issue_comments

Table actions

2 rows where author_association = "MEMBER" and issue = 1339921253 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

issue 1

  • Parallel read with MPI · 2 ✖

author_association 1

  • MEMBER · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1222533244 https://github.com/pydata/xarray/issues/6919#issuecomment-1222533244 https://api.github.com/repos/pydata/xarray/issues/6919 IC_kwDOAMm_X85I3mB8 dcherian 2448579 2022-08-22T15:39:18Z 2022-08-22T15:39:18Z MEMBER

I don't know. I think we just hand off to netcdf4-python to figure it out.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Parallel read with MPI 1339921253
1216823659 https://github.com/pydata/xarray/issues/6919#issuecomment-1216823659 https://api.github.com/repos/pydata/xarray/issues/6919 IC_kwDOAMm_X85Ih0Fr dcherian 2448579 2022-08-16T15:49:29Z 2022-08-16T15:49:29Z MEMBER

Haven't used it but you could consider reading with dask chunks and then using dask-mpi

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Parallel read with MPI 1339921253

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]);