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/1179#issuecomment-270193755,https://api.github.com/repos/pydata/xarray/issues/1179,270193755,MDEyOklzc3VlQ29tbWVudDI3MDE5Mzc1NQ==,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()`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,197083082