issue_comments

Table actions

1 row where issue = 253476466 and user = 2443309 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

  • jhamman · 1 ✖

issue 1

  • Better compression algorithms for NetCDF · 1 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
325510075 https://github.com/pydata/xarray/issues/1536#issuecomment-325510075 https://api.github.com/repos/pydata/xarray/issues/1536 MDEyOklzc3VlQ29tbWVudDMyNTUxMDA3NQ== jhamman 2443309 2017-08-28T23:22:13Z 2017-08-28T23:22:13Z MEMBER

This is an interesting idea. I think something similar was discussed in #66.

The main problem I see is that current netCDF libraries don't support LZF so I think we're really talking about adding an HDF5 backend. In practice, this could use the new API of h5netcdf or h5py directly. Either way, my initial thought is that we want a to_hdf5() method on the dataset.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Better compression algorithms for NetCDF 253476466

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]);