issue_comments
1 row where "created_at" is on date 2019-11-07, issue = 512996469, reactions = "{"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0}" and user = 8431341 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Ways to improve fuzzy search speed on larger data sets? · 1 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | issue | performed_via_github_app |
---|---|---|---|---|---|---|---|---|---|---|---|
550649607 | https://github.com/simonw/datasette/issues/607#issuecomment-550649607 | https://api.github.com/repos/simonw/datasette/issues/607 | MDEyOklzc3VlQ29tbWVudDU1MDY0OTYwNw== | zeluspudding 8431341 | 2019-11-07T03:38:10Z | 2019-11-07T03:38:10Z | NONE | I just got FTS5 working and it is incredible! The lookup time for returning all rows where company name contains "Musk" from my table of 16,428,090 rows has dropped from So cool! Thanks again for the pointers and awesome datasette! |
{ "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]);
user 1