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-1494560788,https://api.github.com/repos/pydata/xarray/issues/7079,1494560788,IC_kwDOAMm_X85ZFTAU,950575,2023-04-03T15:44:18Z,2023-04-03T15:44:18Z,CONTRIBUTOR,"@kthyng those files are on a remote server and that may not be the segfault from the original issue here. It may be a server that is not happy with parallel access. Can you try that with local files?
PS: you can also try with `netcdf4<1.6.1` and, if that also fails, it is most likely the server than the issue here.","{""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-1276668410,https://api.github.com/repos/pydata/xarray/issues/7079,1276668410,IC_kwDOAMm_X85MGGn6,950575,2022-10-12T19:57:35Z,2022-10-12T20:17:08Z,CONTRIBUTOR,"Note that this is not a bug per se, netcdf-c was never thread safe and, when the work around were removed in netcdf4-python, this issue surfaced. The right fix is to disable threads, like in my example above, or to wait for a netcdf-c release that is thread safe. I don't think the work around will be re-added in netcdf4-python.","{""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-1276685512,https://api.github.com/repos/pydata/xarray/issues/7079,1276685512,IC_kwDOAMm_X85MGKzI,950575,2022-10-12T20:16:41Z,2022-10-12T20:16:41Z,CONTRIBUTOR,"> This fix will restrict you to serial compute.
I was waiting for someone who do stuff on clusters to comment on that. Thanks! (My workflow is my own laptop only, so I'm quite limited on that front :smile:)","{""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-1267477522,https://api.github.com/repos/pydata/xarray/issues/7079,1267477522,IC_kwDOAMm_X85LjCwS,950575,2022-10-04T19:24:01Z,2022-10-04T19:34:42Z,CONTRIBUTOR,"Also, you can try:
```python
import dask
dask.config.set(scheduler=""single-threaded"")
```
That would ensure you don't use threads when reading with netcdf-c (netcdf4).
---
Edit: this is not an xarray problem and I recommend to close this issue and follow up with the one already opened upstream.","{""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-1267159210,https://api.github.com/repos/pydata/xarray/issues/7079,1267159210,IC_kwDOAMm_X85Lh1Cq,950575,2022-10-04T15:11:17Z,2022-10-04T15:11:17Z,CONTRIBUTOR,"I believe you are hitting https://github.com/Unidata/netcdf4-python/issues/1192
The verdict is not out on that one yet. Your parallelization may not be thread safe, which makes 1.6.1 failures that expected. For now, if you can, downgrade to 1.6.0 or use an engine that is thread safe. Maybe h5netcdf (not sure!)?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1385031286