benchmarks: benchmark dask

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
data, performance

Research direction

Start with the linked pull request discussion to understand the requested benchmark and inspect the repository for its existing benchmark entry point. Add coverage for randomly generated Dask arrays using the shape, dtype, chunks, and mapping approach shown in the issue; done means the Dask benchmark runs alongside the existing benchmarks.

Written by the indexing model from the issue text.

Description

component: benchmark type: dask

See https://github.com/scverse/fast-array-utils/pull/30#discussion_r1965881913

We could create random dask arrays something like this:

import dask.array as da

arr = da.zeros(shape, dtype=dtype, chunks=_half_chunk_size(shape))
return arr.map_blocks(
    lambda x: self.random(x.shape, dtype=x.dtype, gen=gen), dtype=dtype
)

cc @ilan-gold

Dominant language
Python
Stars
15
Forks
5
Avg merge
10h 33m
Merged PRs (30d)
9

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  4. Open a pull request that references the issue number.

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