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Posted to commits@beam.apache.org by da...@apache.org on 2016/11/23 18:42:33 UTC
[1/2] incubator-beam git commit: Add DatastoreIO to Python SDK
Repository: incubator-beam
Updated Branches:
refs/heads/python-sdk 21f9c6d2c -> 9b9d016c8
Add DatastoreIO to Python SDK
Project: http://git-wip-us.apache.org/repos/asf/incubator-beam/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-beam/commit/2b69cce0
Tree: http://git-wip-us.apache.org/repos/asf/incubator-beam/tree/2b69cce0
Diff: http://git-wip-us.apache.org/repos/asf/incubator-beam/diff/2b69cce0
Branch: refs/heads/python-sdk
Commit: 2b69cce0f311a2ef40fdef4fe60d3e6fc13a8868
Parents: 21f9c6d
Author: Vikas Kedigehalli <vi...@google.com>
Authored: Tue Nov 15 16:41:24 2016 -0800
Committer: Davor Bonaci <da...@google.com>
Committed: Wed Nov 23 10:42:00 2016 -0800
----------------------------------------------------------------------
.../apache_beam/examples/datastore_wordcount.py | 118 ++++++++
.../apache_beam/io/datastore/v1/datastoreio.py | 287 +++++++++++++++++++
.../io/datastore/v1/datastoreio_test.py | 172 +++++++++++
.../io/datastore/v1/fake_datastore.py | 75 +++++
.../apache_beam/io/datastore/v1/helper.py | 152 ++++++++++
.../apache_beam/io/datastore/v1/helper_test.py | 125 +++++++-
.../io/datastore/v1/query_splitter_test.py | 62 +---
7 files changed, 930 insertions(+), 61 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/examples/datastore_wordcount.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/examples/datastore_wordcount.py b/sdks/python/apache_beam/examples/datastore_wordcount.py
new file mode 100644
index 0000000..af75b1c
--- /dev/null
+++ b/sdks/python/apache_beam/examples/datastore_wordcount.py
@@ -0,0 +1,118 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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
+#
+# http://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 word-counting workflow that uses Google Cloud Datastore."""
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+import re
+
+import apache_beam as beam
+from apache_beam.io.datastore.v1.datastoreio import ReadFromDatastore
+from apache_beam.utils.options import GoogleCloudOptions
+from apache_beam.utils.options import PipelineOptions
+from apache_beam.utils.options import SetupOptions
+from google.datastore.v1 import query_pb2
+
+
+empty_line_aggregator = beam.Aggregator('emptyLines')
+average_word_size_aggregator = beam.Aggregator('averageWordLength',
+ beam.combiners.MeanCombineFn(),
+ float)
+
+
+class WordExtractingDoFn(beam.DoFn):
+ """Parse each line of input text into words."""
+
+ def process(self, context):
+ """Returns an iterator over the words of this element.
+ The element is a line of text. If the line is blank, note that, too.
+ Args:
+ context: the call-specific context: data and aggregator.
+ Returns:
+ The processed element.
+ """
+ content_value = context.element.properties.get('content', None)
+ text_line = ''
+ if content_value:
+ text_line = content_value.string_value
+
+ if not text_line:
+ context.aggregate_to(empty_line_aggregator, 1)
+ words = re.findall(r'[A-Za-z\']+', text_line)
+ for w in words:
+ context.aggregate_to(average_word_size_aggregator, len(w))
+ return words
+
+
+def run(argv=None):
+ """Main entry point; defines and runs the wordcount pipeline."""
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--kind',
+ dest='kind',
+ required=True,
+ help='Datastore Kind')
+ parser.add_argument('--namespace',
+ dest='namespace',
+ help='Datastore Namespace')
+ parser.add_argument('--output',
+ dest='output',
+ required=True,
+ help='Output file to write results to.')
+ known_args, pipeline_args = parser.parse_known_args(argv)
+ # We use the save_main_session option because one or more DoFn's in this
+ # workflow rely on global context (e.g., a module imported at module level).
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = True
+ gcloud_options = pipeline_options.view_as(GoogleCloudOptions)
+ p = beam.Pipeline(options=pipeline_options)
+
+ query = query_pb2.Query()
+ query.kind.add().name = known_args.kind
+
+ # Read entities from Cloud Datastore into a PCollection.
+ lines = p | 'read from datastore' >> ReadFromDatastore(
+ gcloud_options.project, query, known_args.namespace)
+
+ # Count the occurrences of each word.
+ counts = (lines
+ | 'split' >> (beam.ParDo(WordExtractingDoFn())
+ .with_output_types(unicode))
+ | 'pair_with_one' >> beam.Map(lambda x: (x, 1))
+ | 'group' >> beam.GroupByKey()
+ | 'count' >> beam.Map(lambda (word, ones): (word, sum(ones))))
+
+ # Format the counts into a PCollection of strings.
+ output = counts | 'format' >> beam.Map(lambda (word, c): '%s: %s' % (word, c))
+
+ # Write the output using a "Write" transform that has side effects.
+ # pylint: disable=expression-not-assigned
+ output | 'write' >> beam.io.Write(beam.io.TextFileSink(known_args.output))
+
+ # Actually run the pipeline (all operations above are deferred).
