issue_comments: 142482576
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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/585#issuecomment-142482576 | https://api.github.com/repos/pydata/xarray/issues/585 | 142482576 | MDEyOklzc3VlQ29tbWVudDE0MjQ4MjU3Ng== | 1217238 | 2015-09-23T03:49:46Z | 2017-03-07T05:32:28Z | MEMBER | Indeed, there's no need to load the entire dataset into memory first. I think open_mfdataset is the model to emulate here -- it's parallelism that just works. I'm not quite sure how to do this transparently in groupby operations yet. The problem is that you do want to apply some groupby operations on dask arrays without loading the entire group into memory, if there are only a few groups on a large datasets and the function itself is written in terms of dask operations. I think we will probably need some syntax to disambiguate that scenario. |
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