issue_comments

Table actions

1 row where issue = 628719058 and user = 39640592 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

  • emilbiju · 1 ✖

issue 1

  • Feature Request: Hierarchical storage and processing in xarray · 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
637382925 https://github.com/pydata/xarray/issues/4118#issuecomment-637382925 https://api.github.com/repos/pydata/xarray/issues/4118 MDEyOklzc3VlQ29tbWVudDYzNzM4MjkyNQ== emilbiju 39640592 2020-06-02T08:33:42Z 2020-06-02T08:33:42Z NONE

Thanks @jhamman for sharing the link. Here are my thoughts on the same:

For use-cases similar to the one I have mentioned, I think it would be more meaningful to allow the tree structure (calling it Datatree further) to exist as a separate data structure instead of residing within the Dataset. From what I understand, the xarray Dataset would enforce all its component variables to share the same coordinate set for a given dimension name. This would again result in memory wastage with nan values when the value corresponding to a coordinate is unknown.

Besides, xarray only allows attribute access for getting (and not setting) values, but a separate data structure can allow attribute access for setting values as well. For example, the data structure that I have implemented would allow something like dt.weather = dt.weather.mean('time') to alter all the data arrays under the weather node.

I am currently using attribute-based access for accessing child nodes/data arrays in the Datatree as it appears to reflect the tree structure better, but as @shoyer has pointed out, tuple-based access might be easier to use programmatically.

Instead of using netCDF4 groups for encoding the Datatree, I am currently following a simple 3-step process: - Combine all the data arrays at the leaves of a Datatree object into a dataset. - Add an additional data array to the dataset that would contain an ancestor matrix (or any other array-like representation) that can encode the hierarchical structure with a coordinate set containing names of the tree nodes. - Use the xarray.Dataset.to_netcdf method to store it in a netCDF file.

Therefore, within the netCDF file, it would exist just as a Dataset. A specially implemented Datatree.open_datatree method can open the dataset, detect this additional array and recreate the tree structure to instantiate the object. I would like to know if using netCDF4 groups instead provide any advantages over this approach?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Feature Request: Hierarchical storage and processing in xarray 628719058

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]);