finish

xarray/core/indexing.py

@@ -473,12 +473,6 @@ def __init__(self, key: tuple[slice | np.ndarray[Any, np.dtype[np.generic]], ...
         for k in key:
             if isinstance(k, slice):
                 k = as_integer_slice(k)
-            # elif is_duck_dask_array(k):
-            #     raise ValueError(
-            #         "Vectorized indexing with Dask arrays is not supported. "
-            #         "Please pass a numpy array by calling ``.compute``. "
-            #         "See https://github.com/dask/dask/issues/8958."
-            #     )
             elif is_duck_array(k):
                 if not np.issubdtype(k.dtype, np.integer):
                     raise TypeError(
@@ -1509,6 +1503,7 @@ def _oindex_get(self, indexer: OuterIndexer):
         return self.array[key]
 
     def _vindex_get(self, indexer: VectorizedIndexer):
+        _assert_not_chunked_indexer(indexer.tuple)
         array = NumpyVIndexAdapter(self.array)
         return array[indexer.tuple]
 
@@ -1620,6 +1615,16 @@ def _apply_vectorized_indexer_dask_wrapper(indices, coord):
     )
 
 
+def _assert_not_chunked_indexer(idxr: tuple[Any, ...]) -> None:
+    if any(is_chunked_array(i) for i in idxr):
+        raise ValueError(
+            "Cannot index with a chunked array indexer. "
+            "Please chunk the array you are indexing first, "
+            "and drop any indexed dimension coordinate variables. "
+            "Alternatively, call `.compute()` on any chunked arrays in the indexer."
+        )
+
+
 class DaskIndexingAdapter(ExplicitlyIndexedNDArrayMixin):
     """Wrap a dask array to support explicit indexing."""
 
@@ -1811,6 +1816,7 @@ def _vindex_get(
         | np.datetime64
         | np.timedelta64
     ):
+        _assert_not_chunked_indexer(indexer.tuple)
         key = self._prepare_key(indexer.tuple)
 
         if getattr(key, "ndim", 0) > 1:  # Return np-array if multidimensional

xarray/tests/test_indexing.py

@@ -974,7 +974,7 @@ def test_indexing_1d_object_array() -> None:
 
 
 @requires_dask
-def test_indexing_dask_array():
+def test_indexing_dask_array() -> None:
     import dask.array
 
     da = DataArray(
@@ -988,7 +988,7 @@ def test_indexing_dask_array():
 
 
 @requires_dask
-def test_indexing_dask_array_scalar():
+def test_indexing_dask_array_scalar() -> None:
     # GH4276
     import dask.array
 
@@ -1002,19 +1002,37 @@ def test_indexing_dask_array_scalar():
 
 
 @requires_dask
-def test_vectorized_indexing_dask_array():
+def test_vectorized_indexing_dask_array() -> None:
     # https://github.com/pydata/xarray/issues/2511#issuecomment-563330352
     darr = DataArray(data=[0.2, 0.4, 0.6], coords={"z": range(3)}, dims=("z",))
     indexer = DataArray(
         data=np.random.randint(0, 3, 8).reshape(4, 2).astype(int),
         coords={"y": range(4), "x": range(2)},
         dims=("y", "x"),
     )
-    darr[indexer.chunk({"y": 2})]
+    expected = darr[indexer]
+
+    # fails because we can't index pd.Index lazily (yet)
+    with pytest.raises(ValueError, match="Cannot index with"):
+        with raise_if_dask_computes():
+            darr.chunk()[indexer.chunk({"y": 2})]
+
+    # fails because we can't index pd.Index lazily (yet)
+    with pytest.raises(ValueError, match="Cannot index with"):
+        with raise_if_dask_computes():
+            actual = darr[indexer.chunk({"y": 2})]
+
+    with raise_if_dask_computes():
+        actual = darr.drop_vars("z").chunk()[indexer.chunk({"y": 2})]
+    assert_identical(actual, expected.drop_vars("z"))
+
+    with raise_if_dask_computes():
+        actual = darr.variable.chunk()[indexer.variable.chunk({"y": 2})]
+    assert_identical(actual, expected.variable)
 
 
 @requires_dask
-def test_advanced_indexing_dask_array():
+def test_advanced_indexing_dask_array() -> None:
     # GH4663
     import dask.array as da