Adds chunk key encoding to kwargs passed to zarr

doc/internals/zarr-encoding-spec.rst

@@ -77,3 +77,50 @@ re-open it directly with Zarr:
     import shutil
 
     shutil.rmtree("rasm.zarr")
+
+Chunk Key Encoding
+------------------
+
+When writing data to Zarr stores, Xarray supports customizing how chunk keys are encoded
+through the ``chunk_key_encoding`` parameter in the variable's encoding dictionary. This
+is particularly useful when working with Zarr V2 arrays and you need to control the
+dimension separator in chunk keys.
+
+For example, to specify a custom separator for chunk keys:
+
+.. jupyter-execute::
+
+    import xarray as xr
+    import numpy as np
+    from zarr.core.chunk_key_encodings import V2ChunkKeyEncoding
+
+    # Create a custom chunk key encoding with "/" as separator
+    enc = V2ChunkKeyEncoding(separator="/").to_dict()
+
+    # Create and write a dataset with custom chunk key encoding
+    arr = np.ones((42, 100))
+    ds = xr.DataArray(arr, name="var1").to_dataset()
+    ds.to_zarr(
+        "example.zarr",
+        zarr_format=2,
+        mode="w",
+        encoding={"var1": {"chunks": (42, 50), "chunk_key_encoding": enc}},
+    )
+
+The ``chunk_key_encoding`` option accepts a dictionary that specifies the encoding
+configuration. For Zarr V2 arrays, you can use the ``V2ChunkKeyEncoding`` class from
+``zarr.core.chunk_key_encodings`` to generate this configuration. This is particularly
+useful when you need to ensure compatibility with specific Zarr V2 storage layouts or
+when working with tools that expect a particular chunk key format.
+
+.. note::
+    The ``chunk_key_encoding`` option is only relevant when writing to Zarr stores.
+    When reading Zarr arrays, Xarray automatically detects and uses the appropriate
+    chunk key encoding based on the store's format and configuration.
+
+.. jupyter-execute::
+    :hide-code:
+
+    import shutil
+
+    shutil.rmtree("example.zarr")

doc/whats-new.rst

@@ -164,6 +164,8 @@ Bug fixes
   By `Mathias Hauser <https://github.com/mathause>`_.
 - Variables with no temporal dimension are left untouched by :py:meth:`~xarray.Dataset.convert_calendar`. (:issue:`10266`,  :pull:`10268`)
   By `Pascal Bourgault <https://github.com/aulemahal>`_.
+- Enable ``chunk_key_encoding`` in :py:meth:`~xarray.Dataset.to_zarr` for Zarr v2 Datasets (:pull:`10274`)
+  By `BrianMichell <https://github.com/BrianMichell>`_.
 
 Documentation
 ~~~~~~~~~~~~~

xarray/backends/zarr.py

@@ -395,6 +395,7 @@ def extract_zarr_variable_encoding(
         "serializer",
         "cache_metadata",
         "write_empty_chunks",
+        "chunk_key_encoding",
     }
     if zarr_format == 3:
         valid_encodings.add("fill_value")

xarray/tests/test_backends.py

@@ -2274,7 +2274,7 @@
     # Flaky test. Very open to contributions on fixing this
     @pytest.mark.flaky
     def test_roundtrip_coordinates(self) -> None:
         super().test_roundtrip_coordinates()
 
     @requires_cftime
     def test_roundtrip_cftime_bnds(self):
@@ -3691,6 +3691,39 @@
         else:
             yield {}
 
+    def test_chunk_key_encoding_v2(self) -> None:
+        encoding = {"name": "v2", "configuration": {"separator": "/"}}
+
+        # Create a dataset with a variable name containing a period
+        data = np.ones((4, 4))
+        original = Dataset({"var1": (("x", "y"), data)})
+
+        # Set up chunk key encoding with slash separator
+        encoding = {
+            "var1": {
+                "chunk_key_encoding": encoding,
+                "chunks": (2, 2),
+            }
+        }
+
+        # Write to store with custom encoding
+        with self.create_zarr_target() as store:
+            original.to_zarr(store, encoding=encoding)
+
+            # Verify the chunk keys in store use the slash separator
+            if not has_zarr_v3:
+                chunk_keys = [k for k in store.keys() if k.startswith("var1/")]
+                assert len(chunk_keys) > 0
+                for key in chunk_keys:
+                    assert "/" in key
+                    assert "." not in key.split("/")[1:]  # No dots in chunk coordinates
+
+            # Read back and verify data
+            with xr.open_zarr(store) as actual:
+                assert_identical(original, actual)
+                # Verify chunks are preserved
+                assert actual["var1"].encoding["chunks"] == (2, 2)
+
 
 @requires_zarr
 @pytest.mark.skipif(
@@ -5272,7 +5305,7 @@
     def test_dask_roundtrip(self) -> None:
         with create_tmp_file() as tmp:
             data = create_test_data()
             data.to_netcdf(tmp)
             chunks = {"dim1": 4, "dim2": 4, "dim3": 4, "time": 10}
             with open_dataset(tmp, chunks=chunks) as dask_ds:
                 assert_identical(data, dask_ds)
@@ -5393,7 +5426,7 @@
 
     def test_cmp_local_file(self) -> None:
         with self.create_datasets() as (actual, expected):
             assert_equal(actual, expected)
 
             # global attributes should be global attributes on the dataset
             assert "NC_GLOBAL" not in actual.attrs
@@ -5436,7 +5469,7 @@
     @requires_dask
     def test_dask(self) -> None:
         with self.create_datasets(chunks={"j": 2}) as (actual, expected):
             assert_equal(actual, expected)
 
 
 @network