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Prototype version of xarray_beam.Dataset
xbeam.Dataset implements a high level API for distributed dataset operations at the Dataset rather than beam.PCollection level. PiperOrigin-RevId: 560407095
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# Copyright 2023 Google LLC | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# https://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
"""A high-level interface for Xarray-Beam datasets. | ||
Usage example (not fully implemented yet!): | ||
import xarray_beam as xbeam | ||
transform = ( | ||
xbeam.Dataset.from_zarr(input_path) | ||
.rechunk({'time': -1, 'latitude': 10, 'longitude': 10}) | ||
.map_blocks(lambda x: x.median('time')) | ||
.to_zarr(output_path) | ||
) | ||
with beam.Pipeline() as p: | ||
p | transform | ||
""" | ||
from __future__ import annotations | ||
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import collections | ||
from collections import abc | ||
import dataclasses | ||
import itertools | ||
import os.path | ||
import tempfile | ||
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import apache_beam as beam | ||
import xarray | ||
from xarray_beam._src import core | ||
from xarray_beam._src import zarr | ||
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class _CountNamer: | ||
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def __init__(self): | ||
self._counts = collections.defaultdict(itertools.count) | ||
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def apply(self, name: str) -> str: | ||
return f'{name}_{next(self._counts[name])}' | ||
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_get_label = _CountNamer().apply | ||
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@dataclasses.dataclass | ||
class Dataset: | ||
"""Experimental high-level representation of an Xarray-Beam dataset.""" | ||
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template: xarray.Dataset | ||
chunks: dict[str, int] | ||
split_vars: bool | ||
ptransform: beam.PTransform | ||
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@classmethod | ||
def from_xarray( | ||
cls, | ||
source: xarray.Dataset, | ||
chunks: abc.Mapping[str, int], | ||
split_vars: bool = False, | ||
) -> Dataset: | ||
"""Create an xarray_beam.Dataset from an xarray.Dataset.""" | ||
template = zarr.make_template(source) | ||
ptransform = _get_label('from_xarray') >> core.DatasetToChunks( | ||
source, chunks, split_vars | ||
) | ||
return cls(template, dict(chunks), split_vars, ptransform) | ||
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@classmethod | ||
def from_zarr(cls, path: str, split_vars: bool = False) -> Dataset: | ||
"""Create an xarray_beam.Dataset from a zarr file.""" | ||
source, chunks = zarr.open_zarr(path) | ||
result = cls.from_xarray(source, chunks, split_vars) | ||
result.ptransform = _get_label('from_zarr') >> result.ptransform | ||
return result | ||
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def to_zarr(self, path: str) -> beam.PTransform: | ||
"""Write to a Zarr file.""" | ||
return self.ptransform | _get_label('to_zarr') >> zarr.ChunksToZarr( | ||
path, self.template, self.chunks | ||
) | ||
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def collect_with_direct_runner(self) -> xarray.Dataset: | ||
"""Collect a dataset in memory by writing it to a temp file.""" | ||
# TODO(shoyer): generalize this function to something that support | ||
# alternative runners can we figure out a suitable temp file location for | ||
# distributed runners? | ||
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with tempfile.TemporaryDirectory() as temp_dir: | ||
temp_path = os.path.join(temp_dir, 'tmp.zarr') | ||
with beam.Pipeline(runner='DirectRunner') as pipeline: | ||
pipeline |= self.to_zarr(temp_path) | ||
return xarray.open_zarr(temp_path).compute() | ||
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# TODO(shoyer): implement map_blocks, rechunking, merge, rename, mean, etc | ||
