Support incremental appending

pangeo_forge/recipe.py

@@ -204,7 +204,7 @@ def _prepare_target():
                         chunk_key
                     ).chunk()  # make sure data are not in memory
                     init_dsets.append(chunk_ds)
-                # TODO: create csutomizable option for this step
+                # TODO: create customizable option for this step
                 # How to combine attrs is particularly important. It seems like
                 # xarray is missing a "minimal" option to only keep the attrs
                 # that are the same among all input variables.
@@ -234,36 +234,47 @@ def _prepare_target():
     @property
     def cache_input(self) -> Callable:
         def cache_func(input_key: Hashable) -> None:
-            logger.info(f"Caching input {input_key}")
-            fname = self._inputs[input_key]
-            # TODO: add a check for whether the input is already cached?
-            with input_opener(fname, mode="rb", **self.fsspec_open_kwargs) as source:
-                with self.input_cache.open(fname, mode="wb") as target:
-                    # TODO: make this configurable? Would we ever want to change it?
-                    BLOCK_SIZE = 10_000_000  # 10 MB
-                    while True:
-                        data = source.read(BLOCK_SIZE)
-                        if not data:
-                            break
-                        target.write(data)
+            if self._inputs[input_key]["processed"]:
+                logger.info(f"Dry-run: caching input {input_key}")
+            else:
+                logger.info(f"Caching input {input_key}")
+                fname = self._inputs[input_key]["url"]
+                # TODO: add a check for whether the input is already cached?
+                with input_opener(fname, mode="rb", **self.fsspec_open_kwargs) as source:
+                    with self.input_cache.open(fname, mode="wb") as target:
+                        # TODO: make this configurable? Would we ever want to change it?
+                        BLOCK_SIZE = 10_000_000  # 10 MB
+                        while True:
+                            data = source.read(BLOCK_SIZE)
+                            if not data:
+                                break
+                            target.write(data)
 
         return cache_func
 
     @property
     def store_chunk(self) -> Callable:
         def _store_chunk(chunk_key):
-            ds_chunk = self.open_chunk(chunk_key)
+            write_region = self.region_for_chunk(chunk_key)
+            if all(
+                [
+                    self._inputs[input_key]["processed"]
+                    for input_key in self.inputs_for_chunk(chunk_key)
+                ]
+            ):
+                logger.info(f"Dry-run: storing chunk '{chunk_key}' to Zarr region {write_region}")
+            else:
+                ds_chunk = self.open_chunk(chunk_key)
 
-            def drop_vars(ds):
-                # writing a region means that all the variables MUST have sequence_dim
-                to_drop = [v for v in ds.variables if self.sequence_dim not in ds[v].dims]
-                return ds.drop_vars(to_drop)
+                def drop_vars(ds):
+                    # writing a region means that all the variables MUST have sequence_dim
+                    to_drop = [v for v in ds.variables if self.sequence_dim not in ds[v].dims]
+                    return ds.drop_vars(to_drop)
 
-            ds_chunk = drop_vars(ds_chunk)
-            target_mapper = self.target.get_mapper()
-            write_region = self.region_for_chunk(chunk_key)
-            logger.info(f"Storing chunk '{chunk_key}' to Zarr region {write_region}")
-            ds_chunk.to_zarr(target_mapper, region=write_region)
+                ds_chunk = drop_vars(ds_chunk)
+                target_mapper = self.target.get_mapper()
+                logger.info(f"Storing chunk '{chunk_key}' to Zarr region {write_region}")
+                ds_chunk.to_zarr(target_mapper, region=write_region)
 
         return _store_chunk
 
@@ -297,7 +308,7 @@ def input_opener(self, fname: str):
                     yield f
 
