BE-273: HashQL: Profile and benchmark MIR interpreter

[indietyp]

🌟 What is the purpose of this PR?

Replace imbl with rpds for persistent data structures in the MIR interpreter, and optimize interpreter performance.

The interpreter is now only 20-40% slower instead of the previous 100%+.

🔍 What does this change?

• Replaces imbl dependency with rpds for persistent data structures (List and Dict)
• Adds inlining annotations to hot code paths in the interpreter
• Optimizes SwitchTargets::target() with a fast path for common cases
• Adds cold path annotations to error handling code
• Optimizes value comparison in the interpreter
• Adds a new benchmark for the interpreter (fibonacci recursive)
• Improves parameter passing in the interpreter to avoid unnecessary copies
• Optimizes local variable storage in the interpreter

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 benchmark for the interpreter (fibonacci recursive)
• Existing tests continue to pass

❓ How to test this?

1. Run the new benchmark to verify performance improvements
2. Run existing tests to ensure functionality is preserved

[cursor]

PR Summary

Focuses on MIR interpreter performance and benchmarking.

• Replace imbl with rpds for persistent collections in interpreter values (List, Dict); update Cargo.toml/Cargo.lock and deps in hashql-mir
• Optimize hot paths: add #[inline], cold_path, and a small-size fast path in SwitchTargets::target(); simplify comparisons and arithmetic in value ops
• Reduce allocations/copies and tighten APIs: Locals now stores Value (not Option<Value>), requires ExactSizeIterator for args, and many interpreter APIs take references instead of by-value (e.g., operands, places, subscript_mut)
• Minor core API additions: IdVec::reserve; mark IdSlice::lookup/lookup_mut as inline
• Add new benches/interpret.rs (recursive Fibonacci) and factor shared benchmarking harness into benches/common/run.rs; update transform benches to reuse it
• Enable additional nightly features used in optimizations (e.g., cold_path, likely_unlikely, exhaustive_patterns)

