Execute spatial distance with RowFn

[connortsui20]

Rationale for this change

Moves spatial distance to the shared row executor. Each geometry column decodes once per batch, and a constant operand remains one decoded geometry.

• Progress towards: Epic: Row-oriented scalar functions #9128

What changes are included in this PR?

Adds the crate-private GeometryRow input element and expresses distance as a two-row kernel. Tests cover both constant positions, nullable inputs, mixed geometry types, and exact distance behavior.

Rust 1.97.1 one-CGU fat-LTO measurements keep the ordinary distance cases within 2.7% of develop; the nullable column-by-constant case improves by 13.9%.

What APIs are changed? Are there any user-facing changes?

There are no public API or behavior changes. SpatialDistance implements RowFn and receives the standard scalar-function vtable automatically.

[codspeed-hq]

Merging this PR will degrade performance by 7.5%

⚠️ 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.

⚡ 23 improved benchmarks
❌ 80 regressed benchmarks
✅ 1901 untouched benchmarks
🆕 57 new benchmarks
⏩ 89 skipped benchmarks¹

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	Simulation	mul_i8_nonnull	169.1 µs	466.5 µs	-63.75%
❌	Simulation	mul_u8_nonnull	163.4 µs	416.6 µs	-60.77%
❌	Simulation	mul_u16_nonnull	268.4 µs	576.8 µs	-53.46%
❌	Simulation	mul_i32_nullable	559.7 µs	1,063.2 µs	-47.36%
❌	Simulation	add_i64_nullable	965.7 µs	1,756 µs	-45.01%
❌	Simulation	mul_i16_nonnull	280.5 µs	502.4 µs	-44.17%
❌	Simulation	mul_i32_constant	363.8 µs	649.6 µs	-44%
❌	Simulation	mul_u32_nonnull	484.8 µs	793.1 µs	-38.88%
❌	Simulation	case_when_nary_10_conditions[1000]	396 µs	603.7 µs	-34.41%
❌	Simulation	sub_i64_constant	637.4 µs	928.7 µs	-31.37%
❌	Simulation	add_i32_nonnull	483 µs	700.3 µs	-31.03%
❌	Simulation	add_i64_constant	637.3 µs	918.6 µs	-30.62%
❌	Simulation	mul_i32_nonnull	507.9 µs	701.7 µs	-27.62%
❌	Simulation	add_u32_nonnull	483.5 µs	666 µs	-27.39%
❌	Simulation	case_when_nary_equality_lookup[1000]	310.6 µs	422 µs	-26.4%
❌	Simulation	baseline_lt[4, 1024]	95.8 µs	129.6 µs	-26.08%
❌	Simulation	case_when_nary_10_conditions[10000]	744 µs	981.9 µs	-24.23%
❌	Simulation	baseline_eq[4, 1024]	95.2 µs	125.1 µs	-23.93%
❌	Simulation	case_when_nary_3_conditions[1000]	227 µs	296.6 µs	-23.48%
❌	Simulation	baseline_lt[16, 1024]	97.3 µs	127.1 µs	-23.43%
...	...	...	...	...	...

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Tip

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_{Comparing ct/row-fn-spatial-distance (8bad9d9) with ct/row-fn-tensor-products (91d89a1)}

Footnotes

1. 89 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. ↩

[connortsui20]

Here are the local spatial-distance benchmark results from the final Rust 1.97.1 run.

The comparison used a develop baseline with benchmark-only backfills (1edd0fca) and the full RowFn stack (8ca9fc73). Later stack layers do not change spatial distance source, but unrelated code-placement effects can still affect these binaries.

The machine was an AMD Ryzen 9 7950X running Linux. The build used rustc 1.97.1, LLVM 22.1.6, one CGU, fat LTO, and -C target-cpu=native. Each target used two warm runs and seven alternating measured pairs. Each process used 100 samples with a 0.25–0.5 second measurement window on CPU 4.

Negative changes are faster. The values are medians across the seven paired runs.

The three ordinary distance cases remain within 2.65%. The nullable column-by-constant case improves by 13.85%.

All spatial-distance results: 4 cases

Benchmark	Baseline	RowFn stack	Change
point_column_x_constant_point	13.670 µs	14.080 µs	+2.65%
polygon_column_x_constant_polygon	33.550 µs	34.180 µs	+1.70%
point_column_x_point_column	17.140 µs	17.060 µs	+0.47%
nullable_point_column_x_constant_point	18.190 µs	15.680 µs	-13.85%