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
309227775,MDU6SXNzdWUzMDkyMjc3NzU=,2022,Enable Append/concat to existing zarr datastore,6101444,closed,0,,,7,2018-03-28T05:39:37Z,2019-06-29T23:43:15Z,2019-06-29T23:43:15Z,NONE,,,,"Following discussion from https://github.com/pangeo-data/pangeo/issues/19

How would we go about implementing a concat or append function for zarr data stores? I am imagining something like xr.concat here. Its not clear to me how this would work when using `open_mfdataset`.

```python
zarray_1 = xr.open_zarr(store=gcsmap)

ds2 = xr.open_dataset(path_to_netcdf)

xr.concat([zarray_1, ds2])
```
#### Problem description

If you are using cloud storage facility like gcs, `ds.to_zarr` can fail before the completion of the upload. This is a problem for multi-TB datasets as the entire process needs to be restarted without any way to resume where you left off. 

#### Expected Output

new zarr dataset with additional dataset appended along appropriate dim

","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2022/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
217729216,MDU6SXNzdWUyMTc3MjkyMTY=,1337,ds.sel_points reassigning non-referenced coordinates as variables ,6101444,closed,0,,,1,2017-03-29T00:14:06Z,2017-05-09T02:31:52Z,2017-05-09T02:31:52Z,NONE,,,,"Python version: 2.7
Xarray Version: 0.9.1

When calling`sel_points` on the data_array coordinates are preserved.  However, `ds.sel_points` reassigns non-referenced coordinates as variables. Is this the expected behavior? 

```
In [2]: ds = xr.Dataset({'tas': 
   ...:         xr.DataArray(
   ...:             np.random.random((19, 18, 100)),
   ...:             dims=('lat', 'lon', 'time'),
   ...:             coords={
   ...:                 'lat': np.arange(-90, 91, 10),
   ...:                 'lon': np.arange(-180, 180, 20),
   ...:                 'time': pd.date_range('2000', '2100', freq='A')})})
   ...: 
In [3]: ds
Out[3]: 
<xarray.Dataset>
Dimensions:  (lat: 19, lon: 18, time: 100)
Coordinates:
  * lat      (lat) int64 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 ...
  * lon      (lon) int64 -180 -160 -140 -120 -100 -80 -60 -40 -20 0 20 40 60 ...
  * time     (time) datetime64[ns] 2000-12-31 2001-12-31 2002-12-31 ...
Data variables:
    tas      (lat, lon, time) float64 0.1865 0.1357 0.6215 0.1499 0.104 ...
In [4]: ds.tas.sel_points('regions', lat=[-80, 0, 10], lon=[60, 20, -20])
Out[4]: 
<xarray.DataArray 'tas' (regions: 3, time: 100)>
array([[ 0.319773,  0.908711,  0.584497, ...,  0.62886 ,  0.080532,  0.466724],
       [ 0.235852,  0.40516 ,  0.963833, ...,  0.589328,  0.066798,  0.80928 ],
       [ 0.098497,  0.287534,  0.629137, ...,  0.951644,  0.71438 ,  0.40434 ]])
Coordinates:
    lat      (regions) int64 -80 0 10
    lon      (regions) int64 60 20 -20
  * time     (time) datetime64[ns] 2000-12-31 2001-12-31 2002-12-31 ...
Dimensions without coordinates: regions

In [5]: ds.sel_points('regions', lat=[-80, 0, 10], lon=[60, 20, -20])
Out[5]: 
<xarray.Dataset>
Dimensions:  (regions: 3, time: 100)
Coordinates:
    lat      (regions) int64 -80 0 10
    lon      (regions) int64 60 20 -20
Dimensions without coordinates: regions, time
Data variables:
    time     (time) datetime64[ns] 2000-12-31 2001-12-31 2002-12-31 ...
    tas      (regions, time) float64 0.3198 0.9087 0.5845 0.5493 0.574 ...
```

","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1337/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue