issue_comments: 398575620
This data as json
| 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/2237#issuecomment-398575620 | https://api.github.com/repos/pydata/xarray/issues/2237 | 398575620 | MDEyOklzc3VlQ29tbWVudDM5ODU3NTYyMA== | 306380 | 2018-06-19T23:20:23Z | 2018-06-19T23:20:23Z | MEMBER | It's also probably worth thinking about the kind of operations you're trying to do, and how streamable they are. For example, if you were to take a dataset that was partitioned chronologically by month and then do some sort of day-of-month grouping then that would require the full dataset to be in memory at once. If you're doing something like grouping on every month (keeping months of different years separate) then presumably your index is already sorted, and so you should be fine with the current behavior. It might be useful to take a look at how the various XArray cases you care about convert to dask array slicing operations. |
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