Source code for

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A connector for reading from and writing to Google Cloud Datastore.

This module uses the newer google-cloud-datastore client package. Its API was
different enough to require extensive changes to this and associated modules.
# pytype: skip-file

from __future__ import absolute_import
from __future__ import division

import logging
import time
from builtins import round

from apache_beam import typehints
from import helper
from import query_splitter
from import types
from import util
from import AdaptiveThrottler
from apache_beam.metrics.metric import Metrics
from apache_beam.transforms import Create
from apache_beam.transforms import DoFn
from apache_beam.transforms import ParDo
from apache_beam.transforms import PTransform
from apache_beam.transforms import Reshuffle
from apache_beam.utils import retry

__all__ = ['ReadFromDatastore', 'WriteToDatastore', 'DeleteFromDatastore']

_LOGGER = logging.getLogger(__name__)

[docs]@typehints.with_output_types(types.Entity) class ReadFromDatastore(PTransform): """A ``PTransform`` for querying Google Cloud Datastore. To read a ``PCollection[Entity]`` from a Cloud Datastore ``Query``, use the ``ReadFromDatastore`` transform by providing a `query` to read from. The project and optional namespace are set in the query. The query will be split into multiple queries to allow for parallelism. The degree of parallelism is automatically determined, but can be overridden by setting `num_splits` to a value of 1 or greater. 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. Using a :class:`~apache_beam.transforms.util.Reshuffle` transform after the read in this case might be beneficial for parallelizing work across workers. The semantics for 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 Cloud Datastore returns. See 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 parallelize 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, query, num_splits=0): """Initialize the `ReadFromDatastore` transform. This transform outputs elements of type :class:``. Args: query: (:class:``) query used to fetch entities. num_splits: (:class:`int`) (optional) Number of splits for the query. """ super(ReadFromDatastore, self).__init__() if not query.project: raise ValueError("query.project cannot be empty") if not query: raise ValueError("query cannot be empty") if num_splits < 0: raise ValueError("num_splits must be greater than or equal 0") self._project = query.project # using _namespace conflicts with DisplayData._namespace self._datastore_namespace = query.namespace self._query = query self._num_splits = num_splits
[docs] def expand(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` queries if possible. # # If the value of `num_splits` is 0, the number of splits will be # computed dynamically based on the size of the data for the `query`. # # 2. The resulting ``PCollection`` is sharded across workers using a # ``Reshuffle`` operation. # # 3. In the third step, a ``ParDo`` reads entities for each query and # outputs a ``PCollection[Entity]``. return ( pcoll.pipeline | 'UserQuery' >> Create([self._query]) | 'SplitQuery' >> ParDo( ReadFromDatastore._SplitQueryFn(self._num_splits)) | Reshuffle() | 'Read' >> ParDo(ReadFromDatastore._QueryFn()))
[docs] def display_data(self): disp_data = { 'project': self._query.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
@typehints.with_input_types(types.Query) @typehints.with_output_types(types.Query) class _SplitQueryFn(DoFn): """A `DoFn` that splits a given query into multiple sub-queries.""" def __init__(self, num_splits): super(ReadFromDatastore._SplitQueryFn, self).__init__() self._num_splits = num_splits def process(self, query, *args, **kwargs): client = helper.get_client(query.project, query.namespace) try: # Short circuit estimating num_splits if split is not possible. query_splitter.validate_split(query) if self._num_splits == 0: estimated_num_splits = self.get_estimated_num_splits(client, query) else: estimated_num_splits = self._num_splits"Splitting the query into %d splits", estimated_num_splits) query_splits = query_splitter.get_splits( client, query, estimated_num_splits) except query_splitter.QuerySplitterError: "Unable to parallelize the given query: %s", query, exc_info=True) query_splits = [query] return query_splits def display_data(self): disp_data = {'num_splits': self._num_splits} return disp_data @staticmethod def query_latest_statistics_timestamp(client): """Fetches the latest timestamp of statistics from Cloud Datastore. Cloud Datastore system tables with statistics are periodically