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

[a10y]

This adds the ability to write in PyVortex directly to object storage, along with more ergonomic configuration of object store credentials for the major PyVortex read/write paths: vx.open, vx.io.read, and vx.io.write, as well as VortexWriteOptions.write.

This is using the https://docs.rs/pyo3-object_store/latest/pyo3_object_store/ crate, which is what runs the obstore Python library.

Notably, we can't reuse obstore and all of its types directly. This is b/c of the way it works, you're required to actually link in pyo3-object_store and then expose it as a new module within your Python bundle.

There are some other examples of this pattern:

• nutpie
• geoarrow

Sadly there's no nicely distributed types package, so I need to copy all of the pyi files here with their original MIT license.

I've added a Python unit test to demonstrate using the new object store builders with simple local file system object store

Resolves #5673

[codecov]

Codecov Report

❌ Patch coverage is 0% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 82.93%. Comparing base (12ba988) to head (cc7a06b).
⚠️ Report is 11 commits behind head on develop.

Files with missing lines	Patch %	Lines
vortex-io/src/write.rs	0.00%	1 Missing ⚠️

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[codspeed-hq]

CodSpeed Performance Report

Merging this PR will degrade performance by 39.87%

_{Comparing aduffy/pyvortex-obstore (2d94025) with develop (1ddae04)}

⚠️ 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. ↩