gray}-0.476 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$35.1 \mathrm{ms} \pm 188 \mathrm{μs}\left({\color{gray}0.309 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$81.4 \mathrm{ms} \pm 429 \mathrm{μs}\left({\color{gray}-1.023 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$29.3 \mathrm{ms} \pm 204 \mathrm{μs}\left({\color{lightgreen}-34.219 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$267 \mathrm{ms} \pm 1.35 \mathrm{ms}\left({\color{gray}-2.318 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$9.43 \mathrm{ms} \pm 63.0 \mathrm{μs}\left({\color{gray}0.726 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$9.35 \mathrm{ms} \pm 48.6 \mathrm{μs}\left({\color{gray}-1.035 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$9.41 \mathrm{ms} \pm 53.9 \mathrm{μs}\left({\color{gray}1.04 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$9.38 \mathrm{ms} \pm 62.9 \mathrm{μs}\left({\color{gray}0.859 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$9.17 \mathrm{ms} \pm 42.3 \mathrm{μs}\left({\color{gray}-1.953 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$9.25 \mathrm{ms} \pm 55.2 \mathrm{μs}\left({\color{gray}0.408 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$9.37 \mathrm{ms} \pm 47.8 \mathrm{μs}\left({\color{gray}-1.714 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$9.31 \mathrm{ms} \pm 54.5 \mathrm{μs}\left({\color{gray}0.259 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$9.38 \mathrm{ms} \pm 66.7 \mathrm{μs}\left({\color{gray}-3.611 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$9.80 \mathrm{ms} \pm 58.9 \mathrm{μs}\left({\color{gray}1.40 \mathrm{\%}}\right) $$	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	$$9.54 \mathrm{ms} \pm 63.7 \mathrm{μs}\left({\color{gray}-2.621 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$9.56 \mathrm{ms} \pm 50.6 \mathrm{μs}\left({\color{gray}-1.842 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$9.63 \mathrm{ms} \pm 58.5 \mathrm{μs}\left({\color{gray}-0.284 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$9.69 \mathrm{ms} \pm 55.4 \mathrm{μs}\left({\color{gray}-0.772 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$9.66 \mathrm{ms} \pm 50.0 \mathrm{μs}\left({\color{gray}-1.887 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$9.66 \mathrm{ms} \pm 47.1 \mathrm{μs}\left({\color{gray}-1.729 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$9.66 \mathrm{ms} \pm 53.1 \mathrm{μs}\left({\color{gray}0.577 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$9.66 \mathrm{ms} \pm 48.1 \mathrm{μs}\left({\color{gray}-4.144 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$9.84 \mathrm{ms} \pm 67.1 \mathrm{μs}\left({\color{gray}0.677 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$7.21 \mathrm{ms} \pm 37.6 \mathrm{μs}\left({\color{gray}3.16 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$52.0 \mathrm{ms} \pm 356 \mathrm{μs}\left({\color{lightgreen}-7.768 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$96.5 \mathrm{ms} \pm 503 \mathrm{μs}\left({\color{gray}-2.291 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$58.8 \mathrm{ms} \pm 349 \mathrm{μs}\left({\color{gray}-4.343 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$66.9 \mathrm{ms} \pm 461 \mathrm{μs}\left({\color{gray}-4.912 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$75.1 \mathrm{ms} \pm 394 \mathrm{μs}\left({\color{gray}-2.956 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$80.4 \mathrm{ms} \pm 469 \mathrm{μs}\left({\color{gray}-2.011 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$41.1 \mathrm{ms} \pm 245 \mathrm{μs}\left({\color{gray}-0.923 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$64.6 \mathrm{ms} \pm 319 \mathrm{μs}\left({\color{gray}-1.136 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$46.5 \mathrm{ms} \pm 226 \mathrm{μs}\left({\color{gray}-0.428 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$54.9 \mathrm{ms} \pm 515 \mathrm{μs}\left({\color{gray}0.288 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$56.2 \mathrm{ms} \pm 378 \mathrm{μs}\left({\color{gray}-0.609 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$56.0 \mathrm{ms} \pm 391 \mathrm{μs}\left({\color{gray}-0.620 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$108 \mathrm{ms} \pm 719 \mathrm{μs}\left({\color{red}5.76 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$118 \mathrm{ms} \pm 506 \mathrm{μs}\left({\color{red}5.16 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$16.3 \mathrm{ms} \pm 95.3 \mathrm{μs}\left({\color{gray}-2.372 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$477 \mathrm{ms} \pm 1.04 \mathrm{ms}\left({\color{gray}0.089 \mathrm{\%}}\right) $$	Flame Graph