bench(fsst): add baseline for sparse take optimization

[blaginin]

Prepared with Codex.

[codspeed]

Merging this PR will regress 3 benchmarks

⚠️ 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.

⚠️ 2 benchmarks measured no execution time

Nothing ran under measurement, usually because the compiler removed the code under test. These results are not comparable, so they count as unchanged.

Preventing compiler optimizations

• bench_compare_primitive[(10000, 2)]
• bench_compare_sliced_dict_primitive[(3333, 10000)]

⚠️ 18 benchmarks spent significant time in system calls

System calls cannot be consistently instrumented, so they are not included in the measure, which understates the real cost. Please switch to the Walltime instrument to accurately measure system calls.

Measurement and system calls

• test_scan_scalar_at[int_str]
• test_scan_filter[int_float]
• random_i128[0.01]
• test_scan_filter[int_str]
• test_repeated_scan_filter[int_str]
• from_vec_drop_vortex[65536]
• take_indices[(10000, 4096)]
• decompress[u16, (4000, 256)]
• test_scan_filter[int_str_float]
• pcodec_compress_f64
• …and 8 more

ℹ️ 40 benchmarks have no flame graph

The flamegraph could not be generated because we failed to resolve the symbols in the profiling trace. This happens when we fail to harvest information about language frames.

• test_scan_scalar_at[int_str]
• test_repeated_scan_scalar_at[int_float]
• test_scan_filter[int_float]
• test_scan_filter[int_str]
• test_repeated_scan_scalar_at[int]
• test_repeated_scan_scalar_at[int_str_float]
• test_repeated_scan_filter[int_str]
• test_repeated_scan_scalar_at[int_str]
• test_scan_scalar_at[int]
• test_repeated_scan_to_arrow_array[int]
• …and 30 more

⚠️ 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

⚡ 3 improved benchmarks
❌ 3 regressed benchmarks
✅ 2241 untouched benchmarks
🆕 64 new benchmarks
⏩ 12 skipped benchmarks¹

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	WallTime	threshold_neon[80]	3.7 µs	4.4 µs	-15.57%
❌	WallTime	random_rotating_avx2[(0.9, 0.9, "self_90pct_mask_90pct")]	8.3 µs	9.6 µs	-13.48%
❌	WallTime	random_rotating_avx2[(0.5, 0.5, "self_50pct_mask_50pct")]	8.3 µs	9.2 µs	-10.21%
⚡	Simulation	take_map[(0.1, 1.0)]	352.2 µs	221.2 µs	+59.22%
⚡	Simulation	take_map[(0.1, 0.5)]	182.6 µs	146.4 µs	+24.72%
⚡	Simulation	take_map[(0.1, 0.05)]	87 µs	77.4 µs	+12.42%
🆕	Simulation	take[(1000, 1, 1)]	N/A	145 µs	N/A
🆕	Simulation	take[(1000, 1, 249)]	N/A	152.3 µs	N/A
🆕	Simulation	take[(1000, 16, 1)]	N/A	316.2 µs	N/A
🆕	Simulation	take[(1000, 16, 249)]	N/A	324.8 µs	N/A
🆕	Simulation	take[(1000, 2, 1)]	N/A	160.4 µs	N/A
🆕	Simulation	take[(1000, 2, 249)]	N/A	168.1 µs	N/A
🆕	Simulation	take[(1000, 32, 1)]	N/A	514.7 µs	N/A
🆕	Simulation	take[(1000, 32, 249)]	N/A	523.3 µs	N/A
🆕	Simulation	take[(1000, 4, 1)]	N/A	155.7 µs	N/A
🆕	Simulation	take[(1000, 4, 249)]	N/A	164.4 µs	N/A
🆕	Simulation	take[(1000, 64, 1)]	N/A	904.1 µs	N/A
🆕	Simulation	take[(1000, 64, 249)]	N/A	912.9 µs	N/A
🆕	Simulation	take[(1000, 8, 1)]	N/A	212.3 µs	N/A
🆕	Simulation	take[(1000, 8, 249)]	N/A	220.4 µs	N/A
...	...	...	...	...	...

ℹ️ Only the first 20 benchmarks are displayed. Go to the app to view all benchmarks.

Tip

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

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_{Comparing db/fsst-take-benchmarks (8c628e8) with develop (6eae823)}

Footnotes

1. 12 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. ↩