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
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