Recent dask version support assigning to a list of ints along one dimension. we can use this for unstacking (diff builds on #5577)

```diff diff --git i/xarray/core/variable.py w/xarray/core/variable.py index 222e8dab9..a50dfc574 100644 --- i/xarray/core/variable.py +++ w/xarray/core/variable.py @@ -1593,11 +1593,9 @@ class Variable(AbstractArray, NdimSizeLenMixin, VariableArithmetic): else: dtype = self.dtype

• if sparse:
• if sparse and not is_duck_dask_array(reordered): # unstacking a dense multitindexed array to a sparse array

• Use the sparse.COO constructor until sparse supports advanced indexing

• https://github.com/pydata/sparse/issues/114

• TODO: how do we allow different sparse array types

• Use the sparse.COO constructor since we cannot assign to sparse.COO

       from sparse import COO
  
       codes = zip(*index.codes)
  

  @@ -1618,19 +1616,23 @@ class Variable(AbstractArray, NdimSizeLenMixin, VariableArithmetic): )

   else:
  

  • dask supports assigning to a list of ints along one axis only.

  • So we construct an array with the last dimension flattened,

  • assign the values, then reshape to the final shape.

  • intermediate_shape = reordered.shape[:-1] + (np.prod(new_dim_sizes),)
  • indexer = np.ravel_multi_index(index.codes, new_dim_sizes) data = np.full_like( self.data, fill_value=fill_value,
  • shape=new_shape,

  • shape=intermediate_shape, dtype=dtype, )

     # Indexer is a list of lists of locations. Each list is the locations
     # on the new dimension. This is robust to the data being sparse; in that
     # case the destinations will be NaN / zero.
    

    • sparse doesn't support item assigment,

    • https://github.com/pydata/sparse/issues/114

    • data[(..., *indexer)] = reordered
    • data[(..., indexer)] = reordered
    • data = data.reshape(new_shape)

    return self._replace(dims=new_dims, data=data) ```

This should be what alignment.reindex_variables is doing but I don't fully understand that function.

The annoying bit is figuring out when to use this version and what to do with things like dask wrapping sparse. I think we want to loop over each variable in Dataset.unstack calling Variable.unstack and dispatch based on the type of Variable.data to easily handle all the edge cases.

cc @Illviljan if you're interested in implementing this