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[英]Spark Streaming, kafka: java.lang.StackOverflowError
[英]java.lang.StackOverflowError throw in spark-submit but not in running in IDE
我已經開發了用於協作過濾的Spark 2.2應用程序。 它可以在IntelliJ中正常運行或調試。 我也可以輸入Spark Web UI來檢查過程。 但是,當我嘗試將其部署到EMR並在本地測試spark-submit時,程序無法正常運行。
spark Submit命令的一部分:
spark-submit -v --master local[*] --deploy-mode client --executor-memory 4G --num-executors 10 --conf spark.executor.extraJavaOptions="-Xss200M " --conf spark.executor.memory="500M"
def finalStep(sc: SparkContext): Unit = {
val sameModel = MatrixFactorizationModel.load(sc, "CollaborativeFilter")
val globalInterestStats = mutable.Map[
Int, (DescriptiveStatistics, mutable.MutableList[Rating])
]()
val taxonsForUsers = sameModel.recommendProductsForUsers(200)
taxonsForUsers
.collect()
.flatMap(userToInterestArr => {
userToInterestArr._2.map(rating => {
if (globalInterestStats.get(rating.product).isEmpty) {
globalInterestStats(rating.product) = (
new DescriptiveStatistics(),
mutable.MutableList[Rating]()
)
}
globalInterestStats(rating.product)._1.addValue(rating.rating)
(rating, userToInterestArr._2)
})
})
.foreach(ratingToUserInterestArr => {
val rating = ratingToUserInterestArr._1
if (globalInterestStats.get(rating.product).isDefined) {
val interestStats = globalInterestStats(rating.product)
val userInterests = ratingToUserInterestArr._2
if (rating.rating >= interestStats._1.getPercentile(75)) {
userInterests.foreach(each => interestStats._2 += each)
}
}
})
println(globalInterestStats.toSeq.length) // ~300
val globalInterestRDD = sc.parallelize(globalInterestStats.toSeq, 100)// No. of partition does not matter
val nGlobalInterests = globalInterestStats.map(each => each._2._2.length).sum
// It was not working in spark-submit but I managed to convert this part of code to simplify code before creating the RDD
val taxonIDFMap = sc.parallelize(
globalInterestStats
.toSeq
.flatMap(each => {
each._2._2
.foldLeft(mutable.Map[Int, Double]())(op = (accu, value) => {
if (accu.get(value.product).isEmpty) {
accu(value.product) = 1
} else {
accu(value.product) += 1
}
accu
})
.toList
}), 100)
.reduceByKey((accu, value) => accu + value)
.map(each => {
val a: Double = Math.log10(nGlobalInterests / (1 + each._2)) / Math.log10(2)
(
each._1,
a
)
})
.collect()
.toMap
// Yet I have a way more complicated task need to operate on globalInterestRDD which I cannot simplify the size for Spark to handle
val result = globalInterestRDD
.count()
sc.stop()
println(result)
}
Exception in thread "dispatcher-event-loop-1" java.lang.StackOverflowError
at java.io.ObjectOutputStream$ReplaceTable.lookup(ObjectOutputStream.java:2399)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1113)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
...
我猜它與以下內容高度相關: http : //asyncified.io/2016/12/10/mutablelist-and-the-short-path-to-a-stackoverflowerror/
但是我仍在嘗試理解並修復我的代碼
問題是
val globalInterestStats = mutable.Map[
Int, (DescriptiveStatistics, mutable.MutableList[Rating])
]()
應該
val globalInterestStats = mutable.Map[
Int, (DescriptiveStatistics, mutable.ArrayBuffer[Rating])
]()
盡管為什么spark應用程序不能在IDE中運行但不能在spark-submit中運行仍然沒有意義
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