issue_comments
1 row where "created_at" is on date 2019-10-28 sorted by id descending
This data as json, CSV (advanced)
id ▲ | html_url | issue_url | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
---|---|---|---|---|---|---|---|---|---|---|---|
546752311 | https://github.com/simonw/datasette/issues/607#issuecomment-546752311 | https://api.github.com/repos/simonw/datasette/issues/607 | MDEyOklzc3VlQ29tbWVudDU0Njc1MjMxMQ== | zeluspudding 8431341 | 2019-10-28T00:37:10Z | 2019-10-28T00:37:10Z | NONE | UPDATE: According to tips suggested in [Squeezing Performance from SQLite: Indexes? Indexes!](https://medium.com/@JasonWyatt/squeezing-performance-from-sqlite-indexes-indexes-c4e175f3c346) I have added an index to my large table and benchmarked query speeds in the case where I want to return `all rows`, `rows exactly equal to 'Musk Elon'` and, `rows like 'musk'`. Indexing reduced query time for each of those measures and **dramatically** reduced the time to return `rows exactly equal to 'Musk Elon'` as shown below: > table: edgar_idx > rows: 16,428,090 rows > **indexed: False** > Return all rows where company name exactly equal to Musk Elon > query: select rowid, * from edgar_idx where "company" = :p0 order by rowid limit 101 > query time: Query took 21821.031ms > > Return all rows where company name contains Musk > query: select rowid, * from edgar_idx where "company" like :p0 order by rowid limit 101 > query time: Query took 20505.029ms > > Return everything > query: select rowid, * from edgar_idx order by rowid limit 101 > query time: Query took 7985.011ms > > **indexed: True** > Return all rows where company name exactly equal to Musk Elon > query: select rowid, * from edgar_idx where "company" = :p0 order by rowid limit 101 > query time: Query took 30.0ms > > Return all rows where company name contains Musk > query: select rowid, * from edgar_idx where "company" like :p0 order by rowid limit 101 > query time: Query took 13340.019ms > > Return everything > query: select rowid, * from edgar_idx order by rowid limit 101 > query time: Query took 2190.003ms So indexing reduced query time for an exact match to "Musk Elon" from almost `22 seconds` to `30.0ms`. **That's amazing and truly promising!** However, an autocomplete feature relies on fuzzy / incomplete matching, which is more similar to the `contains 'musk'` query... Unfortunately, that takes 13 seconds even after indexing. So the hunt for a fast fuzzy / autocomplete search capability persists. | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | Ways to improve fuzzy search speed on larger data sets? 512996469 |
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, [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]);
created_at (date) 1 ✖