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 <[email protected]> Authored: Tue Nov 15 16:41:24 2016 -0800 Committer: Davor Bonaci <[email protected]> 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()
