issue_comments

Table actions

1 row where issue = 1733198948

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date)

id ▼ html_url issue_url node_id user created_at updated_at author_association body reactions issue performed_via_github_app
1592047502 https://github.com/simonw/sqlite-utils/issues/555#issuecomment-1592047502 https://api.github.com/repos/simonw/sqlite-utils/issues/555 IC_kwDOCGYnMM5e5LeO chapmanjacobd 7908073 2023-06-14T22:00:10Z 2023-06-14T22:01:57Z CONTRIBUTOR You may want to try doing a performance comparison between this and just selecting all the ids with few constraints and then doing the filtering within python. That might seem like a lazy-programmer, inefficient way but queries with large resultsets are a different profile than what databases like SQLITE are designed for. That is not to say that SQLITE is slow or that python is always faster but when you start reading >20% of an index there is an equilibrium that is reached. Especially when adding in writing extra temp tables and stuff to memory/disk. And especially given the `NOT IN` style of query... You may also try chunking like this: ```py def chunks(lst, n) -> Generator: for i in range(0, len(lst), n): yield lst[i : i + n] SQLITE_PARAM_LIMIT = 32765 data = [] chunked = chunks(video_ids, consts.SQLITE_PARAM_LIMIT) for ids in chunked: data.expand( list( db.query( f"""SELECT * from videos WHERE id in (""" + ",".join(["?"] * len(ids)) + ")", (*ids,), ) ) ) ``` but that actually won't work with your `NOT IN` requirements. You need to query the full resultset to check any row. Since you are doing stuff with files/videos in SQLITE you might be interested in my side project: https://github.com/chapmanjacobd/library {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} Filter table by a large bunch of ids 1733198948  

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

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]);