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  • max-sixty · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
308932098 https://github.com/pydata/xarray/pull/1457#issuecomment-308932098 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMwODkzMjA5OA== max-sixty 5635139 2017-06-16T04:45:49Z 2017-06-16T04:51:14Z MEMBER

This is a great start! Thanks @jhamman !

Our most common performance problems are handling pandas 'oddities', like non-standard indexes. Generally when an operation that is generally vectorized becomes un-vectorized, and starts looping in python. But that's probably not a big use case for most.

What are the instances others have seen performance issues? Are there ever issues with the standard transform operations, such as merge?

(addendum, I just saw the comments above): I think there's some real benefit in benchmarks to ensure we don't add code that slow down operations by an order of magnitude slower - i.e. outside the bounds of reasonable error. That's broader than optimizing around them, particularly since xarray is all python, and shouldn't be doing performance intensive work internally.

 
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   Feature/benchmark 236347050

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