issues
2 rows where comments = 25, repo = 107914493 and type = "issue" sorted by updated_at descending
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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at ▲ | closed_at | author_association | pull_request | body | repo | type | active_lock_reason | performed_via_github_app | reactions | draft | state_reason |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1662951875 | I_kwDOBm6k_c5jHqHD | 2057 | DeprecationWarning: pkg_resources is deprecated as an API | simonw 9599 | closed | 0 | 25 | 2023-04-11T17:41:20Z | 2023-09-21T22:09:10Z | 2023-09-21T22:09:10Z | OWNER | Got this running tests against Python 3.11.
I ran with |
datasette 107914493 | issue | {
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completed | ||||||
| 639993467 | MDU6SXNzdWU2Mzk5OTM0Njc= | 850 | Proof of concept for Datasette on AWS Lambda with EFS | simonw 9599 | open | 0 | 25 | 2020-06-16T21:48:31Z | 2020-06-16T23:52:16Z | OWNER | If Datasette can run on Lambda with access to EFS it could both read AND write large databases there. |
datasette 107914493 | issue | {
"url": "https://api.github.com/repos/simonw/datasette/issues/850/reactions",
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CREATE TABLE [issues] (
[id] INTEGER PRIMARY KEY,
[node_id] TEXT,
[number] INTEGER,
[title] TEXT,
[user] INTEGER REFERENCES [users]([id]),
[state] TEXT,
[locked] INTEGER,
[assignee] INTEGER REFERENCES [users]([id]),
[milestone] INTEGER REFERENCES [milestones]([id]),
[comments] INTEGER,
[created_at] TEXT,
[updated_at] TEXT,
[closed_at] TEXT,
[author_association] TEXT,
[pull_request] TEXT,
[body] TEXT,
[repo] INTEGER REFERENCES [repos]([id]),
[type] TEXT
, [active_lock_reason] TEXT, [performed_via_github_app] TEXT, [reactions] TEXT, [draft] INTEGER, [state_reason] TEXT);
CREATE INDEX [idx_issues_repo]
ON [issues] ([repo]);
CREATE INDEX [idx_issues_milestone]
ON [issues] ([milestone]);
CREATE INDEX [idx_issues_assignee]
ON [issues] ([assignee]);
CREATE INDEX [idx_issues_user]
ON [issues] ([user]);