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https://issues.apache.org/jira/browse/ARROW-18433?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alenka Frim updated ARROW-18433:
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Summary: [C++][Python] Optimize aggregate functions to work with batches.
(was: Optimize aggregate functions to work with batches.)
> [C++][Python] Optimize aggregate functions to work with batches.
> ----------------------------------------------------------------
>
> Key: ARROW-18433
> URL: https://issues.apache.org/jira/browse/ARROW-18433
> Project: Apache Arrow
> Issue Type: New Feature
> Components: C++, Python
> Affects Versions: 10.0.1
> Reporter: A. Coady
> Priority: Major
>
> Most compute functions work with the dataset api and don't load columns. But
> aggregate functions which are associative could also work: `min`, `max`,
> `any`, `all`, `sum`, `product`. Even `unique` and `value_counts`.
> A couple of implementation ideas:
> * expand the dataset api to support expressions which return scalars
> * add a `BatchedArray` type which is like a `ChunkedArray` but with lazy
> loading
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