Append mode for StoreToZarr

docs/composition/styles.md

@@ -32,6 +32,21 @@ If using the {class}`pangeo_forge_recipes.transforms.ConsolidateDimensionCoordin
 
 ```
 
+```{note}
+{class}`pangeo_forge_recipes.transforms.StoreToZarr` supports appending to existing Zarr stores
+via the optional `append_dim` keyword argument. This option functions nearly identically to the
+`append_dim` kwarg in
+[`xarray.Dataset.to_zarr`](https://docs.xarray.dev/en/latest/generated/xarray.Dataset.to_zarr.html);
+the two differences with this method are that Pangeo Forge will automatically introspect the inputs in
+your {class}`FilePattern <pangeo_forge_recipes.patterns.FilePattern>` to determine how the existing Zarr
+store dimensions need to be resized, and that writes are parallelized via Apache Beam. Apart from
+ensuring that the named `append_dim` already exists in the dataset to which you are appending, use of
+this option does not ensure logical consistency (e.g. contiguousness, etc.) of the appended data. When
+selecting this option, it is therefore up to you, the user, to ensure that the inputs provided in the
+{doc}`file pattern <file_patterns>` for the appending recipe are limited to those which you want to
+append.
+```
+
 
 ## Open with Kerchunk, write to virtual Zarr
 

pangeo_forge_recipes/aggregation.py

@@ -287,13 +287,19 @@ def schema_to_zarr(
     attrs: Optional[Dict[str, str]] = None,
     consolidated_metadata: Optional[bool] = True,
     encoding: Optional[Dict] = None,
+    append_dim: Optional[str] = None,
 ) -> zarr.storage.FSStore:
     """Initialize a zarr group based on a schema."""
+    if append_dim:
+        # if appending, only keep schema for coordinate to append. if we don't drop other
+        # coords, we may end up overwriting existing data on the `ds.to_zarr` call below.
+        schema["coords"] = {k: v for k, v in schema["coords"].items() if k == append_dim}
     ds = schema_to_template_ds(schema, specified_chunks=target_chunks, attrs=attrs)
-    # using mode="w" makes this function idempotent
+    # using mode="w" makes this function idempotent when not appending
     ds.to_zarr(
         target_store,
-        mode="w",
+        append_dim=append_dim,
+        mode=("a" if append_dim else "w"),
         compute=False,
         consolidated=consolidated_metadata,
         encoding=encoding,

pangeo_forge_recipes/patterns.py

@@ -63,18 +63,23 @@ class MergeDim(CombineDim):
     operation: ClassVar[CombineOp] = CombineOp.MERGE
 
 
-def augment_index_with_start_stop(position: Position, item_lens: List[int]) -> IndexedPosition:
+def augment_index_with_start_stop(
+    position: Position,
+    item_lens: List[int],
+    append_offset: int = 0,
+) -> IndexedPosition:
     """Take an index _without_ start / stop and add them based on the lens defined in sequence_lens.
 
     :param index: The ``DimIndex`` instance to augment.
     :param item_lens: A list of integer lengths for all items in the sequence.
+    :param append_offset: If appending, the length of the existing ``append_dim``.
     """
 
     if position.indexed:
         raise ValueError("This position is already indexed")
     start = sum(item_lens[: position.value])
     dimsize = sum(item_lens)
-    return IndexedPosition(start, dimsize=dimsize)
+    return IndexedPosition(start + append_offset, dimsize=dimsize + append_offset)
 
 
 class AutoName(Enum):

pangeo_forge_recipes/transforms.py

@@ -301,21 +301,26 @@ class IndexItems(beam.PTransform):
     """Augment dataset indexes with information about start and stop position."""