+ result = p.run()
+ empty_line_values = result.aggregated_values(empty_line_aggregator)
+ logging.info('number of empty lines: %d', sum(empty_line_values.values()))
+ word_length_values = result.aggregated_values(average_word_size_aggregator)
+ logging.info('average word lengths: %s', word_length_values.values())
+
+if __name__ == '__main__':
+ logging.getLogger().setLevel(logging.INFO)
+ run()
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/io/datastore/v1/datastoreio.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/io/datastore/v1/datastoreio.py b/sdks/python/apache_beam/io/datastore/v1/datastoreio.py
new file mode 100644
index 0000000..d542439
--- /dev/null
+++ b/sdks/python/apache_beam/io/datastore/v1/datastoreio.py
@@ -0,0 +1,287 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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
+#
+# http://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 connector for reading from and writing to Google Cloud Datastore"""
+
+import logging
+
+from googledatastore import helper as datastore_helper
+
+from apache_beam.io.datastore.v1 import helper
+from apache_beam.io.datastore.v1 import query_splitter
+from apache_beam.transforms import Create
+from apache_beam.transforms import DoFn
+from apache_beam.transforms import FlatMap
+from apache_beam.transforms import GroupByKey
+from apache_beam.transforms import PTransform
+from apache_beam.transforms import ParDo
+from apache_beam.transforms.util import Values
+
+__all__ = ['ReadFromDatastore']
+
+
+class ReadFromDatastore(PTransform):
+ """A ``PTransform`` for reading from Google Cloud Datastore.
+
+ To read a ``PCollection[Entity]`` from a Cloud Datastore ``Query``, use
+ ``ReadFromDatastore`` transform by providing a `project` id and a `query` to
+ read from. You can optionally provide a `namespace` and/or specify how many
+ splits you want for the query through `num_splits` option.
+
+ Note: Normally, a runner will read from Cloud Datastore in parallel across
+ many workers. However, when the `query` is configured with a `limit` or if the
+ query contains inequality filters like `GREATER_THAN, LESS_THAN` etc., then
+ all the returned results will be read by a single worker in order to ensure
+ correct data. Since data is read from a single worker, this could have
+ significant impact on the performance of the job.
+
+ The semantics for the query splitting is defined below:
+ 1. If `num_splits` is equal to 0, then the number of splits will be chosen
+ dynamically at runtime based on the query data size.
+
+ 2. Any value of `num_splits` greater than
+ `ReadFromDatastore._NUM_QUERY_SPLITS_MAX` will be capped at that value.
+
+ 3. If the `query` has a user limit set, or contains inequality filters, then
+ `num_splits` will be ignored and no split will be performed.
+
+ 4. Under certain cases Cloud Datastore is unable to split query to the
+ requested number of splits. In such cases we just use whatever the Cloud
+ Datastore returns.
+
+ See https://developers.google.com/datastore/ for more details on Google Cloud
+ Datastore.
+ """
+
+ # An upper bound on the number of splits for a query.
+ _NUM_QUERY_SPLITS_MAX = 50000
+ # A lower bound on the number of splits for a query. This is to ensure that
+ # we parellelize the query even when Datastore statistics are not available.
+ _NUM_QUERY_SPLITS_MIN = 12
+ # Default bundle size of 64MB.
+ _DEFAULT_BUNDLE_SIZE_BYTES = 64 * 1024 * 1024
+
+ def __init__(self, project, query, namespace=None, num_splits=0):
+ """Initialize the ReadFromDatastore transform.
+
+ Args:
+ project: The Project ID
+ query: Cloud Datastore query to be read from.
+ namespace: An optional namespace.
+ num_splits: Number of splits for the query.
+ """
+ super(ReadFromDatastore, self).__init__()
+
+ if not project:
+ ValueError("Project cannot be empty")
+ if not query:
+ ValueError("Query cannot be empty")
+ if num_splits < 0:
+ ValueError("num_splits must be greater than or equal 0")
+
+ self._project = project
+ # using _namespace conflicts with DisplayData._namespace
+ self._datastore_namespace = namespace
+ self._query = query
+ self._num_splits = num_splits
+
+ def apply(self, pcoll):
+ # This is a composite transform involves the following:
+ # 1. Create a singleton of the user provided `query` and apply a ``ParDo``
+ # that splits the query into `num_splits` and assign each split query a
+ # unique `int` as the key. The resulting output is of the type
+ # ``PCollection[(int, Query)]``.
+ #
+ # If the value of `num_splits` is less than or equal to 0, then the
+ # number of splits will be computed dynamically based on the size of the
+ # data for the `query`.
+ #
+ # 2. The resulting ``PCollection`` is sharded using a ``GroupByKey``
+ # operation. The queries are extracted from the (int, Iterable[Query]) and
+ # flattened to output a ``PCollection[Query]``.
+ #
+ # 3. In the third step, a ``ParDo`` reads entities for each query and
+ # outputs a ``PCollection[Entity]``.
+
+ queries = (pcoll.pipeline
+ | 'User Query' >> Create([self._query])
+ | 'Split Query' >> ParDo(ReadFromDatastore.SplitQueryFn(
+ self._project, self._query, self._datastore_namespace,
+ self._num_splits)))
+
+ sharded_queries = queries | GroupByKey() | Values() | FlatMap('flatten',
+ lambda x: x)
+
+ entities = sharded_queries | 'Read' >> ParDo(
+ ReadFromDatastore.ReadFn(self._project, self._datastore_namespace))
+ return entities
+
+ def display_data(self):
+ disp_data = {'project': self._project,
+ 'query': str(self._query),
+ 'num_splits': self._num_splits}
+
+ if self._datastore_namespace is not None:
+ disp_data['namespace'] = self._datastore_namespace
+
+ return disp_data
+
+ class SplitQueryFn(DoFn):
+ """A `DoFn` that splits a given query into multiple sub-queries."""
+ def __init__(self, project, query, namespace, num_splits):
+ super(ReadFromDatastore.SplitQueryFn, self).__init__()
+ self._datastore = None
+ self._project = project
+ self._datastore_namespace = namespace
+ self._query = query
+ self._num_splits = num_splits
+
+ def start_bundle(self, context):
+ self._datastore = helper.get_datastore(self._project)
+
+ def process(self, p_context, *args, **kwargs):
+ # distinct key to be used to group query splits.
+ key = 1
+ query = p_context.element
+
+ # If query has a user set limit, then the query cannot be split.
+ if query.HasField('limit'):
+ return [(key, query)]
+
+ # Compute the estimated numSplits if not specified by the user.
+ if self._num_splits == 0:
+ estimated_num_splits = ReadFromDatastore.get_estimated_num_splits(
+ self._project, self._datastore_namespace, self._query,
+ self._datastore)
+ else:
+ estimated_num_splits = self._num_splits
+
+ logging.info("Splitting the query into %d splits", estimated_num_splits)
+ try:
+ query_splits = query_splitter.get_splits(
+ self._datastore, query, estimated_num_splits,
+ helper.make_partition(self._project, self._datastore_namespace))
+ except Exception:
+ logging.warning("Unable to parallelize the given query: %s", query,
+ exc_info=True)
+ query_splits = [(key, query)]
+
+ sharded_query_splits = []
+ for split_query in query_splits:
+ sharded_query_splits.append((key, split_query))
+ key += 1
+
+ return sharded_query_splits
+
+ def display_data(self):
+ disp_data = {'project': self._project,
+ 'query': str(self._query),
+ 'num_splits': self._num_splits}
+
+ if self._datastore_namespace is not None:
+ disp_data['namespace'] = self._datastore_namespace
+
+ return disp_data
+
+ class ReadFn(DoFn):
+ """A DoFn that reads entities from Cloud Datastore, for a given query."""
+ def __init__(self, project, namespace=None):
+ super(ReadFromDatastore.ReadFn, self).__init__()
+ self._project = project
+ self._datastore_namespace = namespace
+ self._datastore = None
+
+ def start_bundle(self, context):
+ self._datastore = helper.get_datastore(self._project)
+
+ def process(self, p_context, *args, **kwargs):
+ query = p_context.element
+ # Returns an iterator of entities that reads in batches.
+ entities = helper.fetch_entities(self._project, self._datastore_namespace,
+ query, self._datastore)
+ return entities
+
+ def display_data(self):
+ disp_data = {'project': self._project}
+
+ if self._datastore_namespace is not None:
+ disp_data['namespace'] = self._datastore_namespace
+
+ return disp_data
+
+ @staticmethod
+ def query_latest_statistics_timestamp(project, namespace, datastore):
+ """Fetches the latest timestamp of statistics from Cloud Datastore.
+
+ Cloud Datastore system tables with statistics are periodically updated.
+ This method fethes the latest timestamp (in microseconds) of statistics
+ update using the `__Stat_Total__` table.
+ """
+ query = helper.make_latest_timestamp_query(namespace)
+ req = helper.make_request(project, namespace, query)
+ resp = datastore.run_query(req)
+ if len(resp.batch.entity_results) == 0:
+ raise RuntimeError("Datastore total statistics unavailable.")
+
+ entity = resp.batch.entity_results[0].entity
+ return datastore_helper.micros_from_timestamp(
+ entity.properties['timestamp'].timestamp_value)
+
+ @staticmethod
+ def get_estimated_size_bytes(project, namespace, query, datastore):
+ """Get the estimated size of the data returned by the given query.
+
+ Cloud Datastore provides no way to get a good estimate of how large the
+ result of a query is going to be. Hence we use the __Stat_Kind__ system
+ table to get size of the entire kind as an approximate estimate, assuming
+ exactly 1 kind is specified in the query.
+ See https://cloud.google.com/datastore/docs/concepts/stats.
+ """
+ kind = query.kind[0].name
+ latest_timestamp = ReadFromDatastore.query_latest_statistics_timestamp(
+ project, namespace, datastore)
+ logging.info('Latest stats timestamp for kind %s is %s',
+ kind, latest_timestamp)
+
+ kind_stats_query = (
+ helper.make_kind_stats_query(namespace, kind, latest_timestamp))
+
+ req = helper.make_request(project, namespace, kind_stats_query)
+ resp = datastore.run_query(req)
+ if len(resp.batch.entity_results) == 0:
+ raise RuntimeError("Datastore statistics for kind %s unavailable" % kind)
+
+ entity = resp.batch.entity_results[0].entity
+ return datastore_helper.get_value(entity.properties['entity_bytes'])
+
+ @staticmethod
+ def get_estimated_num_splits(project, namespace, query, datastore):
+ """Computes the number of splits to be performed on the given query."""
+ try:
+ estimated_size_bytes = ReadFromDatastore.get_estimated_size_bytes(
+ project, namespace, query, datastore)
+ logging.info('Estimated size bytes for query: %s', estimated_size_bytes)
+ num_splits = int(min(ReadFromDatastore._NUM_QUERY_SPLITS_MAX, round(
+ (float(estimated_size_bytes) /
+ ReadFromDatastore._DEFAULT_BUNDLE_SIZE_BYTES))))
+
+ except Exception as e:
+ logging.warning('Failed to fetch estimated size bytes: %s', e)
+ # Fallback in case estimated size is unavailable.
+ num_splits = ReadFromDatastore._NUM_QUERY_SPLITS_MIN
+
+ return max(num_splits, ReadFromDatastore._NUM_QUERY_SPLITS_MIN)
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/io/datastore/v1/datastoreio_test.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/io/datastore/v1/datastoreio_test.py b/sdks/python/apache_beam/io/datastore/v1/datastoreio_test.py
new file mode 100644
index 0000000..2bf01f4
--- /dev/null
+++ b/sdks/python/apache_beam/io/datastore/v1/datastoreio_test.py
@@ -0,0 +1,172 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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
+#
+# http://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 unittest
+
+from google.datastore.v1 import datastore_pb2
+from google.datastore.v1 import query_pb2
+from google.protobuf import timestamp_pb2
+from googledatastore import helper as datastore_helper
+from mock import MagicMock, call, patch
+from apache_beam.io.datastore.v1 import helper
+from apache_beam.io.datastore.v1 import query_splitter
+from apache_beam.io.datastore.v1.datastoreio import ReadFromDatastore
+
+
+class DatastoreioTest(unittest.TestCase):
+ _PROJECT = 'project'
+ _KIND = 'kind'
+ _NAMESPACE = 'namespace'
+
+ def setUp(self):
+ self._mock_datastore = MagicMock()
+ self._query = query_pb2.Query()
+ self._query.kind.add().name = self._KIND
+
+ def test_get_estimated_size_bytes_without_namespace(self):
+ entity_bytes = 100
+ timestamp = timestamp_pb2.Timestamp(seconds=1234)
+ self.check_estimated_size_bytes(entity_bytes, timestamp)
+
+ def test_get_estimated_size_bytes_with_namespace(self):
+ entity_bytes = 100
+ timestamp = timestamp_pb2.Timestamp(seconds=1234)
+ self.check_estimated_size_bytes(entity_bytes, timestamp, self._NAMESPACE)
+
+ def test_SplitQueryFn_with_num_splits(self):
+ with patch.object(helper, 'get_datastore',
+ return_value=self._mock_datastore):
+ num_splits = 23
+
+ def fake_get_splits(datastore, query, num_splits, partition=None):
+ return self.split_query(query, num_splits)
+
+ with patch.object(query_splitter, 'get_splits',
+ side_effect=fake_get_splits):
+
+ split_query_fn = ReadFromDatastore.SplitQueryFn(
+ self._PROJECT, self._query, None, num_splits)
+ mock_context = MagicMock()
+ mock_context.element = self._query
+ split_query_fn.start_bundle(mock_context)
+ returned_split_queries = []
+ for split_query in split_query_fn.process(mock_context):
+ returned_split_queries.append(split_query)
+
+ self.assertEqual(len(returned_split_queries), num_splits)
+ self.assertEqual(0, len(self._mock_datastore.run_query.call_args_list))
+ self.verify_unique_keys(returned_split_queries)
+
+ def test_SplitQueryFn_without_num_splits(self):
+ with patch.object(helper, 'get_datastore',
+ return_value=self._mock_datastore):
+ # Force SplitQueryFn to compute the number of query splits
+ num_splits = 0
+ expected_num_splits = 23
+ entity_bytes = (expected_num_splits *
+ ReadFromDatastore._DEFAULT_BUNDLE_SIZE_BYTES)
+ with patch.object(ReadFromDatastore, 'get_estimated_size_bytes',
+ return_value=entity_bytes):
+
+ def fake_get_splits(datastore, query, num_splits, partition=None):
+ return self.split_query(query, num_splits)
+
+ with patch.object(query_splitter, 'get_splits',
+ side_effect=fake_get_splits):
+ split_query_fn = ReadFromDatastore.SplitQueryFn(
+ self._PROJECT, self._query, None, num_splits)
+ mock_context = MagicMock()
+ mock_context.element = self._query
+ split_query_fn.start_bundle(mock_context)
+ returned_split_queries = []
+ for split_query in split_query_fn.process(mock_context):
+ returned_split_queries.append(split_query)
+
+ self.assertEqual(len(returned_split_queries), expected_num_splits)
+ self.assertEqual(0,
+ len(self._mock_datastore.run_query.call_args_list))
+ self.verify_unique_keys(returned_split_queries)
+
+ def test_SplitQueryFn_with_query_limit(self):
+ """A test that verifies no split is performed when the query has a limit."""
