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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/06/11 00:28:00 UTC
[jira] [Resolved] (SPARK-8200) Exception in
StreamingLinearAlgorithm on Stream with Empty RDD.
[ https://issues.apache.org/jira/browse/SPARK-8200?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-8200.
------------------------------
Resolution: Fixed
Fix Version/s: 1.4.1
1.5.0
Issue resolved by pull request 6713
[https://github.com/apache/spark/pull/6713]
> Exception in StreamingLinearAlgorithm on Stream with Empty RDD.
> ---------------------------------------------------------------
>
> Key: SPARK-8200
> URL: https://issues.apache.org/jira/browse/SPARK-8200
> Project: Spark
> Issue Type: Bug
> Components: MLlib, Streaming
> Affects Versions: 1.3.1
> Environment: Ubuntu 14.04.2 LTS
> Linux 3.13.0-45-generic #74-Ubuntu SMP Tue Jan 13 19:36:28 UTC 2015
> java version "1.8.0_25"
> Java(TM) SE Runtime Environment (build 1.8.0_25-b17)
> Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)
> Scala code runner version 2.10.4 -- Copyright 2002-2013, LAMP/EPFL
> Reporter: Paavo Parkkinen
> Priority: Minor
> Fix For: 1.5.0, 1.4.1
>
>
> When training a streaming logistic regression model or a streaming linear regression model, any empty RDDs in a stream will cause an exception.
> java.lang.UnsupportedOperationException: empty collection
> at org.apache.spark.rdd.RDD$$anonfun$first$1.apply(RDD.scala:1288)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
> at org.apache.spark.rdd.RDD.first(RDD.scala:1285)
> at org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:215)
> at org.apache.spark.mllib.regression.StreamingLinearAlgorithm$$anonfun$trainOn$1.apply(StreamingLinearAlgorithm.scala:91)
> at org.apache.spark.mllib.regression.StreamingLinearAlgorithm$$anonfun$trainOn$1.apply(StreamingLinearAlgorithm.scala:85)
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:42)
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:40)
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