Spark-Streaming结合Redis

1、JedisConnectionPool.scala

package sparkstreaming


import redis.clients.jedis.{Jedis, JedisPool, JedisPoolConfig}


object JedisConnectionPool{

  val config = new JedisPoolConfig()
  //最大连接数,
  config.setMaxTotal(20)
  //最大空闲连接数
  config.setMaxIdle(10)
  //当调用borrow Object方法时，是否进行有效性检查 -->
  config.setTestOnBorrow(true)
  //10000代表超时时间（10秒）
  val pool = new JedisPool(config, "127.0.0.1", 6379)

  def getConnection(): Jedis = {
    pool.getResource
  }

  def main(args: Array[String]) {


    val conn = JedisConnectionPool.getConnection()
//    conn.set("income", "1000")
//
//    val r1 = conn.get("xiaoyang")
//
//    println(r1)
//
//    conn.incrBy("xiaoyang", -50)
//
//    val r2 = conn.get("xiaoyang")
//
//    println(r2)
//
//    conn.close()

    val r = conn.keys("*")
    import scala.collection.JavaConversions._
    for (p <- r) {
      println(p + " : " + conn.get(p))
    }
  }

}

2、KafkaDirectWordCountV2.scala

package sparkstreaming

import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import kafka.utils.{ZKGroupTopicDirs, ZkUtils}
import org.I0Itec.zkclient.ZkClient
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaUtils, OffsetRange}
import org.apache.spark.streaming.{Duration, StreamingContext}
import redis.clients.jedis.Jedis


object KafkaDirectWordCountV2 {
  
  def main(args: Array[String]): Unit = {

    //指定组名
    val group = "g001"
    //创建SparkConf
    val conf = new SparkConf().setAppName("KafkaDirectWordCount").setMaster("local[2]")
    //创建SparkStreaming，并设置间隔时间
    val ssc = new StreamingContext(conf, Duration(5000))
    //指定消费的 topic 名字
    val topic = "wc"
    //指定kafka的broker地址(sparkStream的Task直连到kafka的分区上，用更加底层的API消费，效率更高)
    val brokerList = "localhost:9092"

    //指定zk的地址，后期更新消费的偏移量时使用(以后可以使用Redis、MySQL来记录偏移量)
    val zkQuorum = "localhost:2181"
    //创建 stream 时使用的 topic 名字集合，SparkStreaming可同时消费多个topic
    val topics: Set[String] = Set(topic)

    //创建一个 ZKGroupTopicDirs 对象,其实是指定往zk中写入数据的目录，用于保存偏移量
    val topicDirs = new ZKGroupTopicDirs(group, topic)
    //获取 zookeeper 中的路径 "/g001/offsets/wc/"
    val zkTopicPath = s"${topicDirs.consumerOffsetDir}"

    //准备kafka的参数
    val kafkaParams = Map(
      "metadata.broker.list" -> brokerList,
      "group.id" -> group,
      //从头开始读取数据
      "auto.offset.reset" -> kafka.api.OffsetRequest.SmallestTimeString
    )

    //zookeeper 的host 和 ip，创建一个 client,用于跟新偏移量量的
    //是zookeeper的客户端，可以从zk中读取偏移量数据，并更新偏移量
    val zkClient = new ZkClient(zkQuorum)

    //查询该路径下是否字节点（默认有字节点为我们自己保存不同 partition 时生成的）
    // /g001/offsets/wordcount/0/10001"
    // /g001/offsets/wordcount/1/30001"
    // /g001/offsets/wordcount/2/10001"
    //zkTopicPath  -> /g001/offsets/wordcount/
    val children = zkClient.countChildren(zkTopicPath)

    var kafkaStream: InputDStream[(String, String)] = null

    //如果 zookeeper 中有保存 offset，我们会利用这个 offset 作为 kafkaStream 的起始位置
    var fromOffsets: Map[TopicAndPartition, Long] = Map()

    //如果保存过 offset
    //注意：偏移量的查询是在Driver完成的
    if (children > 0) {
      for (i <- 0 until children) {
        // /g001/offsets/wordcount/0/10001