+ with patch.object(helper, 'get_datastore',
+ return_value=self._mock_datastore):
+ self._query.limit.value = 3
+ split_query_fn = ReadFromDatastore.SplitQueryFn(
+ self._PROJECT, self._query, None, 4)
+ mock_context = MagicMock()
+ mock_context.element = self._query
+ split_query_fn.start_bundle(mock_context)
+ returned_split_queries = []
+ for split_query in split_query_fn.process(mock_context):
+ returned_split_queries.append(split_query)
+
+ self.assertEqual(1, len(returned_split_queries))
+ self.assertEqual(0, len(self._mock_datastore.method_calls))
+
+ def verify_unique_keys(self, queries):
+ """A helper function that verifies if all the queries have unique keys."""
+ keys, _ = zip(*queries)
+ keys = set(keys)
+ self.assertEqual(len(keys), len(queries))
+
+ def check_estimated_size_bytes(self, entity_bytes, timestamp, namespace=None):
+ """A helper method to test get_estimated_size_bytes"""
+
+ timestamp_req = helper.make_request(
+ self._PROJECT, namespace, helper.make_latest_timestamp_query(namespace))
+ timestamp_resp = self.make_stats_response(
+ {'timestamp': datastore_helper.from_timestamp(timestamp)})
+ kind_stat_req = helper.make_request(
+ self._PROJECT, namespace, helper.make_kind_stats_query(
+ namespace, self._query.kind[0].name,
+ datastore_helper.micros_from_timestamp(timestamp)))
+ kind_stat_resp = self.make_stats_response(
+ {'entity_bytes': entity_bytes})
+
+ def fake_run_query(req):
+ if req == timestamp_req:
+ return timestamp_resp
+ elif req == kind_stat_req:
+ return kind_stat_resp
+ else:
+ print kind_stat_req
+ raise ValueError("Unknown req: %s" % req)
+
+ self._mock_datastore.run_query.side_effect = fake_run_query
+ self.assertEqual(entity_bytes, ReadFromDatastore.get_estimated_size_bytes(
+ self._PROJECT, namespace, self._query, self._mock_datastore))
+ self.assertEqual(self._mock_datastore.run_query.call_args_list,
+ [call(timestamp_req), call(kind_stat_req)])
+
+ def make_stats_response(self, property_map):
+ resp = datastore_pb2.RunQueryResponse()
+ entity_result = resp.batch.entity_results.add()
+ datastore_helper.add_properties(entity_result.entity, property_map)
+ return resp
+
+ def split_query(self, query, num_splits):
+ """Generate dummy query splits."""
+ split_queries = []
+ for _ in range(0, num_splits):
+ q = query_pb2.Query()
+ q.CopyFrom(query)
+ split_queries.append(q)
+ return split_queries
+
+if __name__ == '__main__':
+ unittest.main()
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/io/datastore/v1/fake_datastore.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/io/datastore/v1/fake_datastore.py b/sdks/python/apache_beam/io/datastore/v1/fake_datastore.py
new file mode 100644
index 0000000..631908e
--- /dev/null
+++ b/sdks/python/apache_beam/io/datastore/v1/fake_datastore.py
@@ -0,0 +1,75 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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
+#
+# http://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.
+#
+
+"""Fake datastore used for unit testing."""
+import uuid
+
+from google.datastore.v1 import datastore_pb2
+from google.datastore.v1 import query_pb2
+
+
+def create_run_query(entities, batch_size):
+ """A fake datastore run_query method that returns entities in batches.
+
+ Note: the outer method is needed to make the `entities` and `batch_size`
+ available in the scope of fake_run_query method.
+
+ Args:
+ entities: list of entities supposed to be contained in the datastore.
+ batch_size: the number of entities that run_query method returns in one
+ request.
+ """
+ def run_query(req):
+ start = int(req.query.start_cursor) if req.query.start_cursor else 0
+ # if query limit is less than batch_size, then only return that much.
+ count = min(batch_size, req.query.limit.value)
+ # cannot go more than the number of entities contained in datastore.
+ end = min(len(entities), start + count)
+ finish = False
+ # Finish reading when there are no more entities to return,
+ # or request query limit has been satisfied.
+ if end == len(entities) or count == req.query.limit.value:
+ finish = True
+ return create_response(entities[start:end], str(end), finish)
+ return run_query
+
+
+def create_response(entities, end_cursor, finish):
+ """Creates a query response for a given batch of scatter entities."""
+ resp = datastore_pb2.RunQueryResponse()
+ if finish:
+ resp.batch.more_results = query_pb2.QueryResultBatch.NO_MORE_RESULTS
+ else:
+ resp.batch.more_results = query_pb2.QueryResultBatch.NOT_FINISHED
+
+ resp.batch.end_cursor = end_cursor
+ for entity_result in entities:
+ resp.batch.entity_results.add().CopyFrom(entity_result)
+
+ return resp
+
+
+def create_entities(count):
+ """Creates a list of entities with random keys."""
+ entities = []
+
+ for _ in range(count):
+ entity_result = query_pb2.EntityResult()
+ entity_result.entity.key.path.add().name = str(uuid.uuid4())
+ entities.append(entity_result)
+
+ return entities
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/io/datastore/v1/helper.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/io/datastore/v1/helper.py b/sdks/python/apache_beam/io/datastore/v1/helper.py
index 626ab35..39ca40c 100644
--- a/sdks/python/apache_beam/io/datastore/v1/helper.py
+++ b/sdks/python/apache_beam/io/datastore/v1/helper.py
@@ -16,6 +16,18 @@
#
"""Cloud Datastore helper functions."""
