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/4209#issuecomment-656403217,https://api.github.com/repos/pydata/xarray/issues/4209,656403217,MDEyOklzc3VlQ29tbWVudDY1NjQwMzIxNw==,2448579,2020-07-09T23:43:17Z,2020-07-09T23:43:17Z,MEMBER,"Here's an alternative `map_blocks` solution:

``` python
def write_block(ds, t0):
    if len(ds.time) > 0:
        fname = (ds.time[0] - t0).values.astype(""timedelta64[h]"").astype(int)
        ds.to_netcdf(f""temp/file-{fname:06d}.nc"")

    # dummy return
    return ds.time


ds = xr.tutorial.open_dataset(""air_temperature"", chunks={""time"": 100})
ds.map_blocks(write_block, kwargs=dict(t0=ds.time[0])).compute(scheduler=""processes"")
```

There are two workarounds here though. 
1. The user function always has to return something.
2. We can't provide `template=ds.time` because it has no chunk information and `ds.time.chunk({""time"": 100})` silently does nothing because it is an IndexVariable. So the user function still needs the `len(ds.time) > 0` workaround.

I think a cleaner API may be to have `dask.compute([write_block(block) for block in ds.to_delayed()])` where `ds.to_delayed()` yields a bunch of tasks; each of which gives a Dataset wrapping one block of the underlying array.
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