issue_comments: 1483092471

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/7657#issuecomment-1483092471 https://api.github.com/repos/pydata/xarray/issues/7657 1483092471 IC_kwDOAMm_X85YZjH3 14808389 2023-03-24T16:35:17Z 2023-03-24T16:37:45Z MEMBER

depends on the test, I guess. Most of them are related to one of the netcdf backends (not sure which, the tests don't specify that), test_dataarray_compute just checks _in_memory (no I/O involved), and the pydap tests use netcdf underneath. So I'd say the issue is with one of the netcdf backends (netcdf4, as that's first in the priority list).

I've also seen a drastic reduction in performance with HDF5 1.12.2 (both netcdf4 and h5netcdf) on one of my colleague's datasets, so maybe that's much more visible on a mac? That doesn't explain the slow test_dataarray_compute, though.

Do we isolate the dask scheduler in any way? I assume that makes use of the builtin (non-distributed) scheduler?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   1635470616