+import sys
+
+from google.datastore.v1 import datastore_pb2
+from google.datastore.v1 import entity_pb2
+from google.datastore.v1 import query_pb2
+from googledatastore import PropertyFilter, CompositeFilter
+from googledatastore import helper as datastore_helper
+from googledatastore.connection import Datastore
+from googledatastore.connection import RPCError
+import googledatastore
+
+from apache_beam.utils import retry
def key_comparator(k1, k2):
@@ -82,3 +94,143 @@ def str_compare(s1, s2):
return -1
else:
return 1
+
+
+def get_datastore(project):
+ """Returns a Cloud Datastore client."""
+ credentials = googledatastore.helper.get_credentials_from_env()
+ datastore = Datastore(project, credentials)
+ return datastore
+
+
+def make_request(project, namespace, query):
+ """Make a Cloud Datastore request for the given query."""
+ req = datastore_pb2.RunQueryRequest()
+ req.partition_id.CopyFrom(make_partition(project, namespace))
+
+ req.query.CopyFrom(query)
+ return req
+
+
+def make_partition(project, namespace):
+ """Make a PartitionId for the given project and namespace."""
+ partition = entity_pb2.PartitionId()
+ partition.project_id = project
+ if namespace is not None:
+ partition.namespace_id = namespace
+
+ return partition
+
+
+def retry_on_rpc_error(exception):
+ """A retry filter for Cloud Datastore RPCErrors."""
+ if isinstance(exception, RPCError):
+ if exception.code >= 500:
+ return True
+ else:
+ return False
+ else:
+ # TODO(vikasrk): Figure out what other errors should be retried.
+ return False
+
+
+def fetch_entities(project, namespace, query, datastore):
+ """A helper method to fetch entities from Cloud Datastore.
+
+ Args:
+ project: Project ID
+ namespace: Cloud Datastore namespace
+ query: Query to be read from
+ datastore: Cloud Datastore Client
+
+ Returns:
+ An iterator of entities.
+ """
+ return QueryIterator(project, namespace, query, datastore)
+
+
+def make_latest_timestamp_query(namespace):
+ """Make a Query to fetch the latest timestamp statistics."""
+ query = query_pb2.Query()
+ if namespace is None:
+ query.kind.add().name = '__Stat_Total__'
+ else:
+ query.kind.add().name = '__Stat_Ns_Total__'
+
+ # Descending order of `timestamp`
+ datastore_helper.add_property_orders(query, "-timestamp")
+ # Only get the latest entity
+ query.limit.value = 1
+ return query
+
+
+def make_kind_stats_query(namespace, kind, latest_timestamp):
+ """Make a Query to fetch the latest kind statistics."""
+ kind_stat_query = query_pb2.Query()
+ if namespace is None:
+ kind_stat_query.kind.add().name = '__Stat_Kind__'
+ else:
+ kind_stat_query.kind.add().name = '__Stat_Ns_Kind__'
+
+ kind_filter = datastore_helper.set_property_filter(
+ query_pb2.Filter(), 'kind_name', PropertyFilter.EQUAL, unicode(kind))
+ timestamp_filter = datastore_helper.set_property_filter(
+ query_pb2.Filter(), 'timestamp', PropertyFilter.EQUAL,
+ latest_timestamp)
+
+ datastore_helper.set_composite_filter(kind_stat_query.filter,
+ CompositeFilter.AND, kind_filter,
+ timestamp_filter)
+ return kind_stat_query
+
+
+class QueryIterator(object):
+ """A iterator class for entities of a given query.
+
+ Entities are read in batches. Retries on failures.
+ """
+ _NOT_FINISHED = query_pb2.QueryResultBatch.NOT_FINISHED
+ # Maximum number of results to request per query.
+ _BATCH_SIZE = 500
+
+ def __init__(self, project, namespace, query, datastore):
+ self._query = query
+ self._datastore = datastore
+ self._project = project
+ self._namespace = namespace
+ self._start_cursor = None
+ self._limit = self._query.limit.value or sys.maxint
+ self._req = make_request(project, namespace, query)
+
+ @retry.with_exponential_backoff(num_retries=5,
+ retry_filter=retry_on_rpc_error)
+ def _next_batch(self):
+ """Fetches the next batch of entities."""
+ if self._start_cursor is not None:
+ self._req.query.start_cursor = self._start_cursor
+
+ # set batch size
+ self._req.query.limit.value = min(self._BATCH_SIZE, self._limit)
+ resp = self._datastore.run_query(self._req)
+ return resp
+
+ def __iter__(self):
+ more_results = True
+ while more_results:
+ resp = self._next_batch()
+ for entity_result in resp.batch.entity_results:
+ yield entity_result.entity
+
+ self._start_cursor = resp.batch.end_cursor
+ num_results = len(resp.batch.entity_results)
+ self._limit -= num_results
+
+ # Check if we need to read more entities.
+ # True when query limit hasn't been satisfied and there are more entities
+ # to be read. The latter is true if the response has a status
+ # `NOT_FINISHED` or if the number of results read in the previous batch
+ # is equal to `_BATCH_SIZE` (all indications that there is more data be
+ # read).