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@property | ||
def sizes(self) -> dict[str, int]: | ||
"""Size of each dimension on this dataset.""" | ||
return dict(self.template.sizes) | ||
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def pipe(self, func, *args, **kwargs): | ||
return func(*args, **kwargs) | ||
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def __repr__(self): | ||
base = repr(self.template) | ||
chunks_str = ', '.join(f'{k}: {v}' for k, v in self.chunks.items()) | ||
return base.replace( | ||
'<xarray.Dataset>', | ||
f'<xarray_beam.Dataset[{chunks_str}][split_vars={self.split_vars}]>', | ||
) |
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# Copyright 2023 Google LLC | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# https://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
import textwrap | ||
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from absl.testing import absltest | ||
from absl.testing import parameterized | ||
import apache_beam as beam | ||
import numpy as np | ||
import xarray | ||
import xarray_beam as xbeam | ||
from xarray_beam._src import test_util | ||
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class DatasetTest(test_util.TestCase): | ||
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def test_from_xarray(self): | ||
ds = xarray.Dataset({'foo': ('x', np.arange(10))}) | ||
beam_ds = xbeam.Dataset.from_xarray(ds, {'x': 5}) | ||
self.assertIsInstance(beam_ds, xbeam.Dataset) | ||
self.assertEqual(beam_ds.sizes, {'x': 10}) | ||
self.assertEqual(beam_ds.template.keys(), {'foo'}) | ||
self.assertEqual(beam_ds.chunks, {'x': 5}) | ||
self.assertFalse(beam_ds.split_vars) | ||
self.assertRegex(beam_ds.ptransform.label, r'^from_xarray_\d+$') | ||
self.assertEqual( | ||
repr(beam_ds), | ||
textwrap.dedent(""" | ||
<xarray_beam.Dataset[x: 5][split_vars=False]> | ||
Dimensions: (x: 10) | ||
Dimensions without coordinates: x | ||
Data variables: | ||
foo (x) int64 dask.array<chunksize=(10,), meta=np.ndarray> | ||
""").strip(), | ||
) | ||
expected = [ | ||
(xbeam.Key({'x': 0}), ds.head(x=5)), | ||
(xbeam.Key({'x': 5}), ds.tail(x=5)), | ||
] | ||
actual = test_util.EagerPipeline() | beam_ds.ptransform | ||
self.assertIdenticalChunks(expected, actual) | ||
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def test_collect_with_direct_runner(self): | ||
ds = xarray.Dataset({'foo': ('x', np.arange(10))}) | ||
beam_ds = xbeam.Dataset.from_xarray(ds, {'x': 5}) | ||
collected = beam_ds.collect_with_direct_runner() | ||
xarray.testing.assert_identical(ds, collected) | ||
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@parameterized.parameters( | ||
dict(split_vars=False), | ||
dict(split_vars=True), | ||
) | ||
def test_from_zarr(self, split_vars): | ||
temp_dir = self.create_tempdir().full_path | ||
ds = xarray.Dataset({'foo': ('x', np.arange(10))}) | ||
ds.chunk({'x': 5}).to_zarr(temp_dir) | ||
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beam_ds = xbeam.Dataset.from_zarr(temp_dir, split_vars) | ||
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self.assertRegex(beam_ds.ptransform.label, r'^from_zarr_\d+$') | ||
self.assertEqual(beam_ds.chunks, {'x': 5}) | ||
self.assertEqual(beam_ds.split_vars, split_vars) | ||
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collected = beam_ds.collect_with_direct_runner() | ||
xarray.testing.assert_identical(ds, collected) | ||
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def test_to_zarr(self): | ||
temp_dir = self.create_tempdir().full_path | ||
ds = xarray.Dataset({'foo': ('x', np.arange(10))}) | ||
beam_ds = xbeam.Dataset.from_xarray(ds, {'x': 5}) | ||
to_zarr = beam_ds.to_zarr(temp_dir) | ||
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self.assertRegex(to_zarr.label, r'^from_xarray_\d+|to_zarr_\d+$') | ||
with beam.Pipeline() as p: | ||
p |= to_zarr | ||
opened = xarray.open_zarr(temp_dir).compute() | ||
xarray.testing.assert_identical(ds, opened) | ||
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if __name__ == '__main__': | ||
absltest.main() |