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/issues/7079#issuecomment-1404118074,https://api.github.com/repos/pydata/xarray/issues/7079,1404118074,IC_kwDOAMm_X85TsSQ6,2448579,2023-01-25T19:22:52Z,2023-01-25T19:22:52Z,MEMBER,"> o I'm surprised we're not catching this.

Turns out we're running tests on an older working version ([logs](https://github.com/pydata/xarray/actions/runs/4007135596/jobs/6879584623)) even though  we don't have a pin.
```
netcdf4                   1.6.0           nompi_py310h0a86a1f_103    conda-forge
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1385031286
https://github.com/pydata/xarray/issues/7079#issuecomment-1404041288,https://api.github.com/repos/pydata/xarray/issues/7079,1404041288,IC_kwDOAMm_X85Tr_hI,2448579,2023-01-25T18:21:26Z,2023-01-25T19:03:07Z,MEMBER,"From https://github.com/conda-forge/netcdf4-feedstock/issues/141: 

> It's on users to manage locking for non-threadsafe resources like netCDF.

@pydata/xarray ~Should we be handling this by default in the netCDF4 backend now?~

EDIT: We already have locks:
https://github.com/pydata/xarray/blob/6e77f5e8942206b3e0ab08c3621ade1499d8235b/xarray/backends/netCDF4_.py#L363-L383","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1385031286
https://github.com/pydata/xarray/issues/7079#issuecomment-1276693638,https://api.github.com/repos/pydata/xarray/issues/7079,1276693638,IC_kwDOAMm_X85MGMyG,2448579,2022-10-12T20:23:11Z,2022-10-12T20:23:11Z,MEMBER,"> My workflow is my own laptop only

Use LocalCluster! ;)","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 1, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1385031286
https://github.com/pydata/xarray/issues/7079#issuecomment-1276681057,https://api.github.com/repos/pydata/xarray/issues/7079,1276681057,IC_kwDOAMm_X85MGJth,2448579,2022-10-12T20:11:54Z,2022-10-12T20:11:54Z,MEMBER,"> The right fix is to disable threads, like in my example above

This fix will restrict you to serial compute.

You can also parallelize across processes using something like

```python
PBSCluster(
	...,
	cores=1,
	processes=2,
)
```

or `LocalCluster(threads_per_worker=1, ...)`","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1385031286