        // /g001/offsets/wordcount/0
        println(s"路径:${zkTopicPath}")
        val partitionOffset = zkClient.readData[String](s"$zkTopicPath/${i}")
        // wordcount/0
        val tp = TopicAndPartition(topic, i)
        //将不同 partition 对应的 offset 增加到 fromOffsets 中
        // wordcount/0 -> 10001
        fromOffsets += (tp -> partitionOffset.toLong)
      }
      //Key: kafka的key   values: "hello tom hello jerry"
      //这个会将 kafka 的消息进行 transform，最终 kafak 的数据都会变成 (kafka的key, message) 这样的 tuple
      val messageHandler = (mmd: MessageAndMetadata[String, String]) => (mmd.key(), mmd.message())

      //通过KafkaUtils创建直连的DStream（fromOffsets参数的作用是:按照前面计算好了的偏移量继续消费数据）
      //[String, String, StringDecoder, StringDecoder,     (String, String)]
      //  key    value    key的解码方式   value的解码方式
      kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, (String, String)](ssc, kafkaParams, fromOffsets, messageHandler)
    } else {
      //如果未保存，根据 kafkaParam 的配置使用最新(largest)或者最旧的（smallest） offset
      kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
    }

    //偏移量的范围
    var offsetRanges = Array[OffsetRange]()

    //如果你调用了DStream的Transformation，就不能使用直连方式
    //    val ds = kafkaStream.map(_._2).flatMap(_.split(" ")).map((_, 1))
    //    ds.foreachRDD(rdd => {
    //      //当前的这个RDD已经不是KafkaRDD了，就不能获取到从kafka中读取的偏移量
    //      offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
    //    })
    //

    //直连方式只有在KafkaDStream的RDD（KafkaRDD）中才能获取偏移量，那么就不能到调用DStream的Transformation
    //所以只能子在kafkaStream调用foreachRDD，获取RDD的偏移量，然后就是对RDD进行操作了
    //依次迭代KafkaDStream中的KafkaRDD
    //如果使用直连方式累加数据，那么就要在外部的数据库中进行累加（用KeyVlaue的内存数据库（NoSQL），Redis）
    kafkaStream.foreachRDD { kafkaRDD =>
      //只有KafkaRDD可以强转成HasOffsetRanges，并获取到偏移量
      offsetRanges = kafkaRDD.asInstanceOf[HasOffsetRanges].offsetRanges
      //获取数据
      val lines: RDD[String] = kafkaRDD.map(_._2)

      //对RDD进行操作，触发Action
      lines.foreachPartition(partition =>
        partition.foreach(x => {
          //写业务逻辑 ---来一个wc吧
          println(x)
          val tuples: Array[(String, Int)] = x.split(" ").map((_, 1))
          val grouped: Map[String, Array[(String, Int)]] = tuples.groupBy(_._1)
          val wordAndCount: Map[String, Int] = grouped.mapValues(_.length)
          val sorted: List[(String, Int)] = wordAndCount.toList.sortBy(- _._2)


          /**写入redis*/
          val conn: Jedis = JedisConnectionPool.getConnection()
          for (i <- 0 until sorted.size){
            println(sorted(i))
            conn.incrBy(sorted(i)._1,sorted(i)._2)
          }
          conn.close()

        })
      )

      for (o <- offsetRanges) {
        //  /g001/offsets/wordcount/0
        val zkPath = s"${topicDirs.consumerOffsetDir}/${o.partition}"
        //将该 partition 的 offset 保存到 zookeeper
        //  /g001/offsets/wordcount/0/20000
        println(s"保存的路径为:${zkPath},保存的偏移量为:${o.untilOffset.toString}")
        ZkUtils.updatePersistentPath(zkClient, zkPath, o.untilOffset.toString)
      }
    }

    ssc.start()
    ssc.awaitTermination()

  }


}

3、pom.xml文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.yangwj</groupId>
    <artifactId>spark</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <scala.version>2.11.8</scala.version>
        <spark.version>2.2.0</spark.version>
        <encoding>UTF-8</encoding>


        <java.version>1.8</java.version>
        <hadoop.version>2.7.7</hadoop.version>
        <hbase.version>2.0.5</hbase.version>
        <spring-data-hadoop.version>2.4.0</spring-data-hadoop.version>
    </properties>
    <dependencies>
        <!--        ml库-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!--        spark-streaming-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!--        ml库-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.codehaus.janino</groupId>
            <artifactId>janino</artifactId>
            <version>3.0.8</version>
        </dependency>
        <!-- 导入scala的依赖 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

        <!-- 导入spark的依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!-- 导入sparksql的依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/redis.clients/jedis -->
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.9.0</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.12</version>
        </dependency>

    </dependencies>
</project>