+ more_results = ((self._limit > 0) and
+ ((num_results == self._BATCH_SIZE) or
+ (resp.batch.more_results == self._NOT_FINISHED)))
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/io/datastore/v1/helper_test.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/io/datastore/v1/helper_test.py b/sdks/python/apache_beam/io/datastore/v1/helper_test.py
index 50f8e4c..69741d2 100644
--- a/sdks/python/apache_beam/io/datastore/v1/helper_test.py
+++ b/sdks/python/apache_beam/io/datastore/v1/helper_test.py
@@ -16,14 +16,134 @@
#
"""Tests for datastore helper."""
-
+import imp
+import sys
import unittest
-from apache_beam.io.datastore.v1 import helper
+
+from google.datastore.v1 import datastore_pb2
+from google.datastore.v1 import query_pb2
from google.datastore.v1.entity_pb2 import Key
+from googledatastore.connection import RPCError
+from mock import MagicMock, Mock, patch
+
+from apache_beam.io.datastore.v1 import fake_datastore
+from apache_beam.io.datastore.v1 import helper
+from apache_beam.utils import retry
class HelperTest(unittest.TestCase):
+ def setUp(self):
+ self._mock_datastore = MagicMock()
+ self._query = query_pb2.Query()
+ self._query.kind.add().name = 'dummy_kind'
+ self.patch_retry()
+
+ def patch_retry(self):
+
+ """A function to patch retry module to use mock clock and logger."""
+ real_retry_with_exponential_backoff = retry.with_exponential_backoff
+
+ def patched_retry_with_exponential_backoff(num_retries, retry_filter):
+ """A patch for retry decorator to use a mock dummy clock and logger."""
+ return real_retry_with_exponential_backoff(
+ num_retries=num_retries, retry_filter=retry_filter, logger=Mock(),
+ clock=Mock())
+
+ patch.object(retry, 'with_exponential_backoff',
+ side_effect=patched_retry_with_exponential_backoff).start()
+
+ # Reload module after patching.
+ imp.reload(helper)
+
+ def kill_patches():
+ patch.stopall()
+ # Reload module again after removing patch.
+ imp.reload(helper)
+
+ self.addCleanup(kill_patches)
+
+ def permanent_datastore_failure(self, req):
+ raise RPCError("dummy", 500, "failed")
+
+ def transient_datastore_failure(self, req):
+ if self._transient_fail_count:
+ self._transient_fail_count -= 1
+ raise RPCError("dummy", 500, "failed")
+ else:
+ return datastore_pb2.RunQueryResponse()
+
+ def test_query_iterator(self):
+ self._mock_datastore.run_query.side_effect = (
+ self.permanent_datastore_failure)
+ query_iterator = helper.QueryIterator("project", None, self._query,
+ self._mock_datastore)
+ self.assertRaises(RPCError, iter(query_iterator).next)
+ self.assertEqual(6, len(self._mock_datastore.run_query.call_args_list))
+
+ def test_query_iterator_with_transient_failures(self):
+ self._mock_datastore.run_query.side_effect = (
+ self.transient_datastore_failure)
+ query_iterator = helper.QueryIterator("project", None, self._query,
+ self._mock_datastore)
+ fail_count = 2
+ self._transient_fail_count = fail_count
+ for _ in query_iterator:
+ pass
+
+ self.assertEqual(fail_count + 1,
+ len(self._mock_datastore.run_query.call_args_list))
+
+ def test_query_iterator_with_single_batch(self):
+ num_entities = 100
+ batch_size = 500
+ self.check_query_iterator(num_entities, batch_size, self._query)
+
+ def test_query_iterator_with_multiple_batches(self):
+ num_entities = 1098
+ batch_size = 500
+ self.check_query_iterator(num_entities, batch_size, self._query)
+
+ def test_query_iterator_with_exact_batch_multiple(self):
+ num_entities = 1000
+ batch_size = 500
+ self.check_query_iterator(num_entities, batch_size, self._query)
+
+ def test_query_iterator_with_query_limit(self):
+ num_entities = 1098
+ batch_size = 500
+ self._query.limit.value = 1004
+ self.check_query_iterator(num_entities, batch_size, self._query)
+
+ def test_query_iterator_with_large_query_limit(self):
+ num_entities = 1098
+ batch_size = 500
+ self._query.limit.value = 10000
+ self.check_query_iterator(num_entities, batch_size, self._query)
+
+ def check_query_iterator(self, num_entities, batch_size, query):
+ """A helper method to test the QueryIterator.
+
+ Args:
+ num_entities: number of entities contained in the fake datastore.
+ batch_size: the number of entities returned by fake datastore in one req.
+ query: the query to be executed
+
+ """
+ entities = fake_datastore.create_entities(num_entities)
+ self._mock_datastore.run_query.side_effect = \
+ fake_datastore.create_run_query(entities, batch_size)
+ query_iterator = helper.QueryIterator("project", None, self._query,
+ self._mock_datastore)
+
+ i = 0
+ for entity in query_iterator:
+ self.assertEqual(entity, entities[i].entity)
+ i += 1
+
+ limit = query.limit.value if query.HasField('limit') else sys.maxint
+ self.assertEqual(i, min(num_entities, limit))
+
def test_compare_path_with_different_kind(self):
p1 = Key.PathElement()
p1.kind = 'dummy1'
@@ -120,5 +240,6 @@ class HelperTest(unittest.TestCase):
p21.kind = 'dummy1'
self.assertLess(helper.key_comparator(k1, k2), 0)
+
if __name__ == '__main__':
unittest.main()
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2b69cce0/sdks/python/apache_beam/io/datastore/v1/query_splitter_test.py
----------------------------------------------------------------------
diff --git a/sdks/python/apache_beam/io/datastore/v1/query_splitter_test.py b/sdks/python/apache_beam/io/datastore/v1/query_splitter_test.py
index 979a69f..810719b 100644
--- a/sdks/python/apache_beam/io/datastore/v1/query_splitter_test.py
+++ b/sdks/python/apache_beam/io/datastore/v1/query_splitter_test.py
@@ -18,11 +18,11 @@
"""Cloud Datastore query splitter test."""
