Add FilterPushdown support to spark data source

[robert3005]

Add spark filter pushdown to spark datasource

Signed-off-by: Robert Kruszewski github@robertk.io

[codspeed-hq]

Merging this PR will improve performance by 20.24%

⚠️ 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.

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

⚡ 6 improved benchmarks
✅ 1210 untouched benchmarks
⏩ 24 skipped benchmarks¹

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	Simulation	new_bp_prim_test_between[i32, 32768]	169.9 µs	141.1 µs	+20.42%
⚡	Simulation	new_bp_prim_test_between[i64, 16384]	144.4 µs	115.2 µs	+25.4%
⚡	Simulation	new_bp_prim_test_between[i64, 32768]	236.7 µs	178.1 µs	+32.94%
⚡	Simulation	new_alp_prim_test_between[f64, 16384]	148.8 µs	126.9 µs	+17.26%
⚡	Simulation	new_bp_prim_test_between[i32, 16384]	109.1 µs	94.8 µs	+15.07%
⚡	Simulation	new_bp_prim_test_between[i16, 32768]	134.1 µs	120.3 µs	+11.54%

Tip

Curious why this is faster? Comment @codspeedbot explain why this is faster on this PR, or directly use the CodSpeed MCP with your agent.

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_{Comparing rk/sparkfilterpushdown (771ffc7) with develop (7349cd6)}

Footnotes

1. 24 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. ↩