issue_comments
3 rows where author_association = "MEMBER", issue = 184722754 and user = 6213168 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- shallow copies become deep copies when pickling · 3 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
277543644 | https://github.com/pydata/xarray/issues/1058#issuecomment-277543644 | https://api.github.com/repos/pydata/xarray/issues/1058 | MDEyOklzc3VlQ29tbWVudDI3NzU0MzY0NA== | crusaderky 6213168 | 2017-02-05T19:44:33Z | 2017-02-05T19:44:33Z | MEMBER | Actually, I very much still am facing the problem. The biggest issue is now when I need to invoke xarray.broadcast. In my use case, I'm broadcasting together
What broadcast does is transform the scalar array to a numpy array of 2**19 elements. This is actually a view on the original 0D array, so it's got negligible RAM requirements. But after pickling and unpickling, it's become a real 2**19 elements array. Add up a few hundreds of them, and I am facing GBs of wasted RAM. A solution would be to change broadcast() to convert to dask before broadcasting, and then broadcast directly to the proper chunk size. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
shallow copies become deep copies when pickling 184722754 | |
260773846 | https://github.com/pydata/xarray/issues/1058#issuecomment-260773846 | https://api.github.com/repos/pydata/xarray/issues/1058 | MDEyOklzc3VlQ29tbWVudDI2MDc3Mzg0Ng== | crusaderky 6213168 | 2016-11-15T21:26:52Z | 2016-11-15T21:26:52Z | MEMBER | Confirmed that #1017 fixes my specific issue, thanks! Leaving the ticket open as other people (particularly those that work on large arrays without dask) will still be affected. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
shallow copies become deep copies when pickling 184722754 | |
255952251 | https://github.com/pydata/xarray/issues/1058#issuecomment-255952251 | https://api.github.com/repos/pydata/xarray/issues/1058 | MDEyOklzc3VlQ29tbWVudDI1NTk1MjI1MQ== | crusaderky 6213168 | 2016-10-25T06:54:02Z | 2016-10-25T06:54:02Z | MEMBER | @maximilianr, if you pickle 2 plain python objects A and B together, and one of the attributes of B is a reference to A, A does not get duplicated. In this case there must be some specific getstate code to prevent this and/or something with the C implementation of the class |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
shallow copies become deep copies when pickling 184722754 |
Advanced export
JSON shape: default, array, newline-delimited, object
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]);
user 1