8.输入DStream之Kafka数据源实战（基于Receiver的方式）

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基于Receiver的方式

这种方式使用Receiver来获取数据。Receiver是使用Kafka的高层次Consumer API来实现的。receiver从Kafka中获取的数据都是存储在Spark Executor的内存中的，然后Spark Streaming启动的job会去处理那些数据。

然而，在默认的配置下，这种方式可能会因为底层的失败而丢失数据。如果要启用高可靠机制，让数据零丢失，就必须启用Spark Streaming的预写日志机制（Write Ahead Log，WAL）。该机制会同步地将接收到的Kafka数据写入分布式文件系统（比如HDFS）上的预写日志中。所以，即使底层节点出现了失败，也可以使用预写日志中的数据进行恢复。

如何进行Kafka数据源连接

1、在maven添加依赖

groupId = org.apache.spark
artifactId = spark-streaming-kafka_2.10
version = 1.5.1

2、使用第三方工具类创建输入DStream

 JavaPairReceiverInputDStream<String, String> kafkaStream =
     KafkaUtils.createStream(streamingContext,
     [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]);

需要注意的要点 

  1. Kafka中的topic的partition，与Spark中的RDD的partition是没有关系的。所以，在KafkaUtils.createStream()中，提高partition的数量，只会增加一个Receiver中，读取partition的线程的数量。不会增加Spark处理数据的并行度。
  2. 可以创建多个Kafka输入DStream，使用不同的consumer group和topic，来通过多个receiver并行接收数据。
  3. 如果基于容错的文件系统，比如HDFS，启用了预写日志机制，接收到的数据都会被复制一份到预写日志中。因此，在KafkaUtils.createStream()中，设置的持久化级别是StorageLevel.MEMORY_AND_DISK_SER。

Kafka命令(进入到cd  /usr/local/kafka)

// 创建topic

bin/kafka-topics.sh --zookeeper 172.20.10.117:2181,172.20.10.118:2181,172.20.10.119:2181 --topic MyTestWordCount --replication-factor 1 --partitions 1 --create

// 创建consumer

bin/kafka-console-producer.sh --broker-list 172.20.10.117:9092, 172.20.10.118:9092, 172.20.10.119:9092 --topic MyTestWordCount

