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feat[python]: support full read/write from object storage #6022

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CodSpeed HQ / CodSpeed Performance Analysis failed Jan 22, 2026 in 0s

Performance Regression: -39.87%

⚠️ 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.

Summary

⚡ 5 improved benchmarks
❌ 15 regressed benchmarks
✅ 1254 untouched benchmarks
⏩ 1254 skipped benchmarks1

⚠️ Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

Mode Benchmark BASE HEAD Efficiency
WallTime u8_FoR[1K] 10.2 µs 8.8 µs +16.94%
WallTime u8_FoR[10M] 7 µs 7.8 µs -10.05%
WallTime u16_FoR[1M] 6.2 µs 7.5 µs -17.07%
WallTime u64_FoR[10K] 8 µs 13.4 µs -39.87%
WallTime u8_FoR[10K] 7 µs 6 µs +18.02%
WallTime u8_FoR[1M] 5.7 µs 7.7 µs -25.4%
WallTime u16_FoR[10K] 6.4 µs 7.8 µs -17.76%
Simulation canonical_into_non_nullable[(10000, 1, 0.01)] 36 µs 44.2 µs -18.45%
Simulation canonical_into_non_nullable[(10000, 1, 0.0)] 30.9 µs 39 µs -20.79%
Simulation canonical_into_non_nullable[(10000, 1, 0.1)] 52 µs 60.2 µs -13.71%
Simulation canonical_into_non_nullable[(10000, 100, 0.0)] 1.9 ms 2.7 ms -29.5%
Simulation canonical_into_non_nullable[(10000, 100, 0.01)] 2.1 ms 2.9 ms -27.4%
Simulation canonical_into_non_nullable[(10000, 100, 0.1)] 3.7 ms 4.5 ms -18.03%
Simulation canonical_into_nullable[(10000, 10, 0.0)] 528.8 µs 444.6 µs +18.93%
Simulation into_canonical_non_nullable[(10000, 100, 0.0)] 1.9 ms 2.7 ms -29.35%
Simulation into_canonical_non_nullable[(10000, 100, 0.1)] 3.8 ms 4.6 ms -17.82%
Simulation into_canonical_non_nullable[(10000, 100, 0.01)] 2.2 ms 3 ms -26.73%
Simulation into_canonical_nullable[(10000, 10, 0.1)] 710.5 µs 632.3 µs +12.37%
Simulation into_canonical_nullable[(10000, 10, 0.0)] 537.4 µs 452.3 µs +18.82%
Simulation into_canonical_nullable[(10000, 100, 0.0)] 4.4 ms 5.2 ms -15.77%

Footnotes

  1. 1254 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.