[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16259587#comment-16259587
]
yuhao yang commented on SPARK-22427:
I tried with larger scale data but did not repro the issue. [~lyt] Can you
please provide the reference for your dataset, or some size info? Thanks.
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
> at org.apache.spark.d
[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16249097#comment-16249097
]
lyt commented on SPARK-22427:
-
-Xss512m didn’t work
发自我的iPhone
-- Original --
From: yuhao yang (JIRA)
Date: 周一,11月 13,2017 8:03 上午
To: 452104285 <452104...@qq.com>
Subject: Re: [jira] [Commented] (SPARK-22427) StackOverFlowError when
usingFPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16249017#comment-16249017
]
yuhao yang commented on SPARK-22427:
Hi [~lyt] does increasing stack size resolve your issue? If not I will look
into it.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethod
[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16249017#comment-16249017
]
yuhao yang commented on SPARK-22427:
Hi [~lyt] does increasing stack size resolve your issue? If not I will look
into it.
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
> at org.ap
[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16240662#comment-16240662
]
lyt commented on SPARK-22427:
-
Maybe it's problem of DataFrame.
This problem won't occur when using pure rdds.
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
> at org.apache.spark.deploy.Sp
[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16239824#comment-16239824
]
Liang-Chi Hsieh commented on SPARK-22427:
-
>From a rough glance, looks like the error didn't be thrown inside ml.FPGrowth?
{code}
...
at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
at DataMining.testFPG$.main(FPGrowth.scala:36)
at DataMining.testFPG.main(FPGrowth.scala)
...
{code}
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
> at
> org.apache.spark.
[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16237174#comment-16237174
]
yuhao yang commented on SPARK-22427:
Could you please try to increase the stack size, E.g. with -Xss10m ?
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>Priority: Normal
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
>
[jira] [Commented] (SPARK-22427) StackOverFlowError when using FPGrowth
[
https://issues.apache.org/jira/browse/SPARK-22427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16237112#comment-16237112
]
Kazuaki Ishizaki commented on SPARK-22427:
--
Thank you for reporting an issue. Could you please attach the data file? Or,
can you write data size with a part of example data?
> StackOverFlowError when using FPGrowth
> --
>
> Key: SPARK-22427
> URL: https://issues.apache.org/jira/browse/SPARK-22427
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.2.0
> Environment: Centos Linux 3.10.0-327.el7.x86_64
> java 1.8.0.111
> spark 2.2.0
>Reporter: lyt
>Priority: Normal
>
> code part:
> val path = jobConfig.getString("hdfspath")
> val vectordata = sc.sparkContext.textFile(path)
> val finaldata = sc.createDataset(vectordata.map(obj => {
> obj.split(" ")
> }).filter(arr => arr.length > 0)).toDF("items")
> val fpg = new FPGrowth()
>
> fpg.setMinSupport(minSupport).setItemsCol("items").setMinConfidence(minConfidence)
> val train = fpg.fit(finaldata)
> print(train.freqItemsets.count())
> print(train.associationRules.count())
> train.save("/tmp/FPGModel")
> And encountered following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2430)
> at
> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836)
> at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
> at DataMining.FPGrowth$.runJob(FPGrowth.scala:116)
> at DataMining.testFPG$.main(FPGrowth.scala:36)
> at DataMining.testFPG.main(FPGrowth.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
>
