issue_comments

Table actions

1 row where author_association = "CONTRIBUTOR", "created_at" is on date 2021-10-07 and "updated_at" is on date 2021-10-07 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 1

author_association 1

  • CONTRIBUTOR · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions issue performed_via_github_app
938171377 https://github.com/simonw/datasette/issues/1480#issuecomment-938171377 https://api.github.com/repos/simonw/datasette/issues/1480 IC_kwDOBm6k_c4361vx ghing 110420 2021-10-07T21:33:12Z 2021-10-07T21:33:12Z CONTRIBUTOR

Thanks for the reply @simonw. What services have you had better success with than Cloud Run for larger database?

Also, what about my issue description makes you think there may be a workaround?

Is there any instrumentation I could add to see at which point in the deploy the memory usage spikes? Should I be able to see this whether it's running under Docker locally, or do you suspect this is Cloud Run-specific?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
 Exceeding Cloud Run memory limits when deploying a 4.8G database 1015646369  

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]);