issue_comments
1 row where "created_at" is on date 2018-05-27 and "updated_at" is on date 2018-06-04 sorted by node_id
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id | html_url | issue_url | node_id ▼ | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
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392343839 | https://github.com/simonw/datasette/issues/292#issuecomment-392343839 | https://api.github.com/repos/simonw/datasette/issues/292 | MDEyOklzc3VlQ29tbWVudDM5MjM0MzgzOQ== | simonw 9599 | 2018-05-27T16:10:09Z | 2018-06-04T17:38:04Z | OWNER | The more efficient way of doing this kind of count would be to provide a mechanism which can also add extra fragments to a `GROUP BY` clause used for the `SELECT`. Or... how about a mechanism similar to Django's `prefetch_related` which lets you define extra queries that will be called with a list of primary keys (or values from other columns) and used to populate a new column? A little unconventional but could be extremely useful and efficient. Related to that: since the per-query overhead in SQLite is tiny, could even define an extra query to be run once-per-row before returning results. | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | Mechanism for customizing the SQL used to select specific columns in the table view 326800219 |
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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, [issue] INTEGER REFERENCES [issues]([id]) , [performed_via_github_app] TEXT); CREATE INDEX [idx_issue_comments_issue] ON [issue_comments] ([issue]); CREATE INDEX [idx_issue_comments_user] ON [issue_comments] ([user]);
updated_at (date) 1 ✖