spark streaming 消费 kafka入门采坑解决过程

kafka 服务相关的命令

# 开启kafka的服务器
bin/kafka-server-start.sh -daemon config/server.properties &
# 创建topic
bin/kafka-topics.sh --create --zookeeper bigdata-senior02.ibeifeng.com:2181 --replication-factor 1 --partitions 1 --topic orderTopic
# 开启kafka的消费者
bin/kafka-console-consumer.sh --zookeeper bigdata-senior02.ibeifeng.com:2181 --topic orderTopic --from-beginning
# 开启kafka的生产者
bin/kafka-console-producer.sh --broker-list bigdata-senior02.ibeifeng.com:9092 --topic orderTopic

# 查看topic
bin/kafka-topics.sh --zookeeper bigdata-senior02.ibeifeng.com:2181 --list

# 标记删除kafka的topic
bin/kafka-topics.sh --delete --zookeeper bigdata-senior02.ibeifeng.com:2181 --topic orderTopic

环境准备(我使用的单机伪分布模式)

首先开启zk,再开启kafka, 并启动kafka的服务

ZK_HOME/bin/zkServer.sh start

KAFKA_HOME/bin/kafka-server-start.sh -daemon config/server.properties &

# 创建topic
bin/kafka-topics.sh --create --zookeeper bigdata-senior02.ibeifeng.com:2181 --replication-factor 1 --partitions 1 --topic orderTopic

# 查看topic
bin/kafka-topics.sh --zookeeper bigdata-senior02.ibeifeng.com:2181 --list

# 开启kafka的消费者
bin/kafka-console-consumer.sh --zookeeper bigdata-senior02.ibeifeng.com:2181 --topic orderTopic --from-beginning
# 开启kafka的生产者
bin/kafka-console-producer.sh --broker-list bigdata-senior02.ibeifeng.com:9092 --topic orderTopic

通过上面的测试,确保kafka可以正常运行

spark streaming 代码编写(scala2.11.8,spark2.0.0,kafka1.1)

maven依赖

<!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka -->
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka_2.11</artifactId>  // 这里要特别注意自己的scala版本,不然会运行时会不兼容,
    <version>1.1.0</version>
</dependency>

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>1.1.0</version>
</dependency>

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <version>1.1.0</version>
</dependency>

<!--&lt;!&ndash; https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka &ndash;&gt;-->
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming-kafka_2.11</artifactId>
    <version>1.6.3</version>
</dependency>

<!-- Spark Core -->
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.11</artifactId>
    <version>2.0.0</version>
</dependency>

<!-- Spark SQL -->
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.11</artifactId>
    <version>2.0.0</version>
</dependency>

<!-- Spark Streaming -->
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming_2.11</artifactId>
    <version>2.0.0</version>
</dependency>

生产端数据生成

/**
  * 这是一个数据生产端
  *
  * 开启服务器,broker, 如果不开启这个会提示没有找到broker
  * bin/kafka-server-start.sh -daemon config/server.properties &
  *
  * 开启消费端
  * bin/kafka-console-consumer.sh --zookeeper bigdata-senior02.ibeifeng.com:2181 --topic orderTopic --from-beginning
  */
//object OrderProductor {
//  def main(args: Array[String]): Unit = {
//
//    val topic = "orderTopic"
//    val brokers = "bigdata-senior02.ibeifeng.com:9092"
//
//    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)
//
//
//    // 每秒生成10个订单
//    while(true){
//      (1 to 10).foreach{messageNum =>
//        // 地区id, 订单id, 订单金额, 订单时间
//        val str = messageNum + "," + Random.nextInt(10)+","+Math.round(Random.nextDouble()*100)+","+ new Date().getTime
//        val message = new ProducerRecord[String, String](topic,null,str)
//        producer.send(message)
//      }
//
//      Thread.sleep(1000)
//    }
//
//  }
//
//}

// Produces some random words between 1 and 100.
object KafkaWordCountProducer {

  def main(args: Array[String]) {

    val topic = "orderTopic"
    val brokers = "bigdata-senior02.ibeifeng.com:9092"
    val messagesPerSec = 10
    val wordsPerMessage = 5

    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)

    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)
      }

      Thread.sleep(1000)
    }
  }

}

消费数据

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

/**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: KafkaWordCount <zkQuorum> <group> <topics> <numThreads>
 *   <zkQuorum> is a list of one or more zookeeper servers that make quorum
 *   <group> is the name of kafka consumer group
 *   <topics> is a list of one or more kafka topics to consume from
 *   <numThreads> is the number of threads the kafka consumer should use
 *
 * Example:
 *    `$ bin/run-example 
 *      org.apache.spark.examples.streaming.KafkaWordCount zoo01,zoo02,zoo03 
 *      my-consumer-group topic1,topic2 1`
 */
object KafkaWordCount {
  def main(args: Array[String]) {

    val zkQuorum = "bigdata-senior02.ibeifeng.com:2181"
    val group = "g1"
    val topics = "orderTopic"
    val numThreads = 2

    val conf= new SparkConf().setAppName("StatelessWordCount").setMaster("local[2]")   // 核数至少给2,否则不会完成计算
    val ssc = new StreamingContext(conf,Seconds(2))  // 两秒进行一个批次

    val topicMap = topics.split(",").map((_,numThreads.toInt)).toMap
    val wc = KafkaUtils.createStream(ssc,zkQuorum,group,topicMap).map(_._2)
        .flatMap(_.split(" "))
        .map((_,1))
        .reduceByKey(_+_)
        .foreachRDD(x=>x.foreach(println))

    ssc.start()
    ssc.awaitTermination()
  }
}

以上就是spark streaming 消费 kafka的helloworld了
===============================================================================================================================
注意点:
1. 一定要选择兼容的版本,否则会出现各种各样奇奇怪怪的问题
2. 在这里卡了将近一周的时间,都是因为上面版本不兼容和导包的时候,出现的失误
3. 如果代码没有问题,没有出现运行时异常,看看版本的兼容性入手,或许更容易找到问题
代码在我的github上,有问题请留言
https://github.com/nulijiushimeili/spark01