 
     schema: beam.PCollection
+    append_offset: int = 0
 
     @staticmethod
-    def index_item(item: Indexed[T], schema: XarraySchema) -> Indexed[T]:
+    def index_item(item: Indexed[T], schema: XarraySchema, append_offset: int) -> Indexed[T]:
         index, ds = item
         new_index = Index()
         for dimkey, dimval in index.items():
             if dimkey.operation == CombineOp.CONCAT:
                 item_len_dict = schema["chunks"][dimkey.name]
                 item_lens = [item_len_dict[n] for n in range(len(item_len_dict))]
-                dimval = augment_index_with_start_stop(dimval, item_lens)
+                dimval = augment_index_with_start_stop(dimval, item_lens, append_offset)
             new_index[dimkey] = dimval
         return new_index, ds
 
     def expand(self, pcoll: beam.PCollection):
-        return pcoll | beam.Map(self.index_item, schema=beam.pvalue.AsSingleton(self.schema))
+        return pcoll | beam.Map(
+            self.index_item,
+            schema=beam.pvalue.AsSingleton(self.schema),
+            append_offset=self.append_offset,
+        )
 
 
 @dataclass
@@ -341,13 +346,15 @@ class PrepareZarrTarget(beam.PTransform):
                                   then falling out of sync with coordinates if
                                   ConsolidateDimensionCoordinates() is applied to the output of
                                   StoreToZarr().
+    :param append_dim: Optional name of the dimension to append to.
     """
 
     target: str | FSSpecTarget
     target_chunks: Dict[str, int] = field(default_factory=dict)
     attrs: Dict[str, str] = field(default_factory=dict)
     consolidated_metadata: Optional[bool] = True
     encoding: Optional[dict] = field(default_factory=dict)
+    append_dim: Optional[str] = None
 
     def expand(self, pcoll: beam.PCollection) -> beam.PCollection:
         if isinstance(self.target, str):
@@ -362,6 +369,7 @@ def expand(self, pcoll: beam.PCollection) -> beam.PCollection:
             attrs=self.attrs,
             encoding=self.encoding,
             consolidated_metadata=False,
+            append_dim=self.append_dim,
         )
         return initialized_target
 
@@ -641,8 +649,8 @@ class StoreToZarr(beam.PTransform, ZarrWriterMixin):
       out https://github.com/jbusecke/dynamic_chunks
     :param dynamic_chunking_fn_kwargs: Optional keyword arguments for ``dynamic_chunking_fn``.
     :param attrs: Extra group-level attributes to inject into the dataset.
-
     :param encoding: Dictionary encoding for xarray.to_zarr().
+    :param append_dim: Optional name of the dimension to append to.
     """
 
     # TODO: make it so we don't have to explicitly specify combine_dims
@@ -657,17 +665,31 @@ class StoreToZarr(beam.PTransform, ZarrWriterMixin):
     dynamic_chunking_fn_kwargs: Optional[dict] = field(default_factory=dict)
     attrs: Dict[str, str] = field(default_factory=dict)
     encoding: Optional[dict] = field(default_factory=dict)
+    append_dim: Optional[str] = None
 
     def __post_init__(self):
         if self.target_chunks and self.dynamic_chunking_fn:
             raise ValueError("Passing both `target_chunks` and `dynamic_chunking_fn` not allowed.")
 
+        self._append_offset = 0
+        if self.append_dim:
+            logger.warn(
+                "When `append_dim` is given, StoreToZarr is NOT idempotent. Successive deployment "
+                "with the same inputs will append duplicate data to the existing store."
+            )
+            dim = [d for d in self.combine_dims if d.name == self.append_dim]
+            assert dim, f"Append dim not in {self.combine_dims=}."
+            assert dim[0].operation == CombineOp.CONCAT, "Append dim operation must be CONCAT."
+            existing_ds = xr.open_dataset(self.get_full_target().get_mapper(), engine="zarr")
+            assert self.append_dim in existing_ds, "Append dim must be in existing dataset."
