issue_comments: 258524115

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/1024#issuecomment-258524115 https://api.github.com/repos/pydata/xarray/issues/1024 258524115 MDEyOklzc3VlQ29tbWVudDI1ODUyNDExNQ== 1217238 2016-11-04T19:19:00Z 2016-11-04T19:19:00Z MEMBER

I admit that currently xarray is perhaps not very suited for handling unstructured meshes, but IMO it has great potential (especially considering multi-index support) and I'd love to use it here.

Right now, xarray is not going to be great fit for such cases, because we already cache an index in memory for any labeled indexing operations. So at best, you could do something like ds.isel(mesh_edge=slice(int(1e6))). Maybe people already do this?

I doubt very many people are relying on this, though indeed, this would include some users of an array database we wrote at my former employer, which did not support different chunking schemes for different variables (which could make coordinate lookup very slow). CC @ToddSmall in case he has opinions here.

For out-of-core operations with labels on big unstructured meshes, you really need a generalization of the pandas.Index that doesn't need to live in memory (or maybe that lives in memory on some remote server).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   180451196