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https://issues.apache.org/jira/browse/BEAM-1145?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15745400#comment-15745400
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ASF GitHub Bot commented on BEAM-1145:
--------------------------------------
GitHub user aviemzur opened a pull request:
https://github.com/apache/incubator-beam/pull/1594
[BEAM-1145] Remove classifier from shaded spark runner artifact
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---
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/aviemzur/incubator-beam
remove-spark-runner-shaded-artifact-classifier
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-beam/pull/1594.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1594
----
commit 0b4a65374629b6490b68db3348de427bc2f14e0a
Author: Aviem Zur <aviem...@gmail.com>
Date: 2016-12-13T15:22:46Z
[BEAM-1145] Remove classifier from shaded spark runner artifact
----
> Remove classifier from shaded spark runner artifact
> ---------------------------------------------------
>
> Key: BEAM-1145
> URL: https://issues.apache.org/jira/browse/BEAM-1145
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Reporter: Aviem Zur
> Assignee: Amit Sela
>
> Shade plugin configured in spark runner's pom adds a classifier to spark
> runner shaded jar
> {code:xml}
> <shadedArtifactAttached>true</shadedArtifactAttached>
> <shadedClassifierName>spark-app</shadedClassifierName>
> {code}
> This means, that in order for a user application that is dependent on
> spark-runner to work in cluster mode, they have to add the classifier in
> their dependency declaration, like so:
> {code:xml}
> <dependency>
> <groupId>org.apache.beam</groupId>
> <artifactId>beam-runners-spark</artifactId>
> <version>0.4.0-incubating-SNAPSHOT</version>
> <classifier>spark-app</classifier>
> </dependency>
> {code}
> Otherwise, if they do not specify classifier, the jar they get is unshaded,
> which in cluster mode, causes collisions between different guava versions.
> Example exception in cluster mode when adding the dependency without
> classifier:
> {code}
> 16/12/12 06:58:56 WARN TaskSetManager: Lost task 4.0 in stage 8.0 (TID 153,
> lvsriskng02.lvs.paypal.com): java.lang.NoSuchMethodError:
> com.google.common.base.Stopwatch.createStarted()Lcom/google/common/base/Stopwatch;
> at
> org.apache.beam.runners.spark.stateful.StateSpecFunctions$1.apply(StateSpecFunctions.java:137)
> at
> org.apache.beam.runners.spark.stateful.StateSpecFunctions$1.apply(StateSpecFunctions.java:98)
> at
> org.apache.spark.streaming.StateSpec$$anonfun$1.apply(StateSpec.scala:180)
> at
> org.apache.spark.streaming.StateSpec$$anonfun$1.apply(StateSpec.scala:179)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:57)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:155)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:275)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:149)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:275)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:275)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> I would suggest that the classifier be removed from the shaded jar, to avoid
> confusion among users, and have a better user experience.
> P.S. Looks like Dataflow runner does not add a classifier to its shaded jar.
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