issue_comments: 361466652
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html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
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https://github.com/pydata/xarray/issues/1836#issuecomment-361466652 | https://api.github.com/repos/pydata/xarray/issues/1836 | 361466652 | MDEyOklzc3VlQ29tbWVudDM2MTQ2NjY1Mg== | 2443309 | 2018-01-30T03:35:07Z | 2018-01-30T03:35:07Z | MEMBER | I tried the above example with the multiprocessing and distributed schedulers. With the multiprocessing scheduler, I can reproduce the error described above. With the distributed scheduler, no error is encountered.
I personally don't have any use cases that would prefer the multiprocessing scheduler over the distributed scheduler but I have been working on improving the I/O performance and stability with xarray and dask lately. If anyone would like to work on this, I'd gladly help this get cleaned up or put a more definitive no on whether or not this can/should work. |
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