issue_comments

Table actions

1 row where issue = 279161550 and user = 5635139 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

  • max-sixty · 1 ✖

issue 1

  • dask compute on reduction failes with ValueError · 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
349763381 https://github.com/pydata/xarray/issues/1759#issuecomment-349763381 https://api.github.com/repos/pydata/xarray/issues/1759 MDEyOklzc3VlQ29tbWVudDM0OTc2MzM4MQ== max-sixty 5635139 2017-12-06T20:24:08Z 2017-12-06T20:24:08Z MEMBER

our own assert_eq functions that both invoke the single-threaded scheduler, and also do a variety of other sanity checks like ensuring that the expected and computed dtypes and shapes are the same, that the keynames in graphs are sensible, etc..

What do you think about using that in our tests, i.e. from dask.utils import asset_eq as assert_dask_equals?

Or we could even check for a dask object in our own assert_equals and pass off to dask's?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   dask compute on reduction failes with ValueError 279161550

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]);