我正在编写将提取日志的应用程序，因此我实现了函数（Lisitng 1），它将字符串作为参数并从中提取有价值的信息（正则表达式：清单2）。我希望这个方法可以发送给其他工作人员，所以我要阻止可序列化的类。
我在DStreams上应用此方法时遇到问题。这是我的溪流解决方案：
def streamMinner(): Unit = {
val ssc = new StreamingContext(sc, Seconds(2))
val logsStream = ssc.textFileStream("logs/")
// Not works
val extractLogs = logsStream.map( log => new Matcher().matchLog(log))
extractLogs.print(1)
// Works
// val words = logsStream.transform( rdd => rdd.map( log => matchLog(log)))
// words.print()
ssc.start()
ssc.awaitTermination()
}
问题是在哪里，logsStream的每个元素都与Matcher类的新对象配对（new Matcher（）。matchLog（log） Apache Spark给出了以下错误：
ERROR YarnScheduler: Lost executor 2 on host1: Container marked as failed: container_e743_1499728610705_0043_01_000003 on host: host1. Exit status: 50. Diagnostics: Exception from container-launch.
Container id: container_e743_1499728610705_0043_01_000003
Exit code: 50
Stack trace: ExitCodeException exitCode=50:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:600)
at org.apache.hadoop.util.Shell.run(Shell.java:511)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:783)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:303)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 50
ERROR YarnScheduler: Lost executor 5 on host2: Container marked as failed: container_e743_1499728610705_0043_01_000006 on host: host2. Exit status: 50. Diagnostics: Exception from container-launch.
Container id: container_e743_1499728610705_0043_01_000006
Exit code: 50
Stack trace: ExitCodeException exitCode=50:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:600)
at org.apache.hadoop.util.Shell.run(Shell.java:511)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:783)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:303)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 50
...
ERROR YarnScheduler: Lost executor 6 ...
ERROR TaskSetManager: Task 0 in stage 0.0 failed 4 times; aborting job
17/07/11 09:41:09 ERROR JobScheduler: Error running job streaming job 1499758850000 ms.0
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, host1): ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container marked as failed: container_e743_1499728610705_0043_01_000007 on host: host1. Exit status: 50. Diagnostics: Exception from container-launch.
Container id: container_e743_1499728610705_0043_01_000007
Exit code: 50
Stack trace: ExitCodeException exitCode=50:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:600)
at org.apache.hadoop.util.Shell.run(Shell.java:511)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:783)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:303)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 50
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1433)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1421)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1420)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1420)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:801)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1642)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1601)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1590)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:622)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1856)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1869)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1882)
at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1335)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:323)
at org.apache.spark.rdd.RDD.take(RDD.scala:1309)
at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$5$1.apply(DStream.scala:768)
at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$5$1.apply(DStream.scala:767)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:227)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:227)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:227)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:226)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, host1): ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container marked as failed: container_e743_1499728610705_0043_01_000007 on host: host1. Exit status: 50. Diagnostics: Exception from container-launch.
Container id: container_e743_1499728610705_0043_01_000007
Exit code: 50
Stack trace: ExitCodeException exitCode=50:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:600)
at org.apache.hadoop.util.Shell.run(Shell.java:511)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:783)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:303)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 50
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1433)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1421)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1420)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1420)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:801)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1642)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1601)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1590)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:622)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1856)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1869)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1882)
at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1335)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:323)
at org.apache.spark.rdd.RDD.take(RDD.scala:1309)
at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$5$1.apply(DStream.scala:768)
at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$5$1.apply(DStream.scala:767)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:227)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:227)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:227)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:226)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
scala> 17/07/11 09:41:11 ERROR TransportResponseHandler: Still have 1 requests outstanding when connection from host3/11.11.11.11:11111 is closed
17/07/11 09:41:11 ERROR YarnScheduler: Lost executor 4 on host3: Slave lost
17/07/11 09:41:12 ERROR TransportClient: Failed to send RPC 7741519719369750843 to host3/11.11.11.11:11111: java.nio.channels.ClosedChannelException
java.nio.channels.ClosedChannelException
17/07/11 09:41:12 ERROR YarnScheduler: Lost executor 1 on host2: Slave lost
17/07/11 09:41:12 ERROR TransportClient: Failed to send RPC 7734757459881277232 to host3//11.11.11.11:11111: java.nio.channels.ClosedChannelException
java.nio.channels.ClosedChannelException
17/07/11 09:41:12 ERROR YarnScheduler: Lost executor 3 on host4: Slave lost
17/07/11 09:41:12 ERROR TransportClient: Failed to send RPC 5816053641531447955 to host3//11.11.11.11:11111: java.nio.channels.ClosedChannelException
java.nio.channels.ClosedChannelException
17/07/11 09:41:12 ERROR YarnScheduler: Lost executor 7 on host2: Slave lost
17/07/11 09:41:13 ERROR TransportClient: Failed to send RPC 8774007142277591342 to host3/11.11.11.11:11111: java.nio.channels.ClosedChannelException
java.nio.channels.ClosedChannelException
17/07/11 09:41:13 ERROR YarnScheduler: Lost executor 8 on host1: Slave lost
17/07/11 09:41:19 ERROR YarnScheduler: Lost executor 1 on host3: Container marked as failed: container_e743_1499728610705_0043_02_000002 on host: host3. Exit status: 50. Diagnostics: Exception from container-launch.