     def open_input(self, input_key: Hashable):
-        fname = self._inputs[input_key]
+        fname = self._inputs[input_key]["url"]
         with self.input_opener(fname) as f:
             logger.info(f"Opening input with Xarray {input_key}: '{fname}'")
             ds = xr.open_dataset(f, **self.xarray_open_kwargs)
@@ -357,7 +368,7 @@ def expand_target_dim(self, dim, dimsize):
         # now explicity write the sequence coordinate to avoid missing data
         # when reopening
         if dim in zgroup:
-            zgroup[dim][:] = 0
+            zgroup[dim][self.nitems_per_input * self.processed_input_nb :] = 0  # noqa: E203
 
     def inputs_for_chunk(self, chunk_key):
         return self._chunks_inputs[chunk_key]
@@ -392,15 +403,20 @@ class NetCDFtoZarrSequentialRecipe(NetCDFtoZarrRecipe):
     """There is only one sequence of input files. Each file can contain
     many variables.
 
+    :param processed_input_urls: The inputs already used to generate the existing dataset.
     :param input_urls: The inputs used to generate the dataset.
     """
 
+    processed_input_urls: Iterable[str] = field(repr=False, default_factory=list)
     input_urls: Iterable[str] = field(repr=False, default_factory=list)
 
     def __post_init__(self):
         super().__post_init__()
-        input_pattern = ExplicitURLSequence(self.input_urls)
-        self._inputs = {k: v for k, v in input_pattern}
+        self.processed_input_nb = len(self.processed_input_urls)
+        input_pattern = ExplicitURLSequence(self.processed_input_urls + self.input_urls)
+        self._inputs = {
+            k: {"url": v, "processed": k < self.processed_input_nb} for k, v in input_pattern
+        }
         self._chunks_inputs = {
             k: v for k, v in enumerate(chunked_iterable(self._inputs, self.inputs_per_chunk))
         }
@@ -428,7 +444,8 @@ class NetCDFtoZarrMultiVarSequentialRecipe(NetCDFtoZarrRecipe):
     def __post_init__(self):
         super().__post_init__()
         self._variables = self.input_pattern.keys["variable"]
-        self._inputs = {k: v for k, v in self.input_pattern}
+        self.processed_input_nb = 0  # TODO
+        self._inputs = {k: {"url": v, "processed": False} for k, v in self.input_pattern}
         # input keys are tuples like
         #  ("temp", 0)
         #  ("temp", 1)

tests/test_recipe.py

@@ -27,6 +27,41 @@ def _manually_execute_recipe(r):
     r.finalize_target()
 
 
+def test_NetCDFtoZarrSequentialRecipeIncremental(
+    daily_xarray_dataset, netcdf_local_paths, tmp_target, tmp_cache
+):
+
+    paths, items_per_file = netcdf_local_paths
+    n = len(paths) // 2
+
+    paths1 = paths[:n]
+    r = recipe.NetCDFtoZarrSequentialRecipe(
+        input_urls=paths1,
+        sequence_dim="time",
+        inputs_per_chunk=1,
+        nitems_per_input=items_per_file,
+        target=tmp_target,
+        input_cache=tmp_cache,
+    )
+    _manually_execute_recipe(r)
+
+    paths2 = paths[n:]
+    r = recipe.NetCDFtoZarrSequentialRecipe(
+        processed_input_urls=paths1,
+        input_urls=paths2,
+        sequence_dim="time",
+        inputs_per_chunk=1,
+        nitems_per_input=items_per_file,
+        target=tmp_target,
+        input_cache=tmp_cache,
+    )
+    _manually_execute_recipe(r)
+
+    ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True).load()
+    ds_expected = daily_xarray_dataset.compute()
+    assert ds_target.identical(ds_expected)
+
+
 @pytest.mark.parametrize(
     "username, password", [("foo", "bar"), ("foo", "wrong"),],  # noqa: E231
 )
@@ -164,6 +199,4 @@ def test_NetCDFtoZarrMultiVarSequentialRecipe(
     _manually_execute_recipe(r)
 
     ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True).compute()
-    print(ds_target)
-    print(daily_xarray_dataset)
     assert ds_target.identical(daily_xarray_dataset)