issue_comments

Table actions

1 row where author_association = "MEMBER", issue = 417356439 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

These facets timed out: author_association, issue

user 1

  • shoyer · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
469841163 https://github.com/pydata/xarray/issues/2801#issuecomment-469841163 https://api.github.com/repos/pydata/xarray/issues/2801 MDEyOklzc3VlQ29tbWVudDQ2OTg0MTE2Mw== shoyer 1217238 2019-03-05T20:16:52Z 2019-03-05T20:16:52Z MEMBER
  1. x[x > 2] silently resolves the dask graph. It definitely shouldn't.

Yes, agreed.

There needs to be some discussion on what needs to happen to indices on the NaN-sized dimension

The simple answer is to not allow undefined indices on NaN sized dimensions for now ("hard crash"). I don't think silently dropping indexes is a good idea.

Long term (after https://github.com/pydata/xarray/issues/1603 is complete), I can imagine supporting some sort of "lazy index" class.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   NaN-sized chunks 417356439

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,
   [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]);