issue_comments
1 row where "created_at" is on date 2018-05-13, issue = 322477187, "updated_at" is on date 2018-05-13 and user = 9599 sorted by updated_at descending
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issue 1
- Facets · 1 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | issue | performed_via_github_app |
---|---|---|---|---|---|---|---|---|---|---|---|
388645828 | https://github.com/simonw/datasette/issues/255#issuecomment-388645828 | https://api.github.com/repos/simonw/datasette/issues/255 | MDEyOklzc3VlQ29tbWVudDM4ODY0NTgyOA== | simonw 9599 | 2018-05-13T18:18:56Z | 2018-05-13T18:20:02Z | OWNER | I may be able to run the SQL for all of the facet counts in one go using a WITH CTE query - will have to microbenchmark this to make sure it is worthwhile: https://datasette-facets-demo.now.sh/fivethirtyeight-2628db9?sql=with+blah+as+%28select++from+%5Bcollege-majors%2Fall-ages%5D%29%0D%0Aselect++from+%28select+%22Major_category%22%2C+Major_category%2C+count%28%29+as+n+from%0D%0Ablah+group+by+Major_category+order+by+n+desc+limit+10%29%0D%0Aunion+all%0D%0Aselect++from+%28select+%22Major_category2%22%2C+Major_category%2C+count%28*%29+as+n+from%0D%0Ablah+group+by+Major_category+order+by+n+desc+limit+10%29 |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Facets 322477187 |
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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]);
user 1