java版本代码：

  1.  class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="1">
class="hljs-ln-code"> class="hljs-ln-line">package cn.spark.study.streaming;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="2">
    • class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="3"> class="hljs-ln-code"> class="hljs-ln-line">import java.util.Arrays;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="4"> class="hljs-ln-code"> class="hljs-ln-line">import java.util.HashMap;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="5"> class="hljs-ln-code"> class="hljs-ln-line">import java.util.Map;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="6"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="7"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.SparkConf;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="8"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.api.java.function.FlatMapFunction;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="9"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.api.java.function.Function2;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="10"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.api.java.function.PairFunction;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="11"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.streaming.Durations;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="12"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.streaming.api.java.JavaDStream;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="13"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.streaming.api.java.JavaPairDStream;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="14"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="15"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.streaming.api.java.JavaStreamingContext;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="16"> class="hljs-ln-code"> class="hljs-ln-line">import org.apache.spark.streaming.kafka.KafkaUtils;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="17"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="18"> class="hljs-ln-code"> class="hljs-ln-line">import scala.Tuple2;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="19"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="20"> class="hljs-ln-code"> class="hljs-ln-line">/**
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="21"> class="hljs-ln-code"> class="hljs-ln-line"> * 基于Kafka receiver方式的实时wordcount程序
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="22"> class="hljs-ln-code"> class="hljs-ln-line"> * @author Administrator
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="23"> class="hljs-ln-code"> class="hljs-ln-line"> *
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="24"> class="hljs-ln-code"> class="hljs-ln-line"> */
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="25"> class="hljs-ln-code"> class="hljs-ln-line">public class KafkaReceiverWordCount {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="26"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="27"> class="hljs-ln-code"> class="hljs-ln-line"> public static void main(String[] args) {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="28"> class="hljs-ln-code"> class="hljs-ln-line"> SparkConf conf = new SparkConf()
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="29"> class="hljs-ln-code"> class="hljs-ln-line"> .setMaster("local[2]")
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="30"> class="hljs-ln-code"> class="hljs-ln-line"> .setAppName("KafkaWordCount");
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="31"> class="hljs-ln-code"> class="hljs-ln-line"> JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="32"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="33"> class="hljs-ln-code"> class="hljs-ln-line"> // 使用KafkaUtils.createStream()方法，创建针对Kafka的输入数据流
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="34"> class="hljs-ln-code"> class="hljs-ln-line"> Map topicThreadMap = new HashMap();
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="35"> class="hljs-ln-code"> class="hljs-ln-line"> topicThreadMap.put("MyTestWordCount", 1);
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="36"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="37"> class="hljs-ln-code"> class="hljs-ln-line"> JavaPairReceiverInputDStream lines = KafkaUtils.createStream(
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="38"> class="hljs-ln-code"> class="hljs-ln-line"> jssc,
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="39"> class="hljs-ln-code"> class="hljs-ln-line"> "172.20.10.117:2181,172.20.10.118:2181,172.20.10.119:2181",
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="40"> class="hljs-ln-code"> class="hljs-ln-line"> "DefaultConsumerGroup",
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="41"> class="hljs-ln-code"> class="hljs-ln-line"> topicThreadMap);
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="42"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="43"> class="hljs-ln-code"> class="hljs-ln-line"> // 然后开发wordcount逻辑
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="44"> class="hljs-ln-code"> class="hljs-ln-line"> JavaDStream words = lines.flatMap(
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="45"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="46"> class="hljs-ln-code"> class="hljs-ln-line"> new FlatMapFunction, String>() {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="47"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="48"> class="hljs-ln-code"> class="hljs-ln-line"> private static final long serialVersionUID = 1L;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="49"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="50"> class="hljs-ln-code"> class="hljs-ln-line"> @Override
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="51"> class="hljs-ln-code"> class="hljs-ln-line"> public Iterable call(Tuple2 tuple)
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="52"> class="hljs-ln-code"> class="hljs-ln-line"> throws Exception {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="53"> class="hljs-ln-code"> class="hljs-ln-line"> return Arrays.asList(tuple._2.split(" "));
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="54"> class="hljs-ln-code"> class="hljs-ln-line"> }
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="55"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="56"> class="hljs-ln-code"> class="hljs-ln-line"> });
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="57"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="58"> class="hljs-ln-code"> class="hljs-ln-line"> JavaPairDStream pairs = words.mapToPair(
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="59"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="60"> class="hljs-ln-code"> class="hljs-ln-line"> new PairFunction() {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="61"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="62"> class="hljs-ln-code"> class="hljs-ln-line"> private static final long serialVersionUID = 1L;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="63"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="64"> class="hljs-ln-code"> class="hljs-ln-line"> @Override
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="65"> class="hljs-ln-code"> class="hljs-ln-line"> public Tuple2 call(String word)
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="66"> class="hljs-ln-code"> class="hljs-ln-line"> throws Exception {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="67"> class="hljs-ln-code"> class="hljs-ln-line"> return new Tuple2(word, 1);
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="68"> class="hljs-ln-code"> class="hljs-ln-line"> }
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="69"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="70"> class="hljs-ln-code"> class="hljs-ln-line"> });
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="71"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="72"> class="hljs-ln-code"> class="hljs-ln-line"> JavaPairDStream wordCounts = pairs.reduceByKey(
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="73"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="74"> class="hljs-ln-code"> class="hljs-ln-line"> new Function2() {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="75"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="76"> class="hljs-ln-code"> class="hljs-ln-line"> private static final long serialVersionUID = 1L;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="77"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="78"> class="hljs-ln-code"> class="hljs-ln-line"> @Override
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="79"> class="hljs-ln-code"> class="hljs-ln-line"> public Integer call(Integer v1, Integer v2) throws Exception {
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="80"> class="hljs-ln-code"> class="hljs-ln-line"> return v1 + v2;
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="81"> class="hljs-ln-code"> class="hljs-ln-line"> }
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="82"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="83"> class="hljs-ln-code"> class="hljs-ln-line"> });
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="84"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="85"> class="hljs-ln-code"> class="hljs-ln-line"> wordCounts.print();
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="86"> class="hljs-ln-code"> class="hljs-ln-line">
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="87"> class="hljs-ln-code"> class="hljs-ln-line"> jssc.start();
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="88"> class="hljs-ln-code"> class="hljs-ln-line"> jssc.awaitTermination();
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="89"> class="hljs-ln-code"> class="hljs-ln-line"> jssc.close();
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="90"> class="hljs-ln-code"> class="hljs-ln-line"> }
  • class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="91"> class="hljs-ln-code"> class="hljs-ln-line">}
    •  class="hide-preCode-box"> class="hljs-button signin active" data-title="登录复制" data-report-click="{"spm":"1001.2101.3001.4334"}" onclick="hljs.signin(event)">

    运行步骤及运行结果(java本地运行)：

     

     

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