updated. This method fetches the latest timestamp (in microseconds) of statistics update using the `__Stat_Total__` table. """ if client.namespace is None: kind = '__Stat_Total__' else: kind = '__Stat_Ns_Total__' query = client.query( kind=kind, order=[ "-timestamp", ]) entities = list(query.fetch(limit=1)) if not entities: raise RuntimeError("Datastore total statistics unavailable.") return entities[0]['timestamp'] @staticmethod def get_estimated_size_bytes(client, query): """Get the estimated size of the data returned by this instance's 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 """ kind_name = query.kind latest_timestamp = ( ReadFromDatastore._SplitQueryFn.query_latest_statistics_timestamp( client)) 'Latest stats timestamp for kind %s is %s', kind_name, latest_timestamp) if client.namespace is None: kind = '__Stat_Kind__' else: kind = '__Stat_Ns_Kind__' query = client.query(kind=kind) query.add_filter('kind_name', '=', kind_name) query.add_filter('timestamp', '=', latest_timestamp) entities = list(query.fetch(limit=1)) if not entities: raise RuntimeError( 'Datastore statistics for kind %s unavailable' % kind_name) return entities[0]['entity_bytes'] @staticmethod def get_estimated_num_splits(client, query): """Computes the number of splits to be performed on the query.""" try: estimated_size_bytes = ( ReadFromDatastore._SplitQueryFn.get_estimated_size_bytes( client, query))'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: _LOGGER.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) @typehints.with_input_types(types.Query) @typehints.with_output_types(types.Entity) class _QueryFn(DoFn): """A DoFn that fetches entities from Cloud Datastore, for a given query.""" def process(self, query, *unused_args, **unused_kwargs): _client = helper.get_client(query.project, query.namespace) client_query = query._to_client_query(_client) for client_entity in client_query.fetch(query.limit): yield types.Entity.from_client_entity(client_entity)
class _Mutate(PTransform): """A ``PTransform`` that writes mutations to Cloud Datastore. Only idempotent Datastore mutation operations (upsert and delete) are supported, as the commits are retried when failures occur. """ def __init__(self, mutate_fn): """Initializes a Mutate transform. Args: mutate_fn: Instance of `DatastoreMutateFn` to use. """ self._mutate_fn = mutate_fn def expand(self, pcoll): return pcoll | 'Write Batch to Datastore' >> ParDo(self._mutate_fn) class DatastoreMutateFn(DoFn): """A ``DoFn`` that write mutations to Datastore. Mutations are written in batches, where the maximum batch size is `util.WRITE_BATCH_SIZE`. Commits are non-transactional. If a commit fails because of a conflict over an entity group, the commit will be retried. This means that the mutation should be idempotent (`upsert` and `delete` mutations) to prevent duplicate data or errors. """ def __init__(self, project): """ Args: project: (str) cloud project id """ self._project = project self._client = None self._rpc_successes = Metrics.counter( _Mutate.DatastoreMutateFn, "datastoreRpcSuccesses") self._rpc_errors = Metrics.counter( _Mutate.DatastoreMutateFn, "datastoreRpcErrors") self._throttled_secs = Metrics.counter( _Mutate.DatastoreMutateFn, "cumulativeThrottlingSeconds") self._throttler = AdaptiveThrottler( window_ms=120000, bucket_ms=1000, overload_ratio=1.25) def _update_rpc_stats(self, successes=0, errors=0, throttled_secs=0): def start_bundle(self): self._client = helper.get_client(self._project, namespace=None) self._init_batch() self._batch_sizer = util.DynamicBatchSizer() self._target_batch_size = self._batch_sizer.get_batch_size( time.time() * 1000) def element_to_client_batch_item(self, element): raise NotImplementedError def add_to_batch(self, client_batch_item): raise NotImplementedError @retry.with_exponential_backoff( num_retries=5, retry_filter=helper.retry_on_rpc_error) def write_mutations(self, throttler, rpc_stats_callback, throttle_delay=1): """Writes a batch of mutations to Cloud Datastore. If a commit fails, it will be retried up to 5 times. All mutations in the batch will be committed again, even if the commit was partially successful. If the retry limit is exceeded, the last exception from Cloud Datastore will be raised. Assumes that the Datastore client library does not perform any retries on commits. It has not been determined how such retries would interact with the retries and throttler used here. See ```` for retry config. Args: rpc_stats_callback: a function to call with arguments `successes` and `failures` and `throttled_secs`; this is called to record successful and failed RPCs to