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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
610517472 MDU6SXNzdWU2MTA1MTc0NzI= 103 sqlite3.OperationalError: too many SQL variables in insert_all when using rows with varying numbers of columns b0b5h4rp13 32605365 closed 0     8 2020-05-01T02:26:14Z 2020-05-14T00:18:57Z 2020-05-14T00:18:57Z CONTRIBUTOR  

If using insert_all to put in 1000 rows of data with varying number of columns, it comes up with this message sqlite3.OperationalError: too many SQL variables if the number of columns is larger in later records (past the first row)

I've reduced SQLITE_MAX_VARS by 100 to 899 at the top of db.py to add wiggle room, so that if the column count increases it wont go past SQLite's batch limit as calculated by this line of code based on the count of the first row's dict keys

    batch_size = max(1, min(batch_size, SQLITE_MAX_VARS // num_columns))
sqlite-utils 140912432 issue     
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589801352 MDExOlB1bGxSZXF1ZXN0Mzk1MjU4Njg3 96 Add type conversion for Panda's Timestamp b0b5h4rp13 32605365 closed 0     2 2020-03-29T14:13:09Z 2020-03-31T04:40:49Z 2020-03-31T04:40:48Z CONTRIBUTOR simonw/sqlite-utils/pulls/96

Add type conversion for Panda's Timestamp, if Panda library is present in system (thanks for this project, I was about to do the same thing from scratch)

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