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https://issues.apache.org/jira/browse/BEAM-5775?focusedWorklogId=226562&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-226562
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ASF GitHub Bot logged work on BEAM-5775:
----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Apr/19 10:03
Start Date: 12/Apr/19 10:03
Worklog Time Spent: 10m
Work Description: iemejia commented on pull request #6714: [BEAM-5775]
Spark: implement a custom class to lazily encode values for persistence.
URL: https://github.com/apache/beam/pull/6714#discussion_r274826081
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupCombineFunctions.java
##########
@@ -182,119 +176,4 @@
.map(TranslationUtils.fromPairFunction())
.map(TranslationUtils.toKVByWindowInValue());
}
-
- /**
- * Wrapper around accumulated (combined) value with custom lazy
serialization. Serialization is
- * done through given coder and it is performed within on-serialization
callbacks {@link
- * #writeObject(ObjectOutputStream)} and {@link
KryoAccumulatorSerializer#write(Kryo, Output,
- * SerializableAccumulator)}. Both Spark's serialization mechanisms (Java
Serialization, Kryo) are
- * supported. Materialization of accumulated value is done when value is
requested to avoid
- * serialization of the coder itself.
- *
- * @param <AccumT>
- */
- public static class SerializableAccumulator<AccumT> implements Serializable {
Review comment:
Huge +1
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Issue Time Tracking
-------------------
Worklog Id: (was: 226562)
Time Spent: 9h (was: 8h 50m)
> Make the spark runner not serialize data unless spark is spilling to disk
> -------------------------------------------------------------------------
>
> Key: BEAM-5775
> URL: https://issues.apache.org/jira/browse/BEAM-5775
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Reporter: Mike Kaplinskiy
> Assignee: Mike Kaplinskiy
> Priority: Minor
> Labels: triaged
> Time Spent: 9h
> Remaining Estimate: 0h
>
> Currently for storage level MEMORY_ONLY, Beam does not coder-ify the data.
> This lets Spark keep the data in memory avoiding the serialization round
> trip. Unfortunately the logic is fairly coarse - as soon as you switch to
> MEMORY_AND_DISK, Beam coder-ifys the data even though Spark might have chosen
> to keep the data in memory, incurring the serialization overhead.
>
> Ideally Beam would serialize the data lazily - as Spark chooses to spill to
> disk. This would be a change in behavior when using beam, but luckily Spark
> has a solution for folks that want data serialized in memory -
> MEMORY_AND_DISK_SER will keep the data serialized.
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