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[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-273: HashQL: Profile and benchmark MIR interpreter #8251 👈 (View in Graphite)
• BE-272: HashQL: Add numeric addition and subtraction operators to MIR #8250
• BE-257: HashQL: Implement MIR interpreter with runtime value representation #8245
• 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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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	$$27.7 \mathrm{ms} \pm 260 \mathrm{μs}\left({\color{red}6.30 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.18 \mathrm{ms} \pm 15.0 \mathrm{μs}\left({\color{gray}-0.631 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.0 \mathrm{ms} \pm 75.2 \mathrm{μs}\left({\color{gray}-0.827 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.2 \mathrm{ms} \pm 343 \mathrm{μs}\left({\color{gray}0.709 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.0 \mathrm{ms} \pm 74.7 \mathrm{μs}\left({\color{gray}-1.415 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.2 \mathrm{ms} \pm 140 \mathrm{μs}\left({\color{gray}-0.025 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$30.3 \mathrm{ms} \pm 158 \mathrm{μs}\left({\color{lightgreen}-29.937 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.56 \mathrm{ms} \pm 18.9 \mathrm{μs}\left({\color{lightgreen}-82.574 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.7 \mathrm{ms} \pm 84.9 \mathrm{μs}\left({\color{lightgreen}-52.269 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.59 \mathrm{ms} \pm 17.2 \mathrm{μs}\left({\color{gray}0.439 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.79 \mathrm{ms} \pm 9.78 \mathrm{μs}\left({\color{gray}-0.304 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.17 \mathrm{ms} \pm 15.5 \mathrm{μs}\left({\color{gray}0.937 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.92 \mathrm{ms} \pm 22.1 \mathrm{μs}\left({\color{gray}-0.296 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.35 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{gray}0.149 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.92 \mathrm{ms} \pm 21.9 \mathrm{μs}\left({\color{gray}0.960 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.17 \mathrm{ms} \pm 21.8 \mathrm{μs}\left({\color{gray}-0.621 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.24 \mathrm{ms} \pm 13.9 \mathrm{μs}\left({\color{gray}0.501 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.84 \mathrm{ms} \pm 36.4 \mathrm{μs}\left({\color{gray}-0.063 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.54 \mathrm{ms} \pm 12.2 \mathrm{μs}\left({\color{red}7.00 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.46 \mathrm{ms} \pm 8.27 \mathrm{μs}\left({\color{gray}4.76 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.60 \mathrm{ms} \pm 18.5 \mathrm{μs}\left({\color{red}6.69 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.74 \mathrm{ms} \pm 11.5 \mathrm{μs}\left({\color{red}5.15 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.63 \mathrm{ms} \pm 9.50 \mathrm{μs}\left({\color{gray}4.30 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.83 \mathrm{ms} \pm 10.4 \mathrm{μs}\left({\color{gray}4.99 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.81 \mathrm{ms} \pm 11.4 \mathrm{μs}\left({\color{gray}3.29 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.53 \mathrm{ms} \pm 9.14 \mathrm{μs}\left({\color{red}5.70 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.69 \mathrm{ms} \pm 10.2 \mathrm{μs}\left({\color{red}5.39 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.17 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}3.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.77 \mathrm{ms} \pm 10.6 \mathrm{μs}\left({\color{red}5.79 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$2.98 \mathrm{ms} \pm 13.2 \mathrm{μs}\left({\color{gray}4.44 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.05 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{gray}2.31 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.76 \mathrm{ms} \pm 15.1 \mathrm{μs}\left({\color{red}6.55 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$2.95 \mathrm{ms} \pm 15.0 \mathrm{μs}\left({\color{gray}4.29 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$38.7 \mathrm{ms} \pm 136 \mathrm{μs}\left({\color{gray}0.618 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$76.0 \mathrm{ms} \pm 348 \mathrm{μs}\left({\color{gray}0.603 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$43.2 \mathrm{ms} \pm 141 \mathrm{μs}\left({\color{gray}2.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.1 \mathrm{ms} \pm 151 \mathrm{μs}\left({\color{gray}0.328 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$53.6 \mathrm{ms} \pm 297 \mathrm{μs}\left({\color{gray}2.05 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.2 \mathrm{ms} \pm 161 \mathrm{μs}\left({\color{gray}0.968 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$414 \mathrm{ms} \pm 735 \mathrm{μs}\left({\color{gray}0.121 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$95.1 \mathrm{ms} \pm 356 \mathrm{μs}\left({\color{gray}0.154 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$84.5 \mathrm{ms} \pm 315 \mathrm{μs}\left({\color{gray}0.314 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$259 \mathrm{ms} \pm 635 \mathrm{μs}\left({\color{lightgreen}-6.520 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.6 \mathrm{ms} \pm 57.9 \mathrm{μs}\left({\color{gray}-1.056 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$14.8 \mathrm{ms} \pm 68.4 \mathrm{μs}\left({\color{gray}1.82 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.1 \mathrm{ms} \pm 78.6 \mathrm{μs}\left({\color{gray}0.544 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$14.7 \mathrm{ms} \pm 75.8 \mathrm{μs}\left({\color{gray}1.82 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$17.5 \mathrm{ms} \pm 103 \mathrm{μs}\left({\color{gray}0.856 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.5 \mathrm{ms} \pm 72.0 \mathrm{μs}\left({\color{gray}1.65 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.6 \mathrm{ms} \pm 67.3 \mathrm{μs}\left({\color{gray}-0.059 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.6 \mathrm{ms} \pm 84.2 \mathrm{μs}\left({\color{gray}1.33 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.2 \mathrm{ms} \pm 103 \mathrm{μs}\left({\color{gray}1.17 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.5 \mathrm{ms} \pm 150 \mathrm{μs}\left({\color{gray}-0.372 \mathrm{\%}}\right) $$	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	$$29.8 \mathrm{ms} \pm 296 \mathrm{μs}\left({\color{gray}-0.474 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$30.4 \mathrm{ms} \pm 242 \mathrm{μs}\left({\color{gray}-0.035 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$30.0 \mathrm{ms} \pm 320 \mathrm{μs}\left({\color{gray}1.80 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$30.5 \mathrm{ms} \pm 291 \mathrm{μs}\left({\color{gray}-2.564 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$29.8 \mathrm{ms} \pm 309 \mathrm{μs}\left({\color{gray}-1.034 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$29.8 \mathrm{ms} \pm 252 \mathrm{μs}\left({\color{gray}0.176 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$29.8 \mathrm{ms} \pm 251 \mathrm{μs}\left({\color{gray}0.320 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$30.2 \mathrm{ms} \pm 312 \mathrm{μs}\left({\color{gray}-2.406 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$30.6 \mathrm{ms} \pm 336 \mathrm{μs}\left({\color{gray}-0.579 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$7.96 \mathrm{ms} \pm 33.8 \mathrm{μs}\left({\color{gray}0.157 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$47.1 \mathrm{ms} \pm 213 \mathrm{μs}\left({\color{gray}-0.421 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$93.5 \mathrm{ms} \pm 366 \mathrm{μs}\left({\color{gray}0.161 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$52.3 \mathrm{ms} \pm 277 \mathrm{μs}\left({\color{gray}-0.915 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$60.2 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}0.203 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$68.0 \mathrm{ms} \pm 253 \mathrm{μs}\left({\color{gray}-0.634 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$74.5 \mathrm{ms} \pm 426 \mathrm{μs}\left({\color{gray}-0.145 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$49.8 \mathrm{ms} \pm 191 \mathrm{μs}\left({\color{gray}0.663 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$76.6 \mathrm{ms} \pm 357 \mathrm{μs}\left({\color{gray}0.723 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$56.5 \mathrm{ms} \pm 220 \mathrm{μs}\left({\color{gray}0.247 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$64.5 \mathrm{ms} \pm 373 \mathrm{μs}\left({\color{gray}0.192 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$66.2 \mathrm{ms} \pm 265 \mathrm{μs}\left({\color{gray}0.549 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$65.9 \mathrm{ms} \pm 280 \mathrm{μs}\left({\color{gray}-0.767 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$135 \mathrm{ms} \pm 456 \mathrm{μs}\left({\color{gray}-4.867 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$131 \mathrm{ms} \pm 404 \mathrm{μs}\left({\color{lightgreen}-5.260 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$39.3 \mathrm{ms} \pm 207 \mathrm{μs}\left({\color{lightgreen}-61.691 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$572 \mathrm{ms} \pm 805 \mathrm{μs}\left({\color{gray}-3.063 \mathrm{\%}}\right) $$	Flame Graph