import unittest
-import uuid
from mock import MagicMock
from mock import call
+from apache_beam.io.datastore.v1 import fake_datastore
from apache_beam.io.datastore.v1 import query_splitter
from google.datastore.v1 import datastore_pb2
@@ -150,11 +150,11 @@ class QuerySplitterTest(unittest.TestCase):
batch_size: the number of entities returned by fake datastore in one req.
"""
- entities = QuerySplitterTest.create_entities(num_entities)
+ entities = fake_datastore.create_entities(num_entities)
mock_datastore = MagicMock()
# Assign a fake run_query method as a side_effect to the mock.
mock_datastore.run_query.side_effect = \
- QuerySplitterTest.create_run_query(entities, batch_size)
+ fake_datastore.create_run_query(entities, batch_size)
split_queries = query_splitter.get_splits(mock_datastore, query, num_splits)
@@ -173,33 +173,6 @@ class QuerySplitterTest(unittest.TestCase):
self.assertEqual(expected_calls, mock_datastore.run_query.call_args_list)
@staticmethod
- def create_run_query(entities, batch_size):
- """A fake datastore run_query method that returns entities in batches.
-
- Note: the outer method is needed to make the `entities` and `batch_size`
- available in the scope of fake_run_query method.
-
- Args:
- entities: list of entities supposed to be contained in the datastore.
- batch_size: the number of entities that run_query method returns in one
- request.
- """
- def fake_run_query(req):
- start = int(req.query.start_cursor) if req.query.start_cursor else 0
- # if query limit is less than batch_size, then only return that much.
- count = min(batch_size, req.query.limit.value)
- # cannot go more than the number of entities contained in datastore.
- end = min(len(entities), start + count)
- finish = False
- # Finish reading when there are no more entities to return,
- # or request query limit has been satisfied.
- if end == len(entities) or count == req.query.limit.value:
- finish = True
- return QuerySplitterTest.create_scatter_response(entities[start:end],
- str(end), finish)
- return fake_run_query
-
- @staticmethod
def create_scatter_requests(query, num_splits, batch_size, num_entities):
"""Creates a list of expected scatter requests from the query splitter.
@@ -223,35 +196,6 @@ class QuerySplitterTest(unittest.TestCase):
return requests
- @staticmethod
- def create_scatter_response(entities, end_cursor, finish):
- """Creates a query response for a given batch of scatter entities."""
-
- resp = datastore_pb2.RunQueryResponse()
- if finish:
- resp.batch.more_results = query_pb2.QueryResultBatch.NO_MORE_RESULTS
- else:
- resp.batch.more_results = query_pb2.QueryResultBatch.NOT_FINISHED
-
- resp.batch.end_cursor = end_cursor
- for entity_result in entities:
- resp.batch.entity_results.add().CopyFrom(entity_result)
-
- return resp
-
- @staticmethod
- def create_entities(count):
- """Creates a list of entities with random keys."""
-
- entities = []
-
- for _ in range(0, count):
- entity_result = query_pb2.EntityResult()
- entity_result.entity.key.path.add().name = str(uuid.uuid4())
- entities.append(entity_result)
-
- return entities
-
if __name__ == '__main__':
unittest.main()
[2/2] incubator-beam git commit: This closes #1398
Posted by da...@apache.org.
This closes #1398
Project: http://git-wip-us.apache.org/repos/asf/incubator-beam/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-beam/commit/9b9d016c
Tree: http://git-wip-us.apache.org/repos/asf/incubator-beam/tree/9b9d016c
Diff: http://git-wip-us.apache.org/repos/asf/incubator-beam/diff/9b9d016c
Branch: refs/heads/python-sdk
Commit: 9b9d016c80b9a7e73a7485d3e579ead3ada18ac6
Parents: 21f9c6d 2b69cce
Author: Davor Bonaci <da...@google.com>
Authored: Wed Nov 23 10:42:17 2016 -0800
Committer: Davor Bonaci <da...@google.com>
Committed: Wed Nov 23 10:42:17 2016 -0800
----------------------------------------------------------------------
.../apache_beam/examples/datastore_wordcount.py | 118 ++++++++
.../apache_beam/io/datastore/v1/datastoreio.py | 287 +++++++++++++++++++
.../io/datastore/v1/datastoreio_test.py | 172 +++++++++++
.../io/datastore/v1/fake_datastore.py | 75 +++++
.../apache_beam/io/datastore/v1/helper.py | 152 ++++++++++
.../apache_beam/io/datastore/v1/helper_test.py | 125 +++++++-
.../io/datastore/v1/query_splitter_test.py | 62 +---
7 files changed, 930 insertions(+), 61 deletions(-)
----------------------------------------------------------------------