issue_comments: 302843502

This data as json

html_url issue_url id node_id user created_at updated_at author_association body reactions performed_via_github_app issue
https://github.com/pydata/xarray/pull/1413#issuecomment-302843502 https://api.github.com/repos/pydata/xarray/issues/1413 302843502 MDEyOklzc3VlQ29tbWVudDMwMjg0MzUwMg== 1197350 2017-05-20T01:51:03Z 2017-05-20T01:51:03Z MEMBER

Since the expensive part (for me) is actually reading all the coordinates, I'm not sure that this PR makes sense any more.

The same thing I am going for here could probably be accomplished by allowing the user to pass join='exact' via open_mfdataset. A related optimization would be to allow the user to pass coords='minimal' (or other concat coords options) via open_mfdataset.

For really big datasets, I think we will want to go the NCML approach, generating the xarray metadata as a pre-processing step. Then we could add a function like open_ncml_dataset to xarray which would parse this metadata and construct the dataset in a more efficient way (i.e. not reading redundant coordinates).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   229474101