The error stack is throwing from your code:
Caused by: scala.MatchError: [Ljava.lang.String;@68d279ec (of class
[Ljava.lang.String;)
at com.jd.deeplog.LogAggregator$.main(LogAggregator.scala:29)
at com.jd.deeplog.LogAggregator.main(LogAggregator.scala)
I think you should debug the code yourself, it may not be the problem of
Spark.
On Fri, Jul 15, 2016 at 3:17 PM, Mekal Zheng <mekal.zh...@gmail.com> wrote:
> Hi,
>
> I have a Spark Streaming job written in Scala and is running well on local
> and client mode, but when I submit it on cluster mode, the driver reported
> an error shown as below.
> Is there anyone know what is wrong here?
> pls help me!
>
> the Job CODE is after
>
> 16/07/14 17:28:21 DEBUG ByteBufUtil:
> -Dio.netty.threadLocalDirectBufferSize: 65536
> 16/07/14 17:28:21 DEBUG NetUtil: Loopback interface: lo (lo,
> 0:0:0:0:0:0:0:1%lo)
> 16/07/14 17:28:21 DEBUG NetUtil: /proc/sys/net/core/somaxconn: 32768
> 16/07/14 17:28:21 DEBUG TransportServer: Shuffle server started on port
> :43492
> 16/07/14 17:28:21 INFO Utils: Successfully started service 'Driver' on
> port 43492.
> 16/07/14 17:28:21 INFO WorkerWatcher: Connecting to worker spark://
> Worker@172.20.130.98:23933
> 16/07/14 17:28:21 DEBUG TransportClientFactory: Creating new connection to
> /172.20.130.98:23933
> Exception in thread "main" java.lang.reflect.InvocationTargetException
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:497)
> at
> org.apache.spark.deploy.worker.DriverWrapper$.main(DriverWrapper.scala:58)
> at
> org.apache.spark.deploy.worker.DriverWrapper.main(DriverWrapper.scala)
> Caused by: scala.MatchError: [Ljava.lang.String;@68d279ec (of class
> [Ljava.lang.String;)
> at com.jd.deeplog.LogAggregator$.main(LogAggregator.scala:29)
> at com.jd.deeplog.LogAggregator.main(LogAggregator.scala)
> ... 6 more
>
> ==================
> Job CODE:
>
> object LogAggregator {
>
> val batchDuration = Seconds(5)
>
> def main(args:Array[String]) {
>
> val usage =
> """Usage: LogAggregator <zkQuorum> <group> <topics> <numThreads>
> <logFormat> <logSeparator> <batchDuration> <destType> <destPath>
> | logFormat: fieldName:fieldRole[,fieldName:fieldRole] each field
> must have both name and role
> | logFormat.role: can be key|avg|enum|sum|ignore
> """.stripMargin
>
> if (args.length < 9) {
> System.err.println(usage)
> System.exit(1)
> }
>
> val Array(zkQuorum, group, topics, numThreads, logFormat, logSeparator,
> batchDuration, destType, destPath) = args
>
> println("Start streaming calculation...")
>
> val conf = new SparkConf().setAppName("LBHaproxy-LogAggregator")
> val ssc = new StreamingContext(conf, Seconds(batchDuration.toInt))
>
> val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap
>
> val lines = KafkaUtils.createStream(ssc, zkQuorum, group,
> topicMap).map(_._2)
>
> val logFields = logFormat.split(",").map(field => {
> val fld = field.split(":")
> if (fld.size != 2) {
> System.err.println("Wrong parameters for logFormat!\n")
> System.err.println(usage)
> System.exit(1)
> }
> // TODO: ensure the field has both 'name' and 'role'
> new LogField(fld(0), fld(1))
> })
>
> val keyFields = logFields.filter(logFieldName => {
> logFieldName.role == "key"
> })
> val keys = keyFields.map(key => {
> key.name
> })
>
> val logsByKey = lines.map(line => {
> val log = new Log(logFields, line, logSeparator)
> log.toMap
> }).filter(log => log.nonEmpty).map(log => {
> val keys = keyFields.map(logField => {
> log(logField.name).value
> })
>
> val key = keys.reduce((key1, key2) => {
> key1.asInstanceOf[String] + key2.asInstanceOf[String]
> })
>
> val fullLog = log + ("count" -> new LogSegment("sum", 1))
>
> (key, fullLog)
> })
>
>
> val aggResults = logsByKey.reduceByKey((log_a, log_b) => {
>
> log_a.map(logField => {
> val logFieldName = logField._1
> val logSegment_a = logField._2
> val logSegment_b = log_b(logFieldName)
>
> val segValue = logSegment_a.role match {
> case "avg" => {
> logSegment_a.value.toString.toInt +
> logSegment_b.value.toString.toInt
> }
> case "sum" => {
> logSegment_a.value.toString.toInt +
> logSegment_b.value.toString.toInt
> }
> case "enum" => {
> val list_a = logSegment_a.value.asInstanceOf[List[(String, Int)]]
> val list_b = logSegment_b.value.asInstanceOf[List[(String, Int)]]
> list_a ++ list_b
> }
> case _ => logSegment_a.value
> }
> (logFieldName, new LogSegment(logSegment_a.role, segValue))
> })
> }).map(logRecord => {
> val log = logRecord._2
> val count = log("count").value.toString.toInt
>
>
> val logContent = log.map(logField => {
> val logFieldName = logField._1
> val logSegment = logField._2
> val fieldValue = logSegment.role match {
> case "avg" => {
> logSegment.value.toString.toInt / count
> }
> case "enum" => {
> val enumList = logSegment.value.asInstanceOf[List[(String, Int)]]
> val enumJson = enumList.groupBy(_._1).map(el => el._2.reduce((e1,
> e2) => (e1._1, e1._2.toString.toInt + e2._2.toString.toInt)))
> JSONObject(enumJson)
> }
> case _ => logSegment.value
> }
> (logFieldName, fieldValue)
> })
>
> logContent + ("count" -> count)
> })
>
> if (destType == "hbase") {
>
> val hbaseQuorum = "localhost"
> val hbaseClientPort = "2181"
> val hbaseStore = new HBaseStore(hbaseQuorum, hbaseClientPort,
> keys.toList, "tb_", true)
>
> val jobConf = hbaseStore.jobConf()
>
> aggResults.foreachRDD((rdd, time) => {
> rdd.map(record => {
> val logPut = hbaseStore.convert(record, time)
> (new ImmutableBytesWritable, logPut)
> }).saveAsHadoopDataset(jobConf)
> })
> } else if (destType == "file") {
> aggResults.foreachRDD((rdd, time) => {
> rdd.foreach(record => {
> val res = record + ("timestamp" -> time.milliseconds)
> io.File(destPath).appendAll(res.toString() + "\n")
> })
> })
> }
>
> ssc.start()
> ssc.awaitTermination()
> }
>
>
> --
> Mekal Zheng
> Sent with Airmail
>
