issue_comments
1 row where issue = 495869721 and user = 1217238 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- arithmetic resulting in inconsistent chunks · 1 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 533190578 | https://github.com/pydata/xarray/issues/3323#issuecomment-533190578 | https://api.github.com/repos/pydata/xarray/issues/3323 | MDEyOklzc3VlQ29tbWVudDUzMzE5MDU3OA== | shoyer 1217238 | 2019-09-19T15:42:40Z | 2019-09-19T15:42:40Z | MEMBER | I think dask array has some utility functions for "unifying chunks" that we might be able to use inside our map_blocks() function. Potentially we could also make Alternatively, we could enforce matching chunksizes on all dask arrays inside a Dataset, as part of xarray's model of a Dataset as a collection of aligned arrays. But this seems unnecessarily limiting, and I am reluctant to add extra complexity to xarray's data model. |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
arithmetic resulting in inconsistent chunks 495869721 |
Advanced export
JSON shape: default, array, newline-delimited, object
CREATE TABLE [issue_comments] (
[html_url] TEXT,
[issue_url] TEXT,
[id] INTEGER PRIMARY KEY,
[node_id] TEXT,
[user] INTEGER REFERENCES [users]([id]),
[created_at] TEXT,
[updated_at] TEXT,
[author_association] TEXT,
[body] TEXT,
[reactions] TEXT,
[performed_via_github_app] TEXT,
[issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
ON [issue_comments] ([user]);
user 1