Datastore and time spent waiting for throttling. throttler: (`` AdaptiveThrottler``) Throttler instance used to select requests to be throttled. throttle_delay: (:class:`float`) time in seconds to sleep when throttled. Returns: (int) The latency of the successful RPC in milliseconds. """ # Client-side throttling. while throttler.throttle_request(time.time() * 1000): "Delaying request for %ds due to previous failures", throttle_delay) time.sleep(throttle_delay) rpc_stats_callback(throttled_secs=throttle_delay) if self._batch is None: # this will only happen when we re-try previously failed batch self._batch = self._client.batch() self._batch.begin() for element in self._batch_elements: self.add_to_batch(element) try: start_time = time.time() self._batch.commit() end_time = time.time() rpc_stats_callback(successes=1) throttler.successful_request(start_time * 1000) commit_time_ms = int((end_time - start_time) * 1000) return commit_time_ms except Exception: self._batch = None rpc_stats_callback(errors=1) raise def process(self, element): client_element = self.element_to_client_batch_item(element) self._batch_elements.append(client_element) self.add_to_batch(client_element) self._batch_bytes_size += self._batch.mutations[-1].ByteSize() if (len(self._batch.mutations) >= self._target_batch_size or self._batch_bytes_size > util.WRITE_BATCH_MAX_BYTES_SIZE): self._flush_batch() def finish_bundle(self): if self._batch_elements: self._flush_batch() def _init_batch(self): self._batch_bytes_size = 0 self._batch = self._client.batch() self._batch.begin() self._batch_elements = [] def _flush_batch(self): # Flush the current batch of mutations to Cloud Datastore. latency_ms = self.write_mutations( self._throttler, rpc_stats_callback=self._update_rpc_stats, throttle_delay=util.WRITE_BATCH_TARGET_LATENCY_MS // 1000) _LOGGER.debug( "Successfully wrote %d mutations in %dms.", len(self._batch.mutations), latency_ms) now = time.time() * 1000 self._batch_sizer.report_latency( now, latency_ms, len(self._batch.mutations)) self._target_batch_size = self._batch_sizer.get_batch_size(now) self._init_batch()
[docs]@typehints.with_input_types(types.Entity) class WriteToDatastore(_Mutate): """ Writes elements of type :class:`` to Cloud Datastore. Entity keys must be complete. The ``project`` field in each key must match the project ID passed to this transform. If ``project`` field in entity or property key is empty then it is filled with the project ID passed to this transform. """ def __init__(self, project): """Initialize the `WriteToDatastore` transform. Args: project: (:class:`str`) The ID of the project to write entities to. """ mutate_fn = WriteToDatastore._DatastoreWriteFn(project) super(WriteToDatastore, self).__init__(mutate_fn) class _DatastoreWriteFn(_Mutate.DatastoreMutateFn): def element_to_client_batch_item(self, element): if not isinstance(element, types.Entity): raise ValueError( '' ' expected, got: %s' % type(element)) if not element.key.project: element.key.project = self._project client_entity = element.to_client_entity() if client_entity.key.is_partial: raise ValueError( 'Entities to be written to Cloud Datastore must ' 'have complete keys:\n%s' % client_entity) return client_entity def add_to_batch(self, client_entity): self._batch.put(client_entity) def display_data(self): return { 'mutation': 'Write (upsert)', 'project': self._project, }
[docs]@typehints.with_input_types(types.Key) class DeleteFromDatastore(_Mutate): """ Deletes elements matching input :class:`` elements from Cloud Datastore. Keys must be complete. The ``project`` field in each key must match the project ID passed to this transform. If ``project`` field in key is empty then it is filled with the project ID passed to this transform. """ def __init__(self, project): """Initialize the `DeleteFromDatastore` transform. Args: project: (:class:`str`) The ID of the project from which the entities will be deleted. """ mutate_fn = DeleteFromDatastore._DatastoreDeleteFn(project) super(DeleteFromDatastore, self).__init__(mutate_fn) class _DatastoreDeleteFn(_Mutate.DatastoreMutateFn): def element_to_client_batch_item(self, element): if not isinstance(element, types.Key): raise ValueError( '' ' expected, got: %s' % type(element)) if not element.project: element.project = self._project client_key = element.to_client_key() if client_key.is_partial: raise ValueError( 'Keys to be deleted from Cloud Datastore must be ' 'complete:\n%s' % client_key) return client_key def add_to_batch(self, client_key): self._batch.delete(client_key) def display_data(self): return { 'mutation': 'Delete', 'project': self._project, }