feat(vortex-bench): wire SpatialBench into the bench orchestrator

[HarukiMoriarty]

Summary

Wires SpatialBench into the vx-bench / bench-orchestrator pipeline so it can be run end-to-end like the other benchmarks (datagen → Parquet → Vortex conversion → query). It builds on the WKB datagen landed in #8598.

Running command:

uv run --project bench-orchestrator vx-bench run spatialbench --engine duckdb --format parquet,vortex --opt scale-factor=N --queries 1,2,3,4,5,6,7,8,9 --iterations 3

Limitation

• DuckDB-only. For now SpatialBench queries use DuckDB-specific ST_* spatial SQL that DataFusion has no functions for yet. There is a a single ad-hoc entry in BENCHMARK_ENGINES = { SPATIALBENCH: {DUCKDB} }.

• No dictionary encoding / compaction on the WKB column. WKB geometry blobs are large and effectively unique, so running the dictionary builder over them balloons memory (tens of GB) for zero compression gain. The normal compaction path is preserved for every other column on every other benchmark.

• Queries 10, 11, 12 is timeout simply because DuckDB poorly support on Spatial index.

Performance

SF=1.0

┏━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Query ┃ duckdb:parquet (base) ┃   duckdb:vortex ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ 1     │                39.1ms │  14.6ms (0.37x) │
│ 2     │                72.1ms │  24.9ms (0.35x) │
│ 3     │                57.7ms │  20.1ms (0.35x) │
│ 4     │               113.1ms │  70.6ms (0.62x) │
│ 5     │               354.2ms │ 288.4ms (0.81x) │
│ 6     │               169.5ms │  91.6ms (0.54x) │
│ 7     │               156.7ms │  71.5ms (0.46x) │
│ 8     │               196.5ms │  80.3ms (0.41x) │
│ 9     │                20.3ms │  18.7ms (0.92x) │
└───────┴───────────────────────┴─────────────────┘

SF=10

┏━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Query ┃ duckdb:parquet (base) ┃   duckdb:vortex ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ 1     │               158.6ms │ 114.4ms (0.72x) │
│ 2     │               255.1ms │ 219.3ms (0.86x) │
│ 3     │               229.2ms │ 181.5ms (0.79x) │
│ 4     │               184.5ms │ 134.3ms (0.73x) │
│ 5     │                 3.30s │   3.08s (0.93x) │
│ 6     │               476.4ms │ 348.9ms (0.73x) │
│ 7     │               918.2ms │ 961.2ms (1.05x) │
│ 8     │               980.6ms │ 926.7ms (0.94x) │
│ 9     │                33.7ms │  33.6ms (1.00x) │
└───────┴───────────────────────┴─────────────────┘

[codspeed-hq]

Merging this PR will not alter performance

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

⚡ 4 improved benchmarks
❌ 2 regressed benchmarks
✅ 1583 untouched benchmarks
⏩ 4 skipped benchmarks¹

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	Simulation	chunked_varbinview_into_canonical[(1000, 10)]	168.9 µs	205.5 µs	-17.85%
❌	Simulation	bitwise_not_vortex_buffer_mut[128]	215.3 ns	244.4 ns	-11.93%
⚡	Simulation	chunked_bool_canonical_into[(1000, 10)]	26.9 µs	16.7 µs	+61.21%
⚡	Simulation	chunked_varbinview_canonical_into[(100, 100)]	259.6 µs	224.5 µs	+15.63%
⚡	Simulation	chunked_varbinview_into_canonical[(100, 100)]	306.4 µs	271.1 µs	+13.01%
⚡	Simulation	eq_i64_constant	318.3 µs	288.6 µs	+10.29%

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

---

_{Comparing nemo/spatial-wire-vx-bench (fdb0872) with nemo/spatial-wkb (c1635cb)}

Footnotes

1. 4 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