将 RDD 转换为 DataFrame Spark Streaming 时出现 ClassCastException

Question

大家好，我有下一个问题。我使用带有 Java 的 Apache Spark Streaming v1.6.0 从 IBM MQ 获取一些消息。我为 MQ 制作了自定义接收器，但我遇到的问题是我需要将 RDD 从 JavaDStream 转换为 DataFrame。为此，我用 foreachRDD 迭代 JavaDStream，并为 DataFrame 定义了模式，但是当我运行作业时，第一条消息抛出下一个异常：

java.lang.ClassCastException: org.apache.spark.rdd.BlockRDDPartition 无法转换为 org.apache.spark.rdd.ParallelCollectionPartition 在 org.apache.spark.rdd.ParallelCollectionRDD.compute(ParallelCollectionRDD.scala:102) 在 org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) 在 org.apache.spark.rdd.RDD.iterator(RDD.scala:270) 在 org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) 在 org.apache.spark.scheduler.Task.run(Task.scala:89) 在 org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) 在 java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 在 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 在 java.lang.Thread.run(Thread.java:748) 28 年 3 月 19 日 12:53:26 警告 TaskSetManager：阶段 0.0 中丢失任务 0.0（TID 0，本地主机）：java.lang.ClassCastException：org.apache.spark.rdd.BlockRDDPartition 无法转换为 org.apache.spark.rdd.ParallelCollectionPartition 在 org.apache.spark.rdd.ParallelCollectionRDD.compute(ParallelCollectionRDD.scala:102) 在 org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) 在 org.apache.spark.rdd.RDD.iterator(RDD.scala:270) 在 org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) 在 org.apache.spark.scheduler.Task.run(Task.scala:89) 在 org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) 在 java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 在 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 在 java.lang.Thread.run(Thread.java:748)

然后代码执行的很好。即使我在 MQ 中没有任何消息，这只是我运行工作时的第一条消息。

这是我的 CustomMQReceiver

public CustomMQReceiver() {

        super(StorageLevel.MEMORY_ONLY_2());

    }

    @Override
    public void onStart() {

        new Thread() {
            @Override
            public void run() {
                try {
                    initConnection();
                    receive();
                } catch (JMSException ex) {
                    ex.printStackTrace();
                }
            }
        }.start();

    }

    @Override
    public void onStop() {

    }

    private void receive() {

        System.out.print("Started receiving messages from MQ");

        try {

            Message receivedMessage = null;

            while (!isStopped() && (receivedMessage = consumer.receiveNoWait()) != null) {

                String userInput = convertStreamToString(receivedMessage);
                System.out.println("Received data :'" + userInput + "'");
                store(userInput);
            }

            stop("No More Messages To read !");
            qCon.close();
            System.out.println("Queue Connection is Closed");

        } catch (Exception e) {
            e.printStackTrace();
            restart("Trying to connect again");
        } catch (Throwable t) {

            restart("Error receiving data", t);
        }

    }

    public void initConnection() throws JMSException {

        MQQueueConnectionFactory conFactory = new MQQueueConnectionFactory();
        conFactory.setHostName(HOST);
        conFactory.setPort(PORT);
        conFactory.setIntProperty(WMQConstants.WMQ_CONNECTION_MODE, WMQConstants.WMQ_CM_CLIENT);
        conFactory.setQueueManager(QMGR);
        conFactory.setChannel(CHANNEL);
        conFactory.setBooleanProperty(WMQConstants.USER_AUTHENTICATION_MQCSP, true);
        conFactory.setStringProperty(WMQConstants.USERID, APP_USER);
        conFactory.setStringProperty(WMQConstants.PASSWORD, APP_PASSWORD);

        qCon = (MQQueueConnection) conFactory.createConnection();
        MQQueueSession qSession = (MQQueueSession) qCon.createQueueSession(false, 1);
        MQQueue queue = (MQQueue) qSession.createQueue(QUEUE_NAME);
        consumer = (MQMessageConsumer) qSession.createConsumer(queue);
        qCon.start();

    }

    @Override
    public StorageLevel storageLevel() {
        return StorageLevel.MEMORY_ONLY_2();
    }

    private static String convertStreamToString(final Message jmsMsg) throws Exception {

        String stringMessage = "";
        JMSTextMessage msg = (JMSTextMessage) jmsMsg;
        stringMessage = msg.getText();

        return stringMessage;
    }

这是我的 spark 代码

SparkConf sparkConf = new SparkConf()
                    .setAppName("MQStreaming")
                    .set("spark.driver.allowMultipleContexts", "true")
                    .setMaster("local[*]");

            JavaSparkContext jsc = new JavaSparkContext(sparkConf);
            final SQLContext sqlContext = new SQLContext(jsc);
            JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(Long.parseLong(propertiesConf.getProperty("duration"))));

            JavaDStream<String> customReceiverStream = ssc.receiverStream(new CustomMQReceiver());

            customReceiverStream.foreachRDD(new VoidFunction<JavaRDD<String>>() {

                @Override
                public void call(JavaRDD<String> rdd) throws Exception {

                    JavaRDD<Row> rddRow = rdd.map(new Function<String, Row>() {

                        @Override
                        public Row call(String v1) throws Exception {

                            return RowFactory.create(v1);

                        }

                    });

                    try {

                        StructType schema = new StructType(new StructField[]{
                            new StructField("trama", DataTypes.StringType, true, Metadata.empty())
                        });

                        DataFrame frame = sqlContext.createDataFrame(rddRow, schema);

                        if (frame.count() > 0) {
                            //Here is where the first messages throw the exception
                            frame.show();
                            frame.write().mode(SaveMode.Append).json("file:///C:/tmp/");

                        }

                    } catch (Exception ex) {

                        System.out.println(" INFO " + ex.getMessage());

                    }

                }

            });

            ssc.start();
            ssc.awaitTermination();

我无法更改 spark 的版本，因为此作业将运行在具有 spark 1.6 的旧 cloudera 集群中。我不知道我是做错了什么还是只是一个错误。求助！！！

Answer 1

我解决了我自己的问题，这个异常是我创建 SQLContext 的方式抛出的，正确的方法是用 JavaStreamingContext 创建 sqlContext

//JavaStreamingContext jsc = ...
SQLContext sqlContext = new SQLContext(jsc.sparkContext());

将 RDD 转换为 DataFrame Spark Streaming 时出现 ClassCastException

ClassCastException at convert RDD to DataFrame Spark Streaming

java

apache-spark

streaming

spark-streaming