In this kind of question, you always want to tell us the spark version.
Yong
________________________________
From: darin <lidal...@foxmail.com>
Sent: Thursday, March 16, 2017 9:59 PM
To: user@spark.apache.org
Subject: spark streaming exectors memory increasing and executor killed by yarn
Hi,
I got this exception when streaming program run some hours.
```
*User class threw exception: org.apache.spark.SparkException: Job aborted
due to stage failure: Task 21 in stage 1194.0 failed 4 times, most recent
failure: Lost task 21.3 in stage 1194.0 (TID 2475, 2.dev3, executor 66):
ExecutorLostFailure (executor 66 exited caused by one of the running tasks)
Reason: Container killed by YARN for exceeding memory limits. 3.5 GB of 3.5
GB physical memory used. Consider boosting
spark.yarn.executor.memoryOverhead.*
```
I have googled some solutions like close yarn memory monitor ,increasing
exector memory... .I think it is not the right way .
And this is the submit script:
```
*spark-submit --master yarn-cluster --driver-cores 1 --driver-memory 1G
--num-executors 6 --executor-cores 3 --executor-memory 3G --conf
"spark.executor.extraJavaOptions=-XX:+UseConcMarkSweepGC -XX:+UseParNewGC
-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/javadump.hprof" --conf
"spark.kryoserializer.buffer.max=512m" --class
com.dtise.data.streaming.ad.DTStreamingStatistics
hdfs://nameservice1/user/yanghb/spark-streaming-1.0.jar*
```
And This is the main codes:
```
val originalStream = ssc.textFileStream(rawDataPath)
originalStream.repartition(10).mapPartitions(parseAdLog).reduceByKey(_
++ _)
.mapWithState(StateSpec.function(countAdLogWithState
_)).foreachRDD(rdd => {
if (!rdd.isEmpty()) {
val batchTime = Calendar.getInstance.getTimeInMillis
val dimensionSumMap = rdd.map(_._1).reduce(_ ++ _)
val nameList = rdd.map(_._2).reduce(_ ++ _).toList
val jedis = RedisUtils.jedis()
jedis.hmset(joinString("t_ad_dimension_sum", batchTime),
dimensionSumMap)
jedis.lpush(joinString("t_ad_name", batchTime), nameList: _*)
jedis.set(joinString("t_ad", batchTime.toString), "OK")
jedis.close()
rdd.flatMap(_._3).foreachPartition(logInfoList => {
val producter = new StringProducter
for (logInfo <- logInfoList) {
val logInfoArr = logInfo.split("\t", -1)
val kafkaKey = "ad/" + logInfoArr(campaignIdIdx) + "/" +
logInfoArr(logDateIdx)
producter.send("cookedLog", kafkaKey, logInfo)
}
producter.close()
})
}
})
```
These are jvm heap mat results
<http://apache-spark-user-list.1001560.n3.nabble.com/file/n28500/QQ20170317-095238%402x.png>
<http://apache-spark-user-list.1001560.n3.nabble.com/file/n28500/QQ20170317-095254%402x.png>
<http://apache-spark-user-list.1001560.n3.nabble.com/file/n28500/QQ20170317-095331%402x.png>
/*Anybody has any advice about this ?
Thanks*/
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