issue_comments: 531499393

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https://github.com/pydata/xarray/issues/3306#issuecomment-531499393 https://api.github.com/repos/pydata/xarray/issues/3306 531499393 MDEyOklzc3VlQ29tbWVudDUzMTQ5OTM5Mw== 1197350 2019-09-14T17:47:10Z 2019-09-14T17:47:10Z MEMBER

What if you just use a dask local cluster, rather than a distributed cluster? Then you can just write to a local directory.

And what if you don’t use a distributed cluster at all, just the threaded scheduler?

In my experience with these problems, by systematically removing layers of complexity from the scenario, we often come to the root of the issue

On Sep 14, 2019, at 11:03 AM, Aimee Barciauskas notifications@github.com wrote:

@rabernat good points. One thing I'm not sure of how to make reproducible is calling a remote file store, since I think it usually requires calling to a write-protected cloud storage provider. Any tips on this?

I have what should be an otherwise working example here: https://gist.github.com/abarciauskas-bgse/d0aac2ae9bf0b06f52a577d0a6251b2d - let me know if this is an ok format to share for reproducing the issue.

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