For referene the original issue in #2068 was ```python In [4]: ds = xr.Dataset() ...: ds.coords['source'] = (['a', 'b', 'c'], np.random.random((2, 3, 4))) ...: ds.coords['unrelated'] = (['a', 'c'], np.random.random((2, 4))) ...: ds ...: Out[4]: <xarray.Dataset> Dimensions: (a: 2, b: 3, c: 4) Coordinates: source (a, b, c) float64 0.4158 0.07152 0.4258 0.4382 0.6616 0.142 ... unrelated (a, c) float64 0.9318 0.03723 0.4226 0.9472 0.8753 0.7022 ... Dimensions without coordinates: a, b, c Data variables: empty

In [5]: ds['dest-2'] = xr.ones_like(ds['source'].isel(c=0)) ...: ds ...: Out[5]: <xarray.Dataset> Dimensions: (a: 2, b: 3) Coordinates: source (a, b) float64 0.4158 0.6616 0.1583 0.7821 0.221 0.2555 unrelated (a) float64 0.9318 0.8753 Dimensions without coordinates: a, b Data variables: dest-2 (a, b) float64 1.0 1.0 1.0 1.0 1.0 1.0 `` whereds['unrelated']drops dimensionc`. We changed this behavior in #2087, but I think it was a wrong direction.

The previous behavior might be OK as long as unrelated is a coordinate variable.

EDIT: I still feel something strange both in the previous and current behavior of __setitem__ with coord. Generally, as @crusaderky has pointed out, the right join will be a better choice. But In the above example, dropping the dimension of c of 'unrelated' looks also awkward.

Thanks, @crusaderky. The first behavior you pointed out is a bug I think. I raised an issue in #2184, and maybe it should be discussed there.

For the second example,

I think this should be a right join.

I agree with this.

So maybe we can leave the current behavior as is for now (but remove the warning).

Agreed.

~@shoyer, how do you think about the current __setitem__ behavior with conflict dimension coordinate? Should it be outer join as @crusaderky pointed out?~

EDIT: I did not noticed the above comment. I will raise an issue for this.

2087 changed the second behavior.

```python In [1]: import xarray ...: ...: fridge = xarray.Dataset( ...: data_vars={ ...: 'var1': ('fruit', [10]), ...: }, ...: coords={ ...: 'fruit': ('fruit', [1]), ...: 'quality': ('fruit', ['Red Velvet']), ...: }) ...: shopping = xarray.Dataset( ...: data_vars={ ...: 'var1': ('fruit', [20]), ...: }, ...: coords={ ...: 'fruit': ('fruit', [1]), ...: 'quality': ('fruit', ['Tangerine']), ...: }) ...: ...: fridge['var1'] = shopping['var1'] ...:

```

with v10.3 python In [2]: fridge Out[2]: <xarray.Dataset> Dimensions: (fruit: 1) Coordinates: * fruit (fruit) int64 1 quality (fruit) <U9 'Tangerine' Data variables: var1 (fruit) int64 20 and with v10.4 python In [3]: fridge Out[3]: <xarray.Dataset> Dimensions: (fruit: 1) Coordinates: * fruit (fruit) int64 1 quality (fruit) <U10 'Red Velvet' Data variables: var1 (fruit) int64 20

I got cofused... I was wrong. I thought it would be an outer joing

I think we need to discuss two issues here separately. 1. If there are conflicts in dimension coordinate, should it be outer join? 2. If there are no conflicts in dimension coordinate, but there are conflicts in non dimension coordinate, whether left or right should be prioritized?

I think we should discuss dimension coordinte and non-dimenson coordinate separately. I guess @shoyer meant non-dimenson coordinate here.

For dimension coordinte, it is always outer join, if I understand correctly.

I think dataset.update(other) should be equivalent with python for key, value in other.items(): dataset[key] = value as similar to python native dict.

Our .item() ony iterates over data_vars not coordinate. So I think even in dataset.update(other) coordinates from other should be dropped if there is a conflict.