Spark-Streaming处理Kafka数据——封装成对象处理

Spark-Streaming处理Kafka数据——封装成对象方式

1、Spark-Streaming处理Kafka数据格式基本上都是字符串类型的，我们如何封装成对象处理呢

首先准备数据，kafka的数据，这是Person的信息，包括id,name,age的信息：

1 billy 14
2 lily 16
3 lilei 17
4 lucy 13
5 green 11
6 jim 17
7 tom 15

2、通过Kakfa来处理，Kafa传过来的Person信息

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import kafka.serializer.StringDecoder
import scala.collection.immutable.HashMap
import org.apache.log4j.Level
import org.apache.log4j.Logger
/**
 * @author Administrator
 */
object KafkaDataTest2 {

  //封装case clase , 需放在main之前声明
  case class Person(id: Long, name: String, age: Int)

  def main(args: Array[String]): Unit = {

    Logger.getLogger("org.apache.spark").setLevel(Level.WARN);
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.ERROR);

    val conf = new SparkConf().setAppName("KafkaDataTest2").setMaster("local[3]")
    val sc = new SparkContext(conf)

    val ssc = new StreamingContext(sc, Seconds(3))


    val topics = Set("MyTopic")

    val brokers = "spark1:9092,spark2:9092,spark3:9092"

    val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers, "serializer.class" -> "kafka.serializer.StringEncoder")

    //  接收到自kafka的数据
    val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)

    val dStream = kafkaStream.map(_._2)

    //按 " " split后，映射成Person对象
    val personDStream = dStream.map(_.split(" ")).filter(_.length >= 3).map(x => Person(x(0).toLong, x(1).toString, x(2).toInt))

    val sqlContext = new org.apache.spark.sql.SQLContext(sc)

    //通过foreachRDD来触发DStream的Action
    personDStream.foreachRDD(rdd => {
      import sqlContext.implicits._
      
      //创建临时表，这个可以与Person名一致，也可以不致
      val df = rdd.toDF().registerTempTable("Person")

      //可通过sql进行过滤或者统计处理，字段名与Person设置的一致，通过Person类的字段反射来指定的
      val rcount = sqlContext.sql("select id,name,age from Person where age>=15")

      //处理sql的RDD需通过foreachPartition来处理
      rcount.foreachPartition(iterator =>
        {
          iterator.foreach(data => {
            val id = data.get(0)
            val name = data.get(1)
            val age = data.get(2)
            println("Person.id=" + id + ", Person.name=" + name + ", Person.age=" + age)
            
          })

        })

    })

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

3、通过定义case class来定义Person类，处理kafka数据时，映射成Person对象，最后用sql的字段来对应到Person的属性，最终返回处理的结果如下：

Person.id=2, Person.name=lily, Person.age=16
Person.id=3, Person.name=lilei, Person.age=17
Person.id=6, Person.name=jim, Person.age=17
Person.id=7, Person.name=tom, Person.age=15

与预期的结果一样