[
https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=503941&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-503941
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 23/Oct/20 00:44
Start Date: 23/Oct/20 00:44
Worklog Time Spent: 10m
Work Description: robertwb commented on a change in pull request #13144:
URL: https://github.com/apache/beam/pull/13144#discussion_r510535483
##########
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)
- element_state.clear()
- count_state.clear()
-
- @on_timer(EXPIRY_TIMER)
- def expiry(
+ key, batch_values = self.flush_batch(
+ element_state, count_state, buffering_timer)
+ if key is not None:
+ yield key, batch_values
+
+ @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)):
+ key, batch_values = self.flush_batch(
+ element_state, count_state, buffering_timer)
+ if key is not None:
+ yield key, batch_values
+
+ @on_timer(BUFFERING_TIMER)
+ def on_buffering_timer(
+ self,
+ element_state=DoFn.StateParam(ELEMENT_STATE),
+ count_state=DoFn.StateParam(COUNT_STATE),
+ buffering_timer=DoFn.TimerParam(BUFFERING_TIMER)):
+ key, batch_values = self.flush_batch(
+ element_state, count_state, buffering_timer)
+ if key is not None:
+ yield key, batch_values
+
+ def flush_batch(self, element_state, count_state, buffering_timer):
batch = [element for element in element_state.read()]
- if batch:
- key, _ = batch[0]
- batch_values = [v for (k, v) in batch]
- yield (key, batch_values)
- element_state.clear()
- count_state.clear()
+ if not batch:
+ return None, None
Review comment:
Specifically, here you would just `return` and below you would `yield
key, batch_values`.
##########
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)
- element_state.clear()
- count_state.clear()
-
- @on_timer(EXPIRY_TIMER)
- def expiry(
+ key, batch_values = self.flush_batch(
+ element_state, count_state, buffering_timer)
+ if key is not None:
Review comment:
None is a valid key, so this is not quite safe. Instead, you could make
flush_batch an iterable, and just do `return self.flush_batch()` everywhere.
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Issue Time Tracking
-------------------
Worklog Id: (was: 503941)
Time Spent: 15h 10m (was: 15h)
> 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: 15h 10m
> 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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