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
729117202,MDU6SXNzdWU3MjkxMTcyMDI=,4539,Failing main branch — test_save_mfdataset_compute_false_roundtrip,5635139,closed,0,,,11,2020-10-25T21:22:36Z,2023-09-21T06:48:03Z,2023-09-20T19:57:17Z,MEMBER,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

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We had the main branch passing for a while, but unfortunately another test failure. Now in our new `Linux py38-backend-api-v2` test case, in`test_save_mfdataset_compute_false_roundtrip`

[link](https://dev.azure.com/xarray/xarray/_build/results?buildId=4089&view=ms.vss-test-web.build-test-results-tab&runId=63370&resultId=101311&paneView=debug)

```
self = <xarray.tests.test_backends.TestDask object at 0x7f821a0d6190>

    def test_save_mfdataset_compute_false_roundtrip(self):
        from dask.delayed import Delayed
    
        original = Dataset({""foo"": (""x"", np.random.randn(10))}).chunk()
        datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))]
        with create_tmp_file(allow_cleanup_failure=ON_WINDOWS) as tmp1:
            with create_tmp_file(allow_cleanup_failure=ON_WINDOWS) as tmp2:
                delayed_obj = save_mfdataset(
                    datasets, [tmp1, tmp2], engine=self.engine, compute=False
                )
                assert isinstance(delayed_obj, Delayed)
                delayed_obj.compute()
                with open_mfdataset(
                    [tmp1, tmp2], combine=""nested"", concat_dim=""x""
                ) as actual:
>                   assert_identical(actual, original)
E                   AssertionError: Left and right Dataset objects are not identical
E                   
E                   
E                   Differing data variables:
E                   L   foo      (x) float64 dask.array<chunksize=(5,), meta=np.ndarray>
E                   R   foo      (x) float64 dask.array<chunksize=(10,), meta=np.ndarray>

/home/vsts/work/1/s/xarray/tests/test_backends.py:3274: AssertionError

AssertionError: Left and right Dataset objects are not identical

Differing data variables:
L   foo      (x) float64 dask.array<chunksize=(5,), meta=np.ndarray>
R   foo      (x) float64 dask.array<chunksize=(10,), meta=np.ndarray>
```

@aurghs & @alexamici — are you familiar with this? Thanks in advance","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4539/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
160505403,MDU6SXNzdWUxNjA1MDU0MDM=,884,Iterating over a Dataset iterates only over its data_vars,5635139,closed,0,,2856429,11,2016-06-15T19:35:50Z,2018-10-25T15:26:59Z,2018-10-25T15:26:59Z,MEMBER,,,,"This has been a small-but-persistent issue for me for a while. I suspect that my perspective might be dependent on my current outlook, but socializing it here to test if it's secular...

Currently `Dataset.keys()` returns both variables and coordinates (but not its `attrs` keys):

``` python
In [5]: ds=xr.Dataset({'a': (('x', 'y'), np.random.rand(10,2))})
In [12]: list(ds.keys())
Out[12]: ['a', 'x', 'y']
```

Is this conceptually correct? I would posit that a Dataset is a mapping of _keys to variables_, and the coordinates contain values that _label_ that data. 

**So should `Dataset.keys()` instead return just the keys of the Variables?**

We're often passing around a dataset as a `Mapping` of keys to values - but then when we run a function across each of the keys, we get something run on both the Variables' keys, _and_ the Coordinate / label's keys.

In Pandas, `DataFrame.keys()` returns just the columns, so that conforms to what we need. While I think the xarray design is in general much better in these areas, this is one area that pandas seems to get correct - and because of the inconsistency between pandas & xarray, we're having to coerce our objects to pandas `DataFrame`s before passing them off to functions that pull out their keys (this is also why we can't just look at `ds.data_vars.keys()` - because it breaks that duck-typing).

Does that make sense?
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