BE-257: HashQL: Implement MIR interpreter with runtime value representation

[indietyp]

🌟 What is the purpose of this PR?

Add MIR interpreter for executing HashQL code at runtime. This implements a stack-based interpreter that can execute MIR code, handle function calls, and manipulate runtime values.

🔍 What does this change?

• Adds a complete MIR interpreter implementation in libs/@local/hashql/mir/src/interpret/
• Implements runtime value representation with support for primitives, aggregates, and collections
• Adds error handling and diagnostic reporting for runtime errors
• Implements a call stack for function execution and tracking
• Adds support for binary and unary operations, control flow, and function calls
• Enhances the newtype! macro to support custom display formatting
• Adds reserve method to InternSet for preallocating capacity
• Updates ID display formatting for better readability in error messages

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?

• Comprehensive test suite for the interpreter covering basic execution, operations, control flow, function calls, and error handling
• Tests for value representation and manipulation

❓ How to test this?

1. Run the test suite with cargo test -p hashql-mir
2. Run Miri tests with cargo miri test -p hashql-mir

[cursor]

PR Summary

Adds a full MIR interpreter capable of executing MIR with function calls, control flow, operand/aggregate evaluation, and rich runtime diagnostics.

• Introduces interpret/ (runtime, call stack, locals/place handling, value ops, error-to-diagnostic conversion)
• Extends Int with bitwise ops and robust negation; supports ~ unary op via op!
• Enhances id::newtype! with #[display = ..] and #[no_display]; updates display of BasicBlockId (bb{}), Local (%{}), Location, and place indexing
• Adds InternSet::reserve() and reserves capacity in type environment prefill
• Adds entity schema fixtures for organization and person
• Updates dependencies (adds imbl; bumps itertools in prost) and adjusts miri test target

^{Written by Cursor Bugbot for commit 85204ae. 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 👈 (View in Graphite)
• 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
• 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: Adds a stack-based MIR interpreter to execute HashQL MIR at runtime, alongside a runtime Value model and rich diagnostics.

Changes:

• Introduce interpret/ with Runtime, CallStack, per-frame Locals, and stepping over statements/terminators
• Add runtime Value types (primitives, aggregates, collections) with total ordering/equality; collections backed by imbl
• Add RuntimeError + InterpretDiagnosticCategory and conversion to diagnostics with callstack labeling
• Implement additional ops: unary ~, plus bitwise/unary operator traits for MIR Int
• Enhance ID display via newtype! display controls and update MIR pretty-printers/analysis output accordingly
• Add InternSet::reserve and pre-reserve environment kinds; update dev scripts and deps

Technical Notes: Includes extensive end-to-end tests (and Miri coverage) for execution, control flow, calls/returns, aggregates, inputs, and error/ICE paths.

_{🤖 Was this summary useful? React with 👍 or 👎}

[augmentcode]

Review completed. 3 suggestions posted.

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

[codspeed-hq]

Merging this PR will not alter performance

✅ 18 untouched benchmarks
🗄️ 12 archived benchmarks run¹

---

_{Comparing bm/be-257-hashql-implement-interpreter (85204ae) with main (b51cad9)}

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. ↩

[graphite-app]

