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https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=416307&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-416307
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ASF GitHub Bot logged work on BEAM-9434:
----------------------------------------
Author: ASF GitHub Bot
Created on: 06/Apr/20 01:30
Start Date: 06/Apr/20 01:30
Worklog Time Spent: 10m
Work Description: lukecwik commented on pull request #11037: [BEAM-9434]
performance improvements reading many Avro files in S3
URL: https://github.com/apache/beam/pull/11037#discussion_r403789265
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java
##########
@@ -695,8 +695,10 @@ public void evaluate(Reshuffle<K, V> transform,
EvaluationContext context) {
final WindowedValue.WindowedValueCoder<KV<K, V>> wvCoder =
WindowedValue.FullWindowedValueCoder.of(coder,
windowFn.windowCoder());
+ int numPartitions =
+ Math.max(context.getSparkContext().defaultParallelism(),
inRDD.getNumPartitions());
Review comment:
I believe we should do the same thing in the streaming and batch portable
pipeline translators as well.
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Issue Time Tracking
-------------------
Worklog Id: (was: 416307)
Time Spent: 3h 20m (was: 3h 10m)
> Performance improvements processing a large number of Avro files in S3+Spark
> ----------------------------------------------------------------------------
>
> Key: BEAM-9434
> URL: https://issues.apache.org/jira/browse/BEAM-9434
> Project: Beam
> Issue Type: Improvement
> Components: io-java-aws, sdk-java-core
> Affects Versions: 2.19.0
> Reporter: Emiliano Capoccia
> Assignee: Emiliano Capoccia
> Priority: Minor
> Time Spent: 3h 20m
> Remaining Estimate: 0h
>
> There is a performance issue when processing a large number of small Avro
> files in Spark on K8S (tens of thousands or more).
> The recommended way of reading a pattern of Avro files in Beam is by means of:
>
> {code:java}
> PCollection<AvroGenClass> records = p.apply(AvroIO.read(AvroGenClass.class)
> .from("s3://my-bucket/path-to/*.avro").withHintMatchesManyFiles())
> {code}
> However, in the case of many small files, the above results in the entire
> reading taking place in a single task/node, which is considerably slow and
> has scalability issues.
> The option of omitting the hint is not viable, as it results in too many
> tasks being spawn, and the cluster being busy doing coordination of tiny
> tasks with high overhead.
> There are a few workarounds on the internet which mainly revolve around
> compacting the input files before processing, so that a reduced number of
> bulky files is processed in parallel.
>
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