home / github

Menu
  • Search all tables
  • GraphQL API

issue_comments

Table actions
  • GraphQL API for issue_comments

2 rows where issue = 295621576 and user = 6815844 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

  • fujiisoup · 2 ✖

issue 1

  • Vectorized indexing with cache=False · 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
364298613 https://github.com/pydata/xarray/issues/1897#issuecomment-364298613 https://api.github.com/repos/pydata/xarray/issues/1897 MDEyOklzc3VlQ29tbWVudDM2NDI5ODYxMw== fujiisoup 6815844 2018-02-09T00:45:15Z 2018-02-09T01:11:34Z MEMBER

Or we could switch LazilyIndexedArray to store a chain of successive indexing operations, but that would potentially have non-desirable performance implications.

I think we can store the chain of successive indexing operations, and apply them sequentially when the evaluation. But I am wondering if this operation has an advantage to the eager indexing. (The total computation cost would be the same?)

A workaround would be to take care outer/basic indexers and vectorized indexers separately, i.e, we can combine successive outer/basic indexers as we are doing now, and store the vectorized indexers and apply them at the evaluation time. It would gain some benefit from the lazy indexing (if we can assume vectorized indexing is not so frequent.).

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Vectorized indexing with cache=False 295621576
364281173 https://github.com/pydata/xarray/issues/1897#issuecomment-364281173 https://api.github.com/repos/pydata/xarray/issues/1897 MDEyOklzc3VlQ29tbWVudDM2NDI4MTE3Mw== fujiisoup 6815844 2018-02-08T23:13:40Z 2018-02-08T23:13:40Z MEMBER

I do not yet understand around here, but I guess cache=True implies to load all the data into memory but is still indexed lazily? Is it reasonable to convert this directly to np.ndarray? Or if it is not, the solution would be + Just improve the error message + Support vectorised indexing with LazilyIndexedArray (needs work)

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Vectorized indexing with cache=False 295621576

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]);
Powered by Datasette · Queries took 3037.543ms · About: xarray-datasette
  • Sort ascending
  • Sort descending
  • Facet by this
  • Hide this column
  • Show all columns
  • Show not-blank rows