+            self._append_offset = len(existing_ds[self.append_dim])
+
     def expand(
         self,
         datasets: beam.PCollection[Tuple[Index, xr.Dataset]],
     ) -> beam.PCollection[zarr.storage.FSStore]:
         schema = datasets | DetermineSchema(combine_dims=self.combine_dims)
-        indexed_datasets = datasets | IndexItems(schema=schema)
+        indexed_datasets = datasets | IndexItems(schema=schema, append_offset=self._append_offset)
         target_chunks = (
             self.target_chunks
             if not self.dynamic_chunking_fn
@@ -684,6 +706,7 @@ def expand(
             target_chunks=target_chunks,
             attrs=self.attrs,
             encoding=self.encoding,
+            append_dim=self.append_dim,
         )
         n_target_stores = rechunked_datasets | StoreDatasetFragments(target_store=target_store)
         singleton_target_store = (

tests/conftest.py

@@ -276,6 +276,11 @@ def daily_xarray_dataset():
     return make_ds(nt=10)
 
 
+@pytest.fixture(scope="session")
+def daily_xarray_datasets_to_append():
+    return make_ds(nt=10, start="2010-01-01"), make_ds(nt=10, start="2010-01-11")
+
+
 @pytest.fixture(scope="session")
 def daily_xarray_dataset_with_coordinateless_dimension(daily_xarray_dataset):
     """
@@ -295,6 +300,23 @@ def netcdf_local_paths_sequential_1d(daily_xarray_dataset, tmpdir_factory):
     )
 
 
+@pytest.fixture(scope="session")
+def netcdf_local_paths_sequential_1d_to_append(
+    daily_xarray_datasets_to_append,
+    tmpdir_factory,
+):
+    return [
+        make_local_paths(
+            ds,
+            tmpdir_factory,
+            "D",
+            split_up_files_by_day,
+            file_type="netcdf4",
+        )
+        for ds in daily_xarray_datasets_to_append
+    ]
+
+
 @pytest.fixture(scope="session")
 def netcdf3_local_paths_sequential_1d(daily_xarray_dataset, tmpdir_factory):
     return make_local_paths(
@@ -448,6 +470,11 @@ def netcdf_local_paths_sequential_with_coordinateless_dimension(
 # FilePattern fixtures ----------------------------------------------------------------------------
 
 
+@pytest.fixture(scope="session")
+def netcdf_local_file_patterns_to_append(netcdf_local_paths_sequential_1d_to_append):
+    return [make_file_pattern(paths) for paths in netcdf_local_paths_sequential_1d_to_append]
+
+
 @pytest.fixture(scope="session")
 def netcdf_local_file_pattern_sequential(netcdf_local_paths_sequential):
     return make_file_pattern(netcdf_local_paths_sequential)

tests/data_generation.py

@@ -3,13 +3,13 @@
 import xarray as xr
 
 
-def make_ds(nt=10, non_dim_coords=False):
+def make_ds(nt=10, non_dim_coords=False, start="2010-01-01"):
     """Return a synthetic random xarray dataset."""
     np.random.seed(2)
     # TODO: change nt to 11 in order to catch the edge case where
     # items_per_input does not evenly divide the length of the sequence dimension
     ny, nx = 18, 36
-    time = pd.date_range(start="2010-01-01", periods=nt, freq="D")
+    time = pd.date_range(start=start, periods=nt, freq="D")
     lon = (np.arange(nx) + 0.5) * 360 / nx
     lon_attrs = {"units": "degrees_east", "long_name": "longitude"}
     lat = (np.arange(ny) + 0.5) * 180 / ny

tests/test_aggregation.py

@@ -8,7 +8,9 @@
     dataset_to_schema,
     determine_target_chunks,
     schema_to_template_ds,
+    schema_to_zarr,
 )
+from pangeo_forge_recipes.storage import FSSpecTarget
 
 from .data_generation import make_ds
 
@@ -190,3 +192,41 @@ def test_concat_accumulator():
     assert (
         merge_accumulator.schema["data_vars"]["bar"] == merge_accumulator.schema["data_vars"]["BAR"]
     )
+
+
+def test_schema_to_zarr(daily_xarray_dataset: xr.Dataset, tmp_target: FSSpecTarget):
+    target_store = tmp_target.get_mapper()
+    schema = dataset_to_schema(daily_xarray_dataset)
+    schema_to_zarr(
+        schema=schema,
+        target_store=target_store,
+        target_chunks={},
+        attrs={},
+        consolidated_metadata=False,
+        encoding=None,
+        append_dim=None,
+    )
+    ds = xr.open_dataset(target_store, engine="zarr")
+    assert len(ds.time) == len(daily_xarray_dataset.time)
+    assert len(ds.lon) == len(daily_xarray_dataset.lon)
+    assert len(ds.lat) == len(daily_xarray_dataset.lat)
+
+
+def test_schema_to_zarr_append_mode(
+    daily_xarray_datasets_to_append: tuple[xr.Dataset, xr.Dataset],
+    tmp_target: FSSpecTarget,
+):
+    """Tests dimension resizing for append."""
