def expand(self, pcoll):
return (
pcoll
# Bind window info to each element using element timestamp (or publish time).
| "Window into fixed intervals" >> WindowInto(
FixedWindows(self.window_size))
| "Add timestamp to windowed elements" >> ParDo(AddTimestamp())
# Assign a random key to each windowed element based on the number of shards.
| "Add key" >>
WithKeys(lambda _: random.randint(0, self.num_shards - 1))
# Group windowed elements by key. All the elements in the same window must fit
# memory for this. If not, you need to use `beam.util.BatchElements`.
| "Group by key" >> GroupByKey())
def main():
options = PipelineOptions()
options.view_as(SetupOptions).save_main_session = True
p = Pipeline(options=options)
(p
| Create(list(range(NUM_SHARDS)))
| FlatMap(lambda _:
(bytes(ELEMENT_BYTES) for _ in range(NUM_ELEMENTS_PER_SHARD)))
| WithKeys('')
| ParDo(BigBagDoFn()))
p.run()
def main():
options = PipelineOptions()
options.view_as(SetupOptions).save_main_session = True
BATCH_SIZE = 1000000
BUFFERING_SECS = 600
p = Pipeline(options=options)
(p
| Create(range(100), reshuffle=True)
| ParDo(make_large_elements) # 128 KiB
| WithKeys('')
| GroupIntoBatchesWithMultiBags(BATCH_SIZE, BUFFERING_SECS) # Big batch size with 1 minute trigger
| Map(lambda kv: logging.info('key: %s, value count: %s',
kv[0], len(kv[1]))))
run = p.run()
run.wait_until_finish()
def main():
options = PipelineOptions()
options.view_as(SetupOptions).save_main_session = True
BATCH_SIZE = 1000000
BUFFERING_SECS = 600
p = Pipeline(options=options)
(p
| Create(range(100), reshuffle=True)
| ParDo(make_large_elements) # 128 KiB
| WithKeys('')
| WindowInto(GlobalWindows(),
trigger=Repeatedly(
AfterAny(AfterCount(BATCH_SIZE),
AfterProcessingTime(BUFFERING_SECS))),
accumulation_mode=AccumulationMode.DISCARDING)
| GroupByKey()
| Map(lambda kv: logging.info('key: %s, value count: %s', kv[0], len(kv[1]
))))
run = p.run()
run.wait_until_finish()