sparkStream-wordCount实现exactlyOne第二种--Redis

主要思路

将数据收集到driver端，利用redis的Pipeline写数据和偏移量，与第一种mysql的类似

但是redis pipline只有单机版redis或者主备redis才支持，集群版的redis不支持

package com.ws.sparkstreaming.kafkaredis

import com.ws.sparkstreaming.utils.{JedisPool, OffsetUtils}
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, HasOffsetRanges, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import redis.clients.jedis.{Jedis, Pipeline}

object WordCountJoinKafkaRedisManagerOffset {
  def main(args: Array[String]): Unit = {
    val appname = "wc_redis"
    val group = "group1"
    val topic = Array("wc1").toIterable
    val conf = new SparkConf().setAppName(appname).setMaster("local[*]")
    val ssc = new StreamingContext(conf, Seconds(5))
    ssc.sparkContext.setLogLevel("WARN")
    val kafkaParam = Map(
      "bootstrap.servers" -> "dream1:9092,dream2:9092,dream3:9092", // kafka地址
      "key.deserializer" -> "org.apache.kafka.common.serialization.StringDeserializer", // 设置反序列化组件
      "value.deserializer" -> "org.apache.kafka.common.serialization.StringDeserializer",
      "group.id" -> group, // 消费者组
      "auto.offset.reset" -> "earliest", // 指定消费者从哪开始消费[latest,earliest]
      "enable.auto.commit" -> "false" // 是否自动提交偏移量，默认是true
    )
    val lastoffset = OffsetUtils.selectOffsetFromRedis(appname, group)
    val dstream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream(
      ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](topic, kafkaParam, lastoffset))
    dstream.foreachRDD((kafkaRdd: RDD[ConsumerRecord[String, String]]) => {
      val ranges = kafkaRdd.asInstanceOf[HasOffsetRanges].offsetRanges
      println("------------------------------------" + System.currentTimeMillis() + "------------------------------------")
      if (!kafkaRdd.isEmpty()) {
        // 转换数据
        val res: RDD[(String, Int)] = kafkaRdd.map(_.value()).flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _)
        // 收集到driver端
        val wordAndNums = res.collect()
        var jedis: Jedis = null
        var pipeline: Pipeline = null
        try {
          jedis = JedisPool.getConnection()
          // 设置redis数据库
          jedis.select(1)
          // 获得pipeline
          pipeline = jedis.pipelined()
          // 开启事务
          pipeline.multi()
          wordAndNums.foreach(word => {
            pipeline.hincrBy(appname, word._1, word._2)
          })
          for (elem <- ranges) {
            pipeline.hset(appname + "_" + group, elem.topic + "_" + elem.partition, elem.untilOffset.toString)
          }
          // 提交事务
          pipeline.exec()
          pipeline.sync()
        } catch {
          case exception: Exception => {
            ssc.stop()
            // 回滚
            pipeline.discard()
            exception.printStackTrace()
          }
        } finally {
          if (pipeline != null) {
            jedis.close()
          }
          if (jedis != null) {
            jedis.close()
          }
        }
      }
    })
    ssc.start()
    ssc.awaitTermination()
  }
}

Jedis连接池 

package com.ws.sparkstreaming.utils

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

object JedisPool {
  val config = new JedisPoolConfig()
  config.setMaxTotal(20)
  config.setMaxIdle(10)
  config.setTestOnBorrow(true)
  val pool = new JedisPool(config, "dream1", 6379, 10000)
  def getConnection() = {
    pool.getResource
  }
}

 Jedis读取偏移量

package com.ws.sparkstreaming.utils

import java.sql.Connection
import java.util

import org.apache.kafka.common.TopicPartition
import org.apache.spark.streaming.kafka010.OffsetRange

import scala.collection.mutable

object OffsetUtils {

  def selectOffsetFromRedis(appname: String, goupId: String): Map[TopicPartition, Long] = {
    val jedis = JedisPool.getConnection()
    jedis.select(1)
    val kvs: util.Map[String, String] = jedis.hgetAll(appname + "_" + goupId)
    import scala.collection.JavaConversions._
    val result = new mutable.HashMap[TopicPartition, Long]()
    for (kv<-kvs){
      val k = kv._1
      val offset = kv._2
      val topic = k.split("_")(0)
      val partition = k.split("_")(1).toInt
      val tp = new TopicPartition(topic, partition)
      result(tp)= offset.toLong
    }
    result.toMap
  }
}