html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/pull/1457#issuecomment-308932098,https://api.github.com/repos/pydata/xarray/issues/1457,308932098,MDEyOklzc3VlQ29tbWVudDMwODkzMjA5OA==,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.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,236347050