[
https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=503453&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-503453
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 22/Oct/20 00:20
Start Date: 22/Oct/20 00:20
Worklog Time Spent: 10m
Work Description: robertwb commented on a change in pull request #13144:
URL: https://github.com/apache/beam/pull/13144#discussion_r509806698
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -777,33 +795,56 @@ def process(
window=DoFn.WindowParam,
element_state=DoFn.StateParam(ELEMENT_STATE),
count_state=DoFn.StateParam(COUNT_STATE),
- expiry_timer=DoFn.TimerParam(EXPIRY_TIMER)):
+ window_timer=DoFn.TimerParam(WINDOW_TIMER),
+ buffering_timer=DoFn.TimerParam(BUFFERING_TIMER)):
# Allowed lateness not supported in Python SDK
#
https://beam.apache.org/documentation/programming-guide/#watermarks-and-late-data
- expiry_timer.set(window.end)
+ window_timer.set(window.end)
element_state.add(element)
count_state.add(1)
count = count_state.read()
+ if count == 1 and max_buffering_duration_secs is not None:
+ # This is the first element in batch. Start counting buffering time if
a
+ # limit was set.
+ buffering_timer.set(clock() + max_buffering_duration_secs)
if count >= batch_size:
batch = [element for element in element_state.read()]
key, _ = batch[0]
batch_values = [v for (k, v) in batch]
- yield (key, batch_values)
+ yield key, batch_values
element_state.clear()
count_state.clear()
+ buffering_timer.clear()
- @on_timer(EXPIRY_TIMER)
- def expiry(
+ @on_timer(WINDOW_TIMER)
+ def on_window_timer(
self,
element_state=DoFn.StateParam(ELEMENT_STATE),
- count_state=DoFn.StateParam(COUNT_STATE)):
+ count_state=DoFn.StateParam(COUNT_STATE),
+ buffering_timer=DoFn.TimerParam(BUFFERING_TIMER)):
+ batch = [element for element in element_state.read()]
+ if batch:
+ key, _ = batch[0]
Review comment:
Pull this (and the exact same logic above and below) into common helper
method.
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -1009,7 +1050,7 @@ def _process(element):
@staticmethod
@typehints.with_input_types(str)
- @typehints.with_output_types(Union[List[str], List[Tuple[str, str]]])
+ @typehints.with_output_types(Union[List[str], Tuple[str, str]])
Review comment:
Was this wrong?
##########
File path: sdks/python/apache_beam/transforms/util_test.py
##########
@@ -644,6 +657,12 @@ def _create_test_data():
data.append(("key", scientists[index]))
return data
+ class _ExpandIterable(DoFn):
Review comment:
_ExpandValuesIterable?
This could also be `FlatMapTuple(lambda k, vs: vs)`
##########
File path: sdks/python/apache_beam/transforms/util_test.py
##########
@@ -110,10 +117,16 @@ def test_windowed_batches(self):
| util.BatchElements(
min_batch_size=5, max_batch_size=10, clock=FakeClock())
| beam.Map(len))
- assert_that(res, equal_to([
- 5, 5, 10, 10, # elements in [0, 30)
- 10, 7, # elements in [30, 47)
- ]))
+ assert_that(
+ res,
+ equal_to([
+ 5,
+ 5,
+ 10,
+ 10, # elements in [0, 30)
Review comment:
Nit: I prefer the previous formatting (as the comment applies to the
whole set of elements).
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Issue Time Tracking
-------------------
Worklog Id: (was: 503453)
Time Spent: 13h 40m (was: 13.5h)
> GroupIntoBatches with Runner-determined Sharding
> ------------------------------------------------
>
> Key: BEAM-10475
> URL: https://issues.apache.org/jira/browse/BEAM-10475
> Project: Beam
> Issue Type: Improvement
> Components: runner-dataflow
> Reporter: Siyuan Chen
> Assignee: Siyuan Chen
> Priority: P2
> Labels: GCP, performance
> Time Spent: 13h 40m
> Remaining Estimate: 0h
>
> [https://s.apache.org/sharded-group-into-batches|https://s.apache.org/sharded-group-into-batches__]
> Improve the existing Beam transform, GroupIntoBatches, to allow runners to
> choose different sharding strategies depending on how the data needs to be
> grouped. The goal is to help with the situation where the elements to process
> need to be co-located to reduce the overhead that would otherwise be incurred
> per element, while not losing the ability to scale the parallelism. The
> essential idea is to build a stateful DoFn with shardable states.
>
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