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-318415555,https://api.github.com/repos/pydata/xarray/issues/1457,318415555,MDEyOklzc3VlQ29tbWVudDMxODQxNTU1NQ==,1217238,2017-07-27T16:31:14Z,2017-07-27T16:31:14Z,MEMBER,"Awesome, thanks @TomAugspurger !","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,236347050
https://github.com/pydata/xarray/pull/1457#issuecomment-315273074,https://api.github.com/repos/pydata/xarray/issues/1457,315273074,MDEyOklzc3VlQ29tbWVudDMxNTI3MzA3NA==,1217238,2017-07-14T05:24:04Z,2017-07-14T05:24:04Z,MEMBER,"We should do this to the extent that it is helpful in driving development.
Even just a few realistic use cases can be helpful, especially for guarding
against performance regressions.
On Thu, Jul 13, 2017 at 3:37 PM Joe Hamman <notifications@github.com> wrote:

> @rabernat <https://github.com/rabernat> - do you have any thoughts on
> this?
>
> @pydata/xarray <https://github.com/orgs/pydata/teams/xarray> - I'm trying
> to decide if this is worth spending any more time on. What sort of coverage
> would we want before we merge this first PR?
>
> —
> You are receiving this because you were mentioned.
> Reply to this email directly, view it on GitHub
> <https://github.com/pydata/xarray/pull/1457#issuecomment-315220704>, or mute
> the thread
> <https://github.com/notifications/unsubscribe-auth/ABKS1rVPL6fCRqwi1Szmtq09chkah9odks5sNpwQgaJpZM4N74gy>
> .
>
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,236347050
https://github.com/pydata/xarray/pull/1457#issuecomment-308925978,https://api.github.com/repos/pydata/xarray/issues/1457,308925978,MDEyOklzc3VlQ29tbWVudDMwODkyNTk3OA==,1217238,2017-06-16T03:50:33Z,2017-06-16T03:50:33Z,MEMBER,"@wesm just setup a machine for dedicated benchmarking of pandas and possibly other pydata/scipy project (if there's extra capacity as expected). @TomAugspurger has been working on getting it setup. So that's potentially an option, at least for single machine benchmarks.

The lore I've heard is that benchmarking on shared cloud resources (e.g., Travis-CI) can have reproducibility issues due to resource contention and/or jobs getting scheduled on slightly different machine types. I don't know how true this still is, or if there are good work arounds for particular cloud platforms. I suspect this should be solvable, though. I can certainly make an internal inquiry about benchmarking on GCP if we can't find answers on our own.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,236347050