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  • rabernat · 1 ✖

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  • Switch to shared Lock (SerializableLock if possible) for reading/writing · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
270193755 https://github.com/pydata/xarray/pull/1179#issuecomment-270193755 https://api.github.com/repos/pydata/xarray/issues/1179 MDEyOklzc3VlQ29tbWVudDI3MDE5Mzc1NQ== rabernat 1197350 2017-01-03T18:59:10Z 2017-01-03T18:59:10Z MEMBER

Is there a clear fail case we can use as a test to demonstrate the value here?

I have found a fail case related to distributed: attempting to use to_netcdf() with a dask.distributed client fails because the threading.Lock() can't be serialized. A SerializableLock would overcome this problem.

Consider this example:

```python import dask.array as da from distributed import Client import xarray as xr

def create_and_store_dataset(): shape = (10000, 1000) chunks = (1000, 1000) data = da.zeros(shape, chunks=chunks) ds = xr.DataArray(data).to_dataset() ds.to_netcdf('test_dataset.nc') print("Success!")

create_and_store_dataset() client = Client() create_and_store_dataset() ```

The first call succeeds, while the second fails with TypeError: can't pickle thread.lock objects.

When using the distributed client, I can successfully call .store on the underlying dask array if I pass lock=SerializableLock().

 
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   Switch to shared Lock (SerializableLock if possible) for reading/writing 197083082

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