issue_comments: 367077311
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/pull/1899#issuecomment-367077311 | https://api.github.com/repos/pydata/xarray/issues/1899 | 367077311 | MDEyOklzc3VlQ29tbWVudDM2NzA3NzMxMQ== | 291576 | 2018-02-20T18:43:56Z | 2018-02-20T18:43:56Z | CONTRIBUTOR | I did some more investigation into the memory usage problem I was having. I had assumed that the vectorized indexed result of a lazily indexed data array would be an in-memory array. So, when I then started to use the result, it was then doing a read of all the data at once, resulting in a near-complete load of the data into memory. I have adjusted my code to chunk out the indexing in order to keep the memory usage under control at reasonable performance penalty. I haven't looked into trying to identify the ideal chunking scheme to follow for an arbitrary dataarray and indexing. Perhaps we can make that a task for another day. At this point, I am satisfied with the features (negative step-sizes aside, of course). |
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