Merge activity

• Jan 19, 11:06 AM UTC: Graphite rebased this pull request, because this pull request is set to merge when ready.
• Jan 19, 11:06 AM 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	$$26.3 \mathrm{ms} \pm 128 \mathrm{μs}\left({\color{gray}0.749 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.25 \mathrm{ms} \pm 21.5 \mathrm{μs}\left({\color{gray}0.456 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.1 \mathrm{ms} \pm 79.6 \mathrm{μs}\left({\color{gray}2.92 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$41.8 \mathrm{ms} \pm 278 \mathrm{μs}\left({\color{gray}0.002 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$13.9 \mathrm{ms} \pm 78.5 \mathrm{μs}\left({\color{gray}2.24 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.6 \mathrm{ms} \pm 175 \mathrm{μs}\left({\color{gray}4.27 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.5 \mathrm{ms} \pm 304 \mathrm{μs}\left({\color{lightgreen}-36.866 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.63 \mathrm{ms} \pm 29.2 \mathrm{μs}\left({\color{lightgreen}-81.584 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$11.1 \mathrm{ms} \pm 70.0 \mathrm{μs}\left({\color{lightgreen}-62.478 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.68 \mathrm{ms} \pm 17.8 \mathrm{μs}\left({\color{gray}3.23 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.84 \mathrm{ms} \pm 11.9 \mathrm{μs}\left({\color{gray}1.80 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.22 \mathrm{ms} \pm 17.4 \mathrm{μs}\left({\color{gray}3.37 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.98 \mathrm{ms} \pm 23.4 \mathrm{μs}\left({\color{gray}1.30 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.44 \mathrm{ms} \pm 26.0 \mathrm{μs}\left({\color{gray}3.07 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.95 \mathrm{ms} \pm 16.4 \mathrm{μs}\left({\color{gray}1.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.23 \mathrm{ms} \pm 27.8 \mathrm{μs}\left({\color{gray}3.93 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.29 \mathrm{ms} \pm 12.6 \mathrm{μs}\left({\color{gray}3.05 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.88 \mathrm{ms} \pm 20.4 \mathrm{μs}\left({\color{gray}1.58 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.57 \mathrm{ms} \pm 13.5 \mathrm{μs}\left({\color{red}9.82 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.48 \mathrm{ms} \pm 9.51 \mathrm{μs}\left({\color{red}8.11 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.61 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{red}9.01 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.75 \mathrm{ms} \pm 10.2 \mathrm{μs}\left({\color{red}5.64 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.65 \mathrm{ms} \pm 9.31 \mathrm{μs}\left({\color{red}7.63 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.87 \mathrm{ms} \pm 12.2 \mathrm{μs}\left({\color{red}8.21 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.92 \mathrm{ms} \pm 16.2 \mathrm{μs}\left({\color{red}7.58 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.60 \mathrm{ms} \pm 10.6 \mathrm{μs}\left({\color{red}9.66 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.75 \mathrm{ms} \pm 11.7 \mathrm{μs}\left({\color{red}7.51 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.26 \mathrm{ms} \pm 16.3 \mathrm{μs}\left({\color{red}7.11 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.84 \mathrm{ms} \pm 13.6 \mathrm{μs}\left({\color{red}8.77 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.05 \mathrm{ms} \pm 13.6 \mathrm{μs}\left({\color{red}7.49 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.15 \mathrm{ms} \pm 18.2 \mathrm{μs}\left({\color{red}6.29 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.79 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{red}7.99 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.07 \mathrm{ms} \pm 21.0 \mathrm{μs}\left({\color{red}8.60 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$39.3 \mathrm{ms} \pm 150 \mathrm{μs}\left({\color{gray}2.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$76.0 \mathrm{ms} \pm 501 \mathrm{μs}\left({\color{gray}1.70 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$43.2 \mathrm{ms} \pm 231 \mathrm{μs}\left({\color{gray}4.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$46.2 \mathrm{ms} \pm 214 \mathrm{μs}\left({\color{gray}4.24 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$53.2 \mathrm{ms} \pm 257 \mathrm{μs}\left({\color{gray}3.13 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$39.8 \mathrm{ms} \pm 167 \mathrm{μs}\left({\color{gray}-0.192 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$417 \mathrm{ms} \pm 959 \mathrm{μs}\left({\color{gray}1.20 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$93.6 \mathrm{ms} \pm 402 \mathrm{μs}\left({\color{red}7.