issue_comments: 286509639
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/1308#issuecomment-286509639 | https://api.github.com/repos/pydata/xarray/issues/1308 | 286509639 | MDEyOklzc3VlQ29tbWVudDI4NjUwOTYzOQ== | 7300413 | 2017-03-14T18:05:54Z | 2017-03-14T18:05:54Z | NONE | @shoyer If I increase the size of the longitude chunk anymore, it will almost like using no chunking at all. I guess this dataset is a corner case. I will try increasing doubling that value and see what happens. I hadn't realised that doing a groupby would also reduce the effective chunk size, thanks for pointing that out. I'm using dask without distributed as of now, is there still some way to do the benchmark? I would be more than happy to run it. @rabernat I would definitely favour a cloud based sandbox to try these things out. What would be the stumbling block towards actually setting it up? I have had some recent experience setting up jupyterhub, I can help set that up so that notebooks can be used easily in such an environment. |
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