Container id: container_e743_1499728610705_0043_02_000002
Exit code: 50
Stack trace: ExitCodeException exitCode=50:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:600)
at org.apache.hadoop.util.Shell.run(Shell.java:511)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:783)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:303)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 50
当我评论线条时：
val extractLogs = logsStream.map( log => new Matcher().matchLog(log))
extractLogs.print(1)
我取消注释：
// val words = logsStream.transform( rdd => rdd.map( log => matchLog(log)))
// words.print()
一切正常。我的问题是为什么？我担心有效的解决方案可能无法在群集上并行化，因为方法matchLog不可序列化。有人有类似的问题或知道如何处理它？</ p>
Lisitng 1：
case class logValues2(time_stamp: String, action: String, protocol: String, connection_id: String, src_ip: String, dst_ip: String, src_port: String, dst_port: String, duration: String, bytes: String, user: String) extends Serializable
class Matcher extends Serializable {
def matchLog(x: String): logValues2 = {
var dst_ip = " "
var dst_port = " "
var time_stamp = time_stamp_reg.findAllIn(x).mkString(",")
var action = action_reg.findAllIn(x).mkString(",")
var protocol = protocol_reg.findAllIn(x).mkString(",")
var connection_id = connection_id_reg.findAllIn(x).mkString(",")
var ips = ips_reg.findAllIn(x).mkString(" ").split(""" """)
var src_ip = ips(0)
if (ips.length > 1) {
dst_ip = ips(1)
} else {
dst_ip = " "
}
var ports = ports_reg.findAllIn(x).mkString(" ").split(""" """)
var src_port = ports(0)
if (ports.length > 1) {
dst_port = ports(1)
} else {
dst_port = " "
}
var duration = duration_reg.findAllIn(x).mkString(",")
var bytes = bytes_reg.findAllIn(x).mkString(",")
var user = user_reg.findAllIn(x).mkString(",")
var logObject = logValues2(time_stamp, action, protocol, connection_id, src_ip, dst_ip, src_port, dst_port, duration, bytes, user)
return logObject
}
上面的方法也是单独实现的（不在Matcher类中）。
更新： 我的正则表达式：清单2：
val time_stamp_reg = """^.*?(?=\s\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\s%)""".r
val action_reg = """((?<=:\s)\w{4,10}(?=\s\w{2})|(?<=\w\s)(\w{7,9})(?=\s[f]))""".r
val protocol_reg = """(?<=[\w:]\s)(\w+)(?=\s[cr])""".r
val connection_id_reg = """(?<=\w\s)(\d+)(?=\sfor)""".r
val ips_reg = """(?<=[\d\w][:\s])(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})(?=\/\d+|\z| \w)""".r
val ports_reg = """(?<=\d\/)(\d{1,6})(?=\z|[\s(])""".r
val duration_reg = """(?<=duration\s)(\d{1,2}:\d{1,2}:\d{1,2})(?=\s|\z)""".r
val bytes_reg = """(?<=bytes\s)(\d+)(?=\s|\z)""".r
val user_reg = """(?<=\\\\)(\d+)(?=\W)""".r