issue_comments: 1008163050
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/simonw/sqlite-utils/issues/365#issuecomment-1008163050 | https://api.github.com/repos/simonw/sqlite-utils/issues/365 | 1008163050 | IC_kwDOCGYnMM48F1jq | 9599 | 2022-01-08T22:10:51Z | 2022-01-08T22:10:51Z | OWNER | Is there a downside to having a `sqlite_stat1` table if it has wildly incorrect statistics in it? Imagine the following sequence of events: - User imports a few records, creating the table, using `sqlite-utils insert` - User runs `sqlite-utils create-index ...` which also creates and populates the `sqlite_stat1` table - User runs `insert` again to populate several million new records The user now has a database file with several million records and a statistics table that is wildly out of date, having been populated when they only had a few. Will this result in surprisingly bad query performance compared to it that statistics table did not exist at all? If so, I lean much harder towards `ANALYZE` as a strictly opt-in optimization, maybe with the `--analyze` option added to `sqlite-utils insert` top to help users opt in to updating their statistics after running big inserts. | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 1096558279 |