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1 row where author_association = "MEMBER", issue = 293293632 and user = 5635139 sorted by updated_at descending

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user 1

  • max-sixty · 1 ✖

issue 1

  • running out of memory trying to write SQL · 1 ✖

author_association 1

  • MEMBER · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
362072512 https://github.com/pydata/xarray/issues/1874#issuecomment-362072512 https://api.github.com/repos/pydata/xarray/issues/1874 MDEyOklzc3VlQ29tbWVudDM2MjA3MjUxMg== max-sixty 5635139 2018-01-31T21:13:49Z 2018-01-31T21:13:49Z MEMBER

There's no xarray->SQL connector, unfortunately.

I don't have that much experience here so I'll let other chime in. You could try chunking to pandas and then to Postgres (but you'll always be limited by memory with pandas). If there's a NetCDF -> tabular connector, that would allow you to operate beyond memory.

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  running out of memory trying to write SQL 293293632

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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,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);
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