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$84.0 \mathrm{ms} \pm 408 \mathrm{μs}\left({\color{gray}1.89 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$309 \mathrm{ms} \pm 911 \mathrm{μs}\left({\color{red}11.7 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.4 \mathrm{ms} \pm 62.3 \mathrm{μs}\left({\color{gray}0.539 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$14.8 \mathrm{ms} \pm 63.8 \mathrm{μs}\left({\color{gray}2.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.0 \mathrm{ms} \pm 67.0 \mathrm{μs}\left({\color{gray}2.59 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$15.3 \mathrm{ms} \pm 87.1 \mathrm{μs}\left({\color{gray}4.93 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$17.7 \mathrm{ms} \pm 79.8 \mathrm{μs}\left({\color{gray}0.287 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.4 \mathrm{ms} \pm 58.1 \mathrm{μs}\left({\color{gray}0.679 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.5 \mathrm{ms} \pm 56.0 \mathrm{μs}\left({\color{gray}1.40 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.4 \mathrm{ms} \pm 62.6 \mathrm{μs}\left({\color{gray}1.45 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.4 \mathrm{ms} \pm 72.9 \mathrm{μs}\left({\color{gray}3.29 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.4 \mathrm{ms} \pm 138 \mathrm{μs}\left({\color{gray}2.82 \mathrm{\%}}\right) $$	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	$$29.8 \mathrm{ms} \pm 286 \mathrm{μs}\left({\color{gray}2.46 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$31.3 \mathrm{ms} \pm 327 \mathrm{μs}\left({\color{red}7.93 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$29.5 \mathrm{ms} \pm 279 \mathrm{μs}\left({\color{gray}2.14 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$29.0 \mathrm{ms} \pm 253 \mathrm{μs}\left({\color{gray}1.10 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$29.8 \mathrm{ms} \pm 290 \mathrm{μs}\left({\color{red}6.16 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$30.0 \mathrm{ms} \pm 273 \mathrm{μs}\left({\color{gray}2.95 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$29.7 \mathrm{ms} \pm 290 \mathrm{μs}\left({\color{gray}3.02 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$29.5 \mathrm{ms} \pm 253 \mathrm{μs}\left({\color{gray}2.64 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$30.3 \mathrm{ms} \pm 249 \mathrm{μs}\left({\color{gray}4.57 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.05 \mathrm{ms} \pm 32.5 \mathrm{μs}\left({\color{gray}1.87 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$47.1 \mathrm{ms} \pm 177 \mathrm{μs}\left({\color{red}6.36 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$96.2 \mathrm{ms} \pm 471 \mathrm{μs}\left({\color{red}5.46 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$53.6 \mathrm{ms} \pm 262 \mathrm{μs}\left({\color{red}7.73 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$62.4 \mathrm{ms} \pm 273 \mathrm{μs}\left({\color{red}7.43 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$70.1 \mathrm{ms} \pm 346 \mathrm{μs}\left({\color{red}5.26 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$75.8 \mathrm{ms} \pm 332 \mathrm{μs}\left({\color{gray}4.94 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$51.2 \mathrm{ms} \pm 324 \mathrm{μs}\left({\color{red}7.32 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$77.9 \mathrm{ms} \pm 306 \mathrm{μs}\left({\color{gray}4.55 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$57.5 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{red}5.65 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$65.2 \mathrm{ms} \pm 308 \mathrm{μs}\left({\color{gray}4.76 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$66.8 \mathrm{ms} \pm 325 \mathrm{μs}\left({\color{gray}4.17 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$67.2 \mathrm{ms} \pm 296 \mathrm{μs}\left({\color{gray}4.20 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$135 \mathrm{ms} \pm 522 \mathrm{μs}\left({\color{gray}-1.582 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$136 \mathrm{ms} \pm 642 \mathrm{μs}\left({\color{gray}-1.567 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$39.5 \mathrm{ms} \pm 194 \mathrm{μs}\left({\color{gray}-2.414 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$561 \mathrm{ms} \pm 1.00 \mathrm{ms}\left({\color{gray}-3.513 \mathrm{\%}}\right) $$	Flame Graph