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https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=503817&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-503817
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 22/Oct/20 17:23
Start Date: 22/Oct/20 17:23
Worklog Time Spent: 10m
Work Description: boyuanzz commented on a change in pull request #13144:
URL: https://github.com/apache/beam/pull/13144#discussion_r509687809
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -751,24 +751,42 @@ class GroupIntoBatches(PTransform):
GroupIntoBatches is experimental. Its use case will depend on the runner if
it has support of States and Timers.
"""
- def __init__(self, batch_size):
+ def __init__(
+ self, batch_size, max_buffering_duration_secs=None, clock=time.time):
"""Create a new GroupIntoBatches with batch size.
Arguments:
batch_size: (required) How many elements should be in a batch
+ max_buffering_duration_secs: (optional) How long in seconds at most an
+ incomplete batch of elements is allowed to be buffered in the states.
+ The duration must be a positive second duration and should be given as
+ an int or float.
+ clock: (optional) an alternative to time.time (mostly for testing)
"""
self.batch_size = batch_size
+ if max_buffering_duration_secs is not None:
+ assert max_buffering_duration_secs > 0, \
Review comment:
You can use parenthesis to avoid using `\` as line continuation.
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Issue Time Tracking
-------------------
Worklog Id: (was: 503817)
Time Spent: 14h (was: 13h 50m)
> 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: 14h
> 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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