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Created June 11, 2015 22:38
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Batching input reader for appengine-mapreduce
class KeyRangeBatchKeyIterator(
datastore_range_iterators.AbstractKeyRangeIterator):
"""Yields datastore.Key type within a key range in batches.
This allows us to do several RPCs in parallel, hopefully reducing running
time.
TODO(colin): the query batch size is the same as the processing batch size.
Figure out if there's a benefit to changing this.
"""
_KEYS_ONLY = True
def __iter__(self):
"""Yield batches of keys for mapping.
The yielded batch is a generator, so that the query cursor doesn't move
until the mapper starts processing elements.
"""
self._query = self._key_range.make_ascending_datastore_query(
self._query_spec.entity_kind, filters=self._query_spec.filters)
while True:
keys = itertools.islice(
self._query.Run(config=datastore_query.QueryOptions(
batch_size=self._query_spec.batch_size,
keys_only=True,
start_cursor=self._cursor)),
self._query_spec.batch_size)
yield keys
if self._cursor == self._query.GetCursor():
# We haven't moved since last time, which means we're at the
# end.
break
self._cursor = self._query.GetCursor()
def _get_cursor(self):
"""Get the current query cursor.
(AbstractKeyRangeIterator method taken from KeyRangeEntityIterator.)
"""
if self._query:
return self._query.GetCursor()
return self._cursor
# Register the new iterator so that the mapreduce library knows how to create
# one from the JSON mapreduce payload.
datastore_range_iterators._KEY_RANGE_ITERATORS[
KeyRangeBatchKeyIterator.__name__] = KeyRangeBatchKeyIterator
class DatastoreKeyBatchingInputReader(
mapreduce.input_readers.RawDatastoreInputReader):
"""Mapreduce input reader that yields datastore keys in batch."""
_KEY_RANGE_ITER_CLS = KeyRangeBatchKeyIterator
The MIT License (MIT)
Copyright (c) 2015 Khan Academy
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