Question

我跟随使用客户接收器的火花流示例，如Spark customer receiver.中提供的火花网站所示

然而，这项工作似乎减少了我的大部分数据。无论我流传输的数据量如何，都会在消费者处成功接收。但是，当我对它进行任何map / flatmap操作时，我只看到10行数据。无论我传输多少数据，情况总是如此。

我已修改此程序以从ActiveMQ队列中读取。如果我查看ActiveMQ Web界面，spark作业会成功消耗我生成的所有数据。但是，每批只处理10个数据。我尝试将批量大小更改为各种值，并尝试在本地以及6节点火花簇上 - 无处不在的结果相同。

我真的很沮丧，因为我不知道为什么要处理有限数量的数据。我在这里找不到什么东西？

这是我的火花计划。自定义接收器包括在内。另外，我并没有真正创建任何套接字连接。相反，我为了测试目的而对消息进行硬编码。行为与为流创建套接字连接时的行为相同。

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.rzt.main;

import com.google.common.collect.Lists;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.storage.StorageLevel;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.receiver.Receiver;
import scala.Tuple2;

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.ConnectException;
import java.net.Socket;
import java.util.regex.Pattern;

/**
 * Custom Receiver that receives data over a socket. Received bytes is
 * interpreted as text and \n delimited lines are considered as records. They
 * are then counted and printed.
 *
 * Usage: TestReceiv3 <master> <hostname> <port> <master> is the Spark master
 * URL. In local mode, <master> should be 'local[n]' with n > 1. <hostname> and
 * <port> of the TCP server that Spark Streaming would connect to receive data.
 *
 * To run this on your local machine, you need to first run a Netcat server `$
 * nc -lk 9999` and then run the example `$ bin/run-example
 * org.apache.spark.examples.streaming.TestReceiv3 localhost 9999`
 */

public class TestReceiv3 extends Receiver<String> {
    private static final Pattern SPACE = Pattern.compile(" ");

    public static void main(String[] args) {

        // Create the context with a 1 second batch size
        SparkConf sparkConf = new SparkConf().setAppName("TestReceiv3").setMaster("local[4]");
        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));

        // Create a input stream with the custom receiver on target ip:port and
        // count the
        // words in input stream of \n delimited text (eg. generated by 'nc')
        JavaReceiverInputDStream<String> lines = ssc.receiverStream(new TestReceiv3("TEST", 1));
        JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            public Iterable<String> call(String x) {
                System.out.println("Message received" + x);
                return Lists.newArrayList(x);
            }
        });

        words.print();
        ssc.start();
        ssc.awaitTermination();
    }

    // ============= Receiver code that receives data over a socket
    // ==============

    String host = null;
    int port = -1;

    public TestReceiv3(String host_, int port_) {
        super(StorageLevel.MEMORY_AND_DISK_2());
        host = host_;
        port = port_;
    }

    public void onStart() {
        // Start the thread that receives data over a connection
        new Thread() {
            @Override
            public void run() {
                receive();
            }
        }.start();
    }

    public void onStop() {
        // There is nothing much to do as the thread calling receive()
        // is designed to stop by itself isStopped() returns false
    }

    /** Create a socket connection and receive data until receiver is stopped */
    private void receive() {
        Socket socket = null;
        String userInput = null;

        try {

            int i = 0;
            // Until stopped or connection broken continue reading
            while (true) {
                i++;
                store("MESSAGE " + i);
                if (i == 1000)
                    break;
            }

            // Restart in an attempt to connect again when server is active
            // again
            restart("Trying to connect again");
        } catch (Throwable t) {
            restart("Error receiving data", t);
        }
    }
}

Answer 1

您看到的输出来自words.print()。 DStream.print仅打印DStream的前10个元素。来自docs：

def print（）：单位

打印此DStream中生成的每个RDD的前十个元素。   这是一个输出操作符，因此这个DStream将被注册为   输出流并实现了物化。

您需要将流数据存储在某处（例如使用DStream.saveAsTextFiles(...)以便全面检查它。

Spark自定义流式传输丢弃了大部分数据

1 个答案: