id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
546562676,MDU6SXNzdWU1NDY1NjI2NzY=,3668,open_mfdataset: support for multiple zarr datasets,3922329,closed,0,,,14,2020-01-07T23:29:41Z,2020-09-22T05:40:31Z,2020-09-22T05:40:31Z,NONE,,,,"I am running calculations on a remote Dask cluster. Some of the data is only available on the workers, not on the client. It is already possible to have an xarray dataset that ""points"" to a remote NetCDF data collection by using the `parallel` option with `xarray.open_mfdataset()` like this:

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

client = Client('<dask_scheduler_ip>:<port>')
ds = xr.open_mfdataset(remote_nc_file_paths, combine='by_coords', parallel=True)
```

Then it will use `dask.delayed` and, for example, the following simple mean calculation will be distributed between the workers, the result returned to the client:

```python
ds['Temp'].mean().compute()
``` 

Unfortunately, I cannot do the same thing with zarr, because `open_mfdataset()` does not support it, and `open_zarr()` does not have an option to utilize `dask.delayed`. Would it be possible to add `dask.delayed` support to the zarr backend? Or, perhaps, I am missing something, and there is another better way to work with zarr data on a remote Dask cluster? 

Output of `xr.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7 |Anaconda custom (64-bit)| (default, Oct 23 2018, 19:16:44) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.2.3.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
libhdf5: 1.10.4
libnetcdf: 4.6.3

xarray: 0.14.1
pandas: 0.25.3
numpy: 1.17.3
scipy: 1.4.1
netCDF4: 1.5.3
pydap: None
h5netcdf: None
h5py: 2.8.0
Nio: None
zarr: 2.3.2
cftime: 1.0.4.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 2.9.1
distributed: 2.9.1
matplotlib: 3.1.2
cartopy: None
seaborn: 0.9.0
numbagg: None
setuptools: 40.4.3
pip: 18.1
conda: 4.8.0
pytest: 3.8.2
IPython: 7.0.1
sphinx: 1.8.1
```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3668/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue