issue_comments: 364583951
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-364583951 | https://api.github.com/repos/pydata/xarray/issues/1899 | 364583951 | MDEyOklzc3VlQ29tbWVudDM2NDU4Mzk1MQ== | 1217238 | 2018-02-09T22:10:43Z | 2018-02-09T22:10:43Z | MEMBER | I think the design choice here really comes down to whether we want to enable VectorizedIndexing on arbitrary data on disk or not: Is it better to:
1. Always allow vectorized indexing by means of (lazily) loading all indexed data into memory as a single chunk. This could potentially be very expensive for IO or memory in hard to predict ways.
2. Or to only allow vectorized indexing if a backend supports it directly. This ensures that when vectorized indexing works it works efficiently. Vectorized indexing is still possibly but you have to explicitly write I think I slightly prefer option (2) but I can see the merits in either decision. |
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