issue_comments: 308923548
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/1457#issuecomment-308923548 | https://api.github.com/repos/pydata/xarray/issues/1457 | 308923548 | MDEyOklzc3VlQ29tbWVudDMwODkyMzU0OA== | 12229877 | 2017-06-16T03:29:12Z | 2017-06-16T03:29:12Z | CONTRIBUTOR | I like the idea of benchmarks, but have some serious concerns. For Dask and IO-bound work in general, benchmark results will vary widely depending on the hardware and (if relevant) network properties. Results will be noncomparable between SSD and HDD, local and remote network access, and in general depend heavily on the specific IO patterns and storage/compute relationship of the computer. This isn't a reason not to benchmark though, just a call for very cautious interpretation - it's clearly useful to catch some of the subtle-but-pathological performance problems that have cropped up. In short, I think benchmarks should have a very clear warnings section in the documentation, and no decision should be taken to change code without benchmarking on a variety of computers (SSD/HDD, PC/cluster, local/remote data...). Also JSON cannot include comments, and there are a number of entries that you need to update, but that's a passing concern. |
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