def process(self, query, *args, **kwargs):
# distinct key to be used to group query splits.
key = 1
# 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 = [query]
sharded_query_splits = []
for split_query in query_splits:
sharded_query_splits.append((key, split_query))
key += 1
return sharded_query_splits
def process(self, query, *args, **kwargs):
# distinct key to be used to group query splits.
key = 1
# 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 = [query]
sharded_query_splits = []
for split_query in query_splits:
sharded_query_splits.append((key, split_query))
key += 1
return sharded_query_splits