+
+    ds0, ds1 = daily_xarray_datasets_to_append
+    target_store = tmp_target.get_mapper()
+
+    schema_ds0 = dataset_to_schema(ds0)
+    schema_to_zarr(schema=schema_ds0, append_dim=None, target_store=target_store)
+    ds0_zarr = xr.open_dataset(target_store, engine="zarr")
+    assert len(ds0_zarr.time) == len(ds0.time)
+
+    schema_ds1 = dataset_to_schema(ds1)
+    schema_to_zarr(schema=schema_ds1, append_dim="time", target_store=target_store)
+    appended_zarr = xr.open_dataset(target_store, engine="zarr")
+    assert len(appended_zarr.time) == len(ds0.time) + len(ds1.time)

tests/test_end_to_end.py

@@ -83,6 +83,56 @@ def test_xarray_zarr_subpath(
     xr.testing.assert_equal(ds.load(), daily_xarray_dataset)
 
 
+def test_xarray_zarr_append(
+    daily_xarray_datasets_to_append,
+    netcdf_local_file_patterns_to_append,
+    tmp_target,
+):
+    ds0_fixture, ds1_fixture = daily_xarray_datasets_to_append
+    pattern0, pattern1 = netcdf_local_file_patterns_to_append
+    assert pattern0.combine_dim_keys == pattern1.combine_dim_keys
+
+    # these kws are reused across both initial and append pipelines
+    common_kws = dict(
+        target_root=tmp_target,
+        store_name="store",
+        combine_dims=pattern0.combine_dim_keys,
+    )
+    store_path = os.path.join(tmp_target.root_path, "store")
+    # build an initial zarr store, to which we will append
+    options = PipelineOptions(runtime_type_check=False)
+    # we run two pipelines in this test, so instantiate them separately to
+    # avoid any potential of strange co-mingling between the same pipeline
+    with TestPipeline(options=options) as p0:
+        (
+            p0
+            | "CreateInitial" >> beam.Create(pattern0.items())
+            | "OpenInitial" >> OpenWithXarray()
+            | "StoreInitial" >> StoreToZarr(**common_kws)
+        )
+
+    # make sure the initial zarr store looks good
+    initial_actual = xr.open_dataset(store_path, engine="zarr")
+    assert len(initial_actual.time) == 10
+    xr.testing.assert_equal(initial_actual.load(), ds0_fixture)
+
+    # now append to it. the two differences here are
+    # passing `pattern1` in `Create` and `append_dim="time"` in `StoreToZarr`
+    with TestPipeline(options=options) as p1:
+        (
+            p1
+            | "CreateAppend" >> beam.Create(pattern1.items())
+            | "OpenAppend" >> OpenWithXarray()
+            | "StoreAppend" >> StoreToZarr(append_dim="time", **common_kws)
+        )
+
+    # now see if we have appended to time dimension as intended
+    append_actual = xr.open_dataset(store_path, engine="zarr")
+    assert len(append_actual.time) == 20
+    append_expected = xr.concat([ds0_fixture, ds1_fixture], dim="time")
+    xr.testing.assert_equal(append_actual.load(), append_expected)
+
+
 @pytest.mark.parametrize("output_file_name", ["reference.json", "reference.parquet"])
 def test_reference_netcdf(
     daily_xarray_dataset,

tests/test_patterns.py

@@ -205,8 +205,9 @@ def test_setting_file_types(file_type_value):
     "position,start",
     [(0, 0), (1, 2), (2, 4), (3, 7), (4, 9)],
 )
-def test_augment_index_with_start_stop(position, start):
+@pytest.mark.parametrize("append_offset", [0, 5, 500])
+def test_augment_index_with_start_stop(position, start, append_offset):
     dk = Position(position)
-    expected = IndexedPosition(start, dimsize=11)
-    actual = augment_index_with_start_stop(dk, [2, 2, 3, 2, 2])
+    expected = IndexedPosition(start + append_offset, dimsize=11 + append_offset)
+    actual = augment_index_with_start_stop(dk, [2, 2, 3, 2, 2], append_offset)
     assert actual == expected