[
https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=513676&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-513676
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 18/Nov/20 18:05
Start Date: 18/Nov/20 18:05
Worklog Time Spent: 10m
Work Description: nehsyc commented on a change in pull request #13292:
URL: https://github.com/apache/beam/pull/13292#discussion_r526310348
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -780,6 +783,48 @@ def expand(self, pcoll):
self.max_buffering_duration_secs,
self.clock))
+ @typehints.with_input_types(Tuple[K, V])
+ @typehints.with_output_types(Tuple[K, Iterable[V]])
+ class WithShardedKey(PTransform):
+ """A GroupIntoBatches transform that outputs batched elements associated
+ with sharded input keys.
+
+ The sharding is determined by the runner to balance the load during the
+ execution time. By default, it spreads the input elements with the same key
+ to all available threads executing the transform.
+ """
+ def __init__(self, batch_size, max_buffering_duration_secs=None):
+ """Create a new GroupIntoBatches.WithShardedKey.
+
+ 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.
+ """
+ self.batch_size = batch_size
+
+ if max_buffering_duration_secs is not None:
+ assert max_buffering_duration_secs > 0, (
+ 'max buffering duration should be a positive value')
+ self.max_buffering_duration_secs = max_buffering_duration_secs
+
+ _pid = os.getpid()
+
+ def expand(self, pcoll):
+ sharded_pcoll = pcoll | Map(
+ lambda x: (
Review comment:
Done
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -780,6 +783,48 @@ def expand(self, pcoll):
self.max_buffering_duration_secs,
self.clock))
+ @typehints.with_input_types(Tuple[K, V])
+ @typehints.with_output_types(Tuple[K, Iterable[V]])
+ class WithShardedKey(PTransform):
+ """A GroupIntoBatches transform that outputs batched elements associated
+ with sharded input keys.
+
+ The sharding is determined by the runner to balance the load during the
+ execution time. By default, it spreads the input elements with the same key
+ to all available threads executing the transform.
+ """
+ def __init__(self, batch_size, max_buffering_duration_secs=None):
+ """Create a new GroupIntoBatches.WithShardedKey.
+
+ 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.
+ """
+ self.batch_size = batch_size
+
+ if max_buffering_duration_secs is not None:
+ assert max_buffering_duration_secs > 0, (
+ 'max buffering duration should be a positive value')
+ self.max_buffering_duration_secs = max_buffering_duration_secs
+
+ _pid = os.getpid()
Review comment:
I see. The pid is probably not a good choice then. I changed it to use
uuid instead, analogous to the Java implementation:
https://github.com/apache/beam/blob/a016ba5632e955af25cedfbc7a9bf93c9ed858ff/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/GroupIntoBatches.java#L96
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Issue Time Tracking
-------------------
Worklog Id: (was: 513676)
Time Spent: 18h 40m (was: 18.5h)
> 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: 18h 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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