issue_comments

Table actions

1 row where author_association = "NONE" and issue = 1340994913 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

issue 1

  • Memory Leakage Issue When Running to_netcdf · 1 ✖

author_association 1

  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1219672919 https://github.com/pydata/xarray/issues/6924#issuecomment-1219672919 https://api.github.com/repos/pydata/xarray/issues/6924 IC_kwDOAMm_X85IsrtX lassiterdc 64621312 2022-08-18T16:05:02Z 2022-08-18T16:05:02Z NONE

I cross posted this as a dask issue and on stack overflow. I learned that "dask will often have as many chunks in memory as twice the number of active threads" (best practices with dask arrays) and including dask.config.set(scheduler='synchronous') which forces single thread computation (dask scheduling) resulted in the behavior I expected which is memory usage fluctuations that are roughly the magnitude of the chunk size. 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Memory Leakage Issue When Running to_netcdf  1340994913

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]);