java.lang.ClassCastException在远程服务器上的Spark作业中使用Lambda表达式

小编典典

java.lang.ClassCastException在远程服务器上的Spark作业中使用Lambda表达式

java

我正在尝试使用sparkjava.com框架为我的Apache Spark作业构建Web API。我的代码是：

@Override
public void init() {
    get("/hello",
            (req, res) -> {
                String sourcePath = "hdfs://spark:54310/input/*";

                SparkConf conf = new SparkConf().setAppName("LineCount");
                conf.setJars(new String[] { "/home/sam/resin-4.0.42/webapps/test.war" });
                File configFile = new File("config.properties");

                String sparkURI = "spark://hamrah:7077";

                conf.setMaster(sparkURI);
                conf.set("spark.driver.allowMultipleContexts", "true");
                JavaSparkContext sc = new JavaSparkContext(conf);

                @SuppressWarnings("resource")
                JavaRDD<String> log = sc.textFile(sourcePath);

                JavaRDD<String> lines = log.filter(x -> {
                    return true;
                });

                return lines.count();
            });
}

如果删除lambda表达式或将其放在简单的jar而不是Web服务（以某种方式称为servlet）中，它将运行无任何错误。但是在servlet中使用lambda表达式将导致以下异常：

15/01/28 10:36:33 WARN TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, hamrah): java.lang.ClassCastException: cannot assign instance of java.lang.invoke.SerializedLambda to field org.apache.spark.api.java.JavaRDD$$anonfun$filter$1.f$1 of type org.apache.spark.api.java.function.Function in instance of org.apache.spark.api.java.JavaRDD$$anonfun$filter$1
at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2089)
at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1261)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1999)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1918)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1993)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1918)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1993)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1918)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:87)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:57)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)

PS：我尝试过将jersey和javaspark与码头，tomcat和resin结合使用，所有这些使我得出了相同的结果。

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2020-09-08

共1个答案

小编典典

您在这里遇到的是一个后续错误，该错误掩盖了原始错误。

序列化lambda实例时，它们用于writeReplace从作为SerializedLambda
实例的持久形式中分解其JRE特定实现。当SerializedLambda实例已恢复，它的readResolve方法将被调用来重建适当的拉姆达实例。如文档所述，它将通过调用定义原始lambda的类的特殊方法来做到这一点。重要的一点是，需要原始类，而这正是您的情况所缺少的。

但是，……有……的特殊行为ObjectInputStream。当遇到异常时，它不会立即纾困。它将记录异常并继续该过程，标记当前正在读取的所有对象，从而也将错误对象也视为错误对象。仅在该过程结束时，它才会引发遇到的原始异常。使它如此奇怪的是，它还将继续尝试设置这些对象的字段。但是，当您查看方法ObjectInputStream.readOrdinaryObject行1806时：

…
    if (obj != null &&
        handles.lookupException(passHandle) == null &&
        desc.hasReadResolveMethod())
    {
        Object rep = desc.invokeReadResolve(obj);
        if (unshared && rep.getClass().isArray()) {
            rep = cloneArray(rep);
        }
        if (rep != obj) {
            handles.setObject(passHandle, obj = rep);
        }
    }

    return obj;
}

您会看到报告异常readResolve时它不会调用该方法。但是，如果没有发生替换，那么继续尝试设置引荐来源网址的字段值不是一个好主意，但这正是此处发生的情况，因此生成了一个。lookupException``null``ClassCastException

您可以轻松重现该问题：

public class Holder implements Serializable {
    Runnable r;
}
public class Defining {
    public static Holder get() {
        final Holder holder = new Holder();
        holder.r=(Runnable&Serializable)()->{};
        return holder;
    }
}
public class Writing {
    static final File f=new File(System.getProperty("java.io.tmpdir"), "x.ser");
    public static void main(String... arg) throws IOException {
        try(FileOutputStream os=new FileOutputStream(f);
            ObjectOutputStream   oos=new ObjectOutputStream(os)) {
            oos.writeObject(Defining.get());
        }
        System.out.println("written to "+f);
    }
}
public class Reading {
    static final File f=new File(System.getProperty("java.io.tmpdir"), "x.ser");
    public static void main(String... arg) throws IOException, ClassNotFoundException {
        try(FileInputStream is=new FileInputStream(f);
            ObjectInputStream ois=new ObjectInputStream(is)) {
            Holder h=(Holder)ois.readObject();
            System.out.println(h.r);
            h.r.run();
        }
        System.out.println("read from "+f);
    }
}

编译这四个类并运行Writing。然后删除类文件Defining.class并运行Reading。然后你会得到一个

Exception in thread "main" java.lang.ClassCastException: cannot assign instance of java.lang.invoke.SerializedLambda to field test.Holder.r of type java.lang.Runnable in instance of test.Holder
    at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2089)
    at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1261)

（测试1.8.0_20）

最重要的是，一旦了解了序列化问题，您可能会忘记此序列化问题，解决问题所要做的就是确保定义lambda表达式的类在lambda所在的运行时中也可用反序列化。

直接从IDE运行Spark Job的示例（默认情况下，spark-submit分发jar）：

SparkConf sconf = new SparkConf()
  .set("spark.eventLog.dir", "hdfs://nn:8020/user/spark/applicationHistory")
  .set("spark.eventLog.enabled", "true")
  .setJars(new String[]{"/path/to/jar/with/your/class.jar"})
  .setMaster("spark://spark.standalone.uri:7077");

2020-09-08