运行apache spark job时,任务不可序列化异常

Question

编写以下java程序来试验apache spark.

程序尝试从相应的文件中读取正面和负面单词列表,将其与主文件进行比较并相应地过滤结果.

import java.io.Serializable;
import java.io.FileNotFoundException;
import java.io.File;
import java.util.*;
import java.util.Iterator;
import java.util.List;
import java.util.List;
import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.Function;

public class SimpleApp implements Serializable{
  public static void main(String[] args) {
    String logFile = "/tmp/master.txt"; // Should be some file on your system
    String positive = "/tmp/positive.txt"; // Should be some file on your system
    String negative = "/tmp/negative.txt"; // Should be some file on your system

    JavaSparkContext sc = new JavaSparkContext("local[4]", "Twitter Analyzer", "/home/welcome/Downloads/spark-1.1.0/", new String[]{"target/scala-2.10/Simple-assembly-0.1.0.jar"});

    JavaRDD<String> positiveComments = sc.textFile(logFile).cache();

    List<String> positiveList = GetSentiments(positive);
    List<String> negativeList= GetSentiments(negative);

    final Iterator<String> iterator = positiveList.iterator();
    int i = 0;
    while (iterator.hasNext())
    {
      JavaRDD<String> numAs = positiveComments.filter(new Function<String, Boolean>()
      {
        public Boolean call(String s)
        {
          return s.contains(iterator.next());
        }
      });

     numAs.saveAsTextFile("/tmp/output/"+ i);
     i++;
     }

  }

public static List<String> GetSentiments(String fileName) {
  List<String> input = new ArrayList<String>();
try
{
  Scanner sc = new Scanner(new File(fileName));

  while (sc.hasNextLine()) {
      input.add(sc.nextLine());
  }
}
catch (FileNotFoundException e){
    // do stuff here..
}
  return input;
}

}

执行spark job时抛出以下错误,

 Exception in thread "main" org.apache.spark.SparkException: Task not serializable
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:1242)
    at org.apache.spark.rdd.RDD.filter(RDD.scala:282)
    at org.apache.spark.api.java.JavaRDD.filter(JavaRDD.scala:78)
    at SimpleApp.main(SimpleApp.java:37)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:328)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.io.NotSerializableException: java.util.ArrayList$Itr
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
    at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73)
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
    ... 12 more

任何指针？

Answer 1

一些Java事实

1. 外部类中定义的任何匿名类都引用外部类.
2. 如果匿名类需要序列化,它将强制您将外部类序列化.
3. 在lambda函数内部,如果使用封闭类的方法,则需要序列化类,如果正在序列化lambda函数.

关于Spark的一些事实.

1. 在Same Executor上,多个任务可以在同一个JVM中同时运行,因为任务在spark中生成为线程.
2. 任何与spark转换函数(map,mapPartitions,keyBy,redudeByKey ...)一起使用的lambda,Anonymous Class将在驱动程序上实例化,序列化并发送给执行程序.
3. 序列化对象意味着将其状态转换为字节流,以便可以将字节流还原为对象的副本.
4. 如果Java对象的类或其任何超类实现java.io.Serializable接口或其子接口java.io.Externalizable,并且其所有非瞬态非静态字段都是可序列化的,则Java对象是可序列化的.

避免序列化问题的经验法则:

1. 避免使用匿名类,而是使用静态类作为匿名类将强制您将外部类序列化.
2. 避免使用静态变量作为序列化问题的解决方法,因为Multiple Task可以在同一个JVM中运行,而静态实例可能不是线程安全的.
3. 使用瞬态变量来避免序列化问题,您必须在函数调用内而不是构造函数中初始化它们.在驱动程序上将调用构造函数,在Executor上它将反序列化并为对象.初始化的唯一方法是在函数调用中.
4. 使用Static类代替匿名类.
5. 宗教上只为仅需要序列化的类遵循"附加实现Serializable"
6. 在"lambda函数"中,永远不会直接引用outclass方法,因为这将导致外部类的序列化.
7. 如果需要在Lambda函数中直接使用方法,请将方法设为静态,否则直接使用Class :: func()notion而不是func()
8. Java Map <>不实现Serializable,但HashMap实现.
9. 在决定使用Braodcast和Raw DataStructures时要明智.如果您看到真正的好处,那么只使用广播.

如需深入了解,请访问 http://bytepadding.com/big-data/spark/understanding-spark-serialization/

Answer 2

当您创建匿名类时,编译器会执行以下操作:

JavaRDD<String> numAs = positiveComments.filter(new Function<String, Boolean>()
      {
        public Boolean call(String s)
        {
          return s.contains(iterator.next());
        }
      });

它将被重写为:

JavaRDD<String> numAs = positiveComments.filter(new Function<String, Boolean>()
      {
        private Iterator<...> $iterator;
        public Boolean call(String s)
        {
          return s.contains($iterator.next());
        }
      });

这就是为什么你可以拥有一个NotSerializableException因为Iterator不可序列化的原因.

为避免这种情况,只需提取下一个结果:

String value = iterator.next();
JavaRDD<String> numAs = positiveComments.filter(new Function<String, Boolean>()
      {
        public Boolean call(String s)
        {
          return s.contains(value);
        }
      });