SparkStreaming(二)--SparkStreaming整合Kafka

1.环境

CDH 5.16.1
Spark 2.3.0.Cloudera4

2.SparkStreaming整合Kafka

地址：http://spark.apache.org/docs/2.3.0/streaming-kafka-integration.html

两种方式整合：

1. Receiver
2. Direct

3. Receiver整合

SparkStreaming采用Receiver方式整合Kafka

3.1 案例 Demo

package com.monk.sparkstreamingkafka

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.ReceiverInputDStream
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}


/**
  * @ClassName KafakReceiveWordCouont
  * @Author wuning
  * @Date: 2020/2/1 23:42
  * @Description:
  **/
object KafakReceiveWordCouont {

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

    if(args.length != 4){

      System.err.println("Usage:KafakReceiveWordCouont <zkQuorum> <group> <topics> <numThreads>")
    }


    val Array(zkQuorum,group,topics,numThreads) = args

    val sparkConf:SparkConf = new SparkConf().setMaster("local[*]").setAppName(this.getClass.getName)

    val ssc: StreamingContext = new StreamingContext(sparkConf,Seconds(10))

    ssc.sparkContext.setLogLevel("ERROR")


   val topicMap:Map[String,Int] = topics.split(",").map((_,numThreads.toInt)).toMap


    //TODO... Sparkstreaming 采用 receiver方式 对接 kafka
    val msg: ReceiverInputDStream[(String, String)] = KafkaUtils.createStream(
      ssc,
      zkQuorum,
      group,
      topicMap,
      StorageLevel.MEMORY_AND_DISK_SER_2
    )

    msg.map(_._2)
      .flatMap(_.split(" "))
      .map((_,1))
      .reduceByKey(_+_)
      .print()

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

---

KafkaUtils 的 createStream 方法：

3.2 整合服务器环境联调

/opt/cloudera/parcels/SPARK2-2.3.0.cloudera4-1.cdh5.13.3.p0.611179/bin/spark2-submit \
--class com.monk.sparkstreamingkafka.KafakReceiveWordCouont \
--master local[2] \
--name KafakReceiveWordCouont \
--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.3.0 \
/opt/module/job/spark-1.0-SNAPSHOT.jar cdh01:2181/kafka test kafka_streaming_topic 2

---

注意：

1. org.apache.spark:spark-streaming-kafka-0-8_2.11:2.3.0 依赖的jar包第一次下载会比较慢
2. 最后参数记得带上

---

Spark后台：

4. Direct 方式整合

4.1 Direct 方式的优缺点

4.2 案例 Demo

package com.monk.sparkstreamingkafka

import kafka.serializer.StringDecoder
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * @ClassName KafkaDirectWordCount
  * @Author wuning
  * @Date: 2020/2/2 23:25
  * @Description:
  **/
object KafkaDirectWordCount {

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

    if (args.length != 2) {
      System.err.println("Usage:KafkaDirectWordCount <brokers> <topics>")
      System.exit(1)
    }

    val Array(brokers, topics) = args

    val sparkconf: SparkConf = new SparkConf().setMaster("local[*]").setAppName(this.getClass.getName)

    val ssc: StreamingContext = new StreamingContext(sparkconf, Seconds(15))

    ssc.sparkContext.setLogLevel("ERROR")

    val kafkaMap: Map[String, String] = Map[String, String](
      "metadata.broker.list" -> brokers,
      "auto.offset.reset" -> "largest",
      //Kafka 的参数可以在 Kafka.clients下的ConsumerConfig 找
      ConsumerConfig.GROUP_ID_CONFIG -> "test")

    val topicSet: Set[String] = topics.split(",").toSet


    val message: InputDStream[(String, String)] = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc,
      kafkaMap,
      topicSet
    )

    message.map(_._2)
        .flatMap(_.split(""))
        .map((_,1))
        .reduceByKey(_+_)
        .print()

    ssc.start()
    ssc.awaitTermination()
  }

}

---

KafkaUtils 的 createDirectStream方法：