issue_comments

Table actions

where issue = 68759727 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: updated_at (date)

These facets timed out: author_association, issue

user 1

  • shoyer · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
94085685 https://github.com/pydata/xarray/issues/392#issuecomment-94085685 https://api.github.com/repos/pydata/xarray/issues/392 MDEyOklzc3VlQ29tbWVudDk0MDg1Njg1 shoyer 1217238 2015-04-17T22:05:10Z 2015-04-17T22:05:10Z MEMBER

Yeah, like I said in the other issue I don't think this is a blocker (we can add a disclaimer to the docs).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Non-aggregating grouped operations on dask arrays are painfully slow to construct 68759727
94079544 https://github.com/pydata/xarray/issues/392#issuecomment-94079544 https://api.github.com/repos/pydata/xarray/issues/392 MDEyOklzc3VlQ29tbWVudDk0MDc5NTQ0 shoyer 1217238 2015-04-17T21:31:56Z 2015-04-17T21:31:56Z MEMBER

The good news about that timing info is that dask is still much faster for calculating the graph than doing the actual computation. But it's still not ideal from an interactivity perspective.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Non-aggregating grouped operations on dask arrays are painfully slow to construct 68759727
94079391 https://github.com/pydata/xarray/issues/392#issuecomment-94079391 https://api.github.com/repos/pydata/xarray/issues/392 MDEyOklzc3VlQ29tbWVudDk0MDc5Mzkx shoyer 1217238 2015-04-17T21:30:47Z 2015-04-17T21:30:47Z MEMBER

Here's the timing info:

``` %time res = ds.t2m.groupby('time.month').mean('time').sum()

CPU times: user 133 ms, sys: 6.39 ms, total: 140 ms

Wall time: 145 ms

%time res.load_data()

CPU times: user 2min 47s, sys: 1min, total: 3min 48s

Wall time: 1min 19s

%time res = ds.t2m.groupby('time.month').apply(lambda x: x - x.mean()).sum()

CPU times: user 49.1 s, sys: 6.39 s, total: 55.5 s

Wall time: 55.1 s

%time res.load_data()

CPU times: user 6min 17s, sys: 2min 20s, total: 8min 38s

Wall time: 3min 25s

```

Blocks shape is {'latitude': (256,), 'longitude': (512,), 'time': (124, 124, 112, ..., 124, 120, 124)}

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Non-aggregating grouped operations on dask arrays are painfully slow to construct 68759727

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]);