SparkStream：5)Spark streaming+kafka整合实战

Spark streaming+kafka整合实战

kafka的部署请参考：http://blog.csdn.net/jiangpeng59/article/details/53241693

本文主要是参加Spark提供的官网文档进行实际操作，文档地址:http://spark.apache.org/docs/1.6.0/streaming-kafka-integration.html

测试环境：Spark1.6.0 + Kafka0.9.0 + zookeeper3.4.3

1.使用maven添加spark-streaming-kafka_2.10的jar包依赖

  
        
            org.apache.spark
            spark-streaming-kafka_2.10
            1.6.0
        
    

2.使用一个测试程序往kafka的服务器输送数据

import java.util
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerConfig, ProducerRecord}

object KafkaWordCountProducer {
  def main(args: Array[String]) {
    val topic = "test"
    val brokers = "slave01:9092,slave02:9092,slave03:9092"
    val messagesPerSec=1 //每秒发送几条信息
    val wordsPerMessage =4 //一条信息包括多少个单词
    // Zookeeper connection properties
    val props = new util.HashMap[String, Object]()
    props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers)
    props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
      "org.apache.kafka.common.serialization.StringSerializer")
    props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
      "org.apache.kafka.common.serialization.StringSerializer")
    
    val producer = new KafkaProducer[String, String](props)
    // Send some messages
    while(true) {
      (1 to messagesPerSec.toInt).foreach { messageNum =>
        val str = (1 to wordsPerMessage.toInt).map(x => scala.util.Random.nextInt(10).toString)
          .mkString(" ")
        val message = new ProducerRecord[String, String](topic, null, str)
        producer.send(message)
        println(message)
      }
      Thread.sleep(1000)
    }
  }
}

简单解释下上面的代码，每秒发生1条信息到Kafka的服务器端，其消息的内容格式如下

ProducerRecord(topic=test, partition=null, key=null, value=4 6 1 1
ProducerRecord(topic=test, partition=null, key=null, value=7 1 5 0

value字段就是我们所需要的东东

3.开启kafka接受信息进程

在执行上面的代码之前，先开启一个kafka进程来接受从Produce(上面的代码)传递来的信息

bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic test --from-beginning 

执行后，其会一直监听这个是否有数据发送到名为test的topic下，有消息则会接受，并保存打印出来

[root@slave02 kafka_2.10-0.9.0.1]# bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic test --from-beginning 
8 5 6 0
5 4 0 8
9 0 9 0
5 1 7 0
7 4 4 3
2 7 6 8

4.SparkStream消费端

在官方整合文档中，也介绍目前StreamSpark从Kafka拉取数据的方式有两种，一种是基于Receiver的，一种是直接访问不依赖于Receiver的createDirectStream。官方介绍了第二种方法的几个优势，而且Spark2.0是默认使用第二种方法的。

import kafka.serializer.StringDecoder
import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka._
import org.apache.spark.SparkConf

/**
  * Consumes messages from one or more topics in Kafka and does wordcount.
  * Usage: DirectKafkaWordCount  
  *    is a list of one or more Kafka brokers
  *    is a list of one or more kafka topics to consume from
  *
  * Example:
  *    $ bin/run-example streaming.DirectKafkaWordCount broker1-host:port,broker2-host:port \
  *    topic1,topic2
  */
object DirectKafkaWordCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println(s"""
                            |Usage: DirectKafkaWordCount  
                            |   is a list of one or more Kafka brokers
                            |   is a list of one or more kafka topics to consume from
                            |
        """.stripMargin)
      System.exit(1)
    }

    val Array(brokers, topics) = args

    // Create context with 2 second batch interval
    val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(2))

    // Create direct kafka stream with brokers and topics
    val topicsSet = topics.split(",").toSet
    val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
    val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc, kafkaParams, topicsSet)

    // Get the lines, split them into words, count the words and print
    val lines = messages.map(_._2)
    val words = lines.flatMap(_.split(" "))
    val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
    wordCounts.print()

    // Start the computation
    ssc.start()
    ssc.awaitTermination()
  }
}

上面的代码，对从kafka中传递过来的word(数字)进行了简单的单词统计

把上面的文件在IDEA中打包，把spark-streaming-kafka_2.10的相关jar包整合到项目输出的jar包中，虽然文件大了些，但是省了不少麻烦

在Spark集群中提交这个jar包(先运行2、3步骤)

spark-submit --master=spark://master:7077 \
--class DirectKafkaWordCount \
./Spark1.6.0.jar \
slave01:9092,slave02:9092,slave03:9092 test

可以看到如下结果

16/11/22 18:46:56 INFO scheduler.DAGScheduler: Job 301 finished: print at DirectKafkaWordCount.scala:65, took 0.015078 s
-------------------------------------------
Time: 1479811616000 ms
-------------------------------------------
(8,1)
(9,1)
(3,1)
(1,1)