sparkstreaming同时消费多个topic的数据实现exactly-once的语义

最近很多人问我,sparkstreaming怎么消费多个topic的数据,自己维护offest,其实这个跟消费一个topic是一样的,但还是有很多问我,今天就简单的写一个demo,供大家参考,直接上代码吧,已经测试过了.我把offest存到redis里了,当然也可以保存在zk,kafka,mysql,hbase中都可以,看自己的选择.(用了3个topic,每个topic5个partition.)

package spark

import java.io.File
import kafka.{PropertiesScalaUtils, RedisKeysListUtils}
import kafka.streamingRedisHive.{dbIndex}
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.TaskContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010._
import redis.RedisPool

object moreTopic {
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.INFO)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.INFO)
    Logger.getLogger("org.apache.kafka.clients.consumer").setLevel(Level.INFO)
    val warehouseLocation = new File("hdfs://cluster/hive/warehouse").getAbsolutePath
    val spark = SparkSession.builder().appName("Spark Jason").config("spark.sql.warehouse.dir", warehouseLocation).enableHiveSupport().getOrCreate()
    spark.conf.set("spark.streaming.concurrentJobs", 10)
    spark.conf.set("spark.streaming.kafka.maxRetries", 50)
    spark.conf.set("spark.streaming.stopGracefullyOnShutdown",true)
    spark.conf.set("spark.streaming.backpressure.enabled",true)
    spark.conf.set("spark.streaming.backpressure.initialRate",5000)
    spark.conf.set("spark.streaming.kafka.maxRatePerPartition", 3000)
    @transient
    val sc = spark.sparkContext
    val scc = new StreamingContext(sc, Seconds(2))
    val kafkaParams = Map[String, Object](
      "auto.offset.reset" -> "latest",
      "value.deserializer" -> classOf[StringDeserializer]
      , "key.deserializer" -> classOf[StringDeserializer]
      , "bootstrap.servers" -> PropertiesScalaUtils.loadProperties("broker")
      , "group.id" -> PropertiesScalaUtils.loadProperties("groupId")
      , "enable.auto.commit" -> (false: java.lang.Boolean)
    )
    var stream: InputDStream[ConsumerRecord[String, String]] = null
    val topics = Array("jason_20180519", "jason_0606","jason_test")
    val maxTotal = 200
    val maxIdle = 100
    val minIdle = 10
    val testOnBorrow = false
    val testOnReturn = false
    val maxWaitMillis = 5000
    RedisPool.makePool(PropertiesScalaUtils.loadProperties("redisHost"), PropertiesScalaUtils.loadProperties("redisPort").toInt, maxTotal, maxIdle, minIdle, testOnBorrow, testOnReturn, maxWaitMillis)
    val jedis = RedisPool.getPool.getResource
    jedis.select(dbIndex)
    val keys = jedis.keys(topics(0) + "*")
    val keys_2 = jedis.keys(topics(1) +"*")
    val keys_3 = jedis.keys(topics(2) +"*")
    if(keys.size() == 0 && keys_2.size() == 0 && keys_3.size() == 0){
      println("第一次启动,从头开始消费数据-----------------------------------------------------------")
      stream = KafkaUtils.createDirectStream[String, String](
        scc,
        LocationStrategies.PreferConsistent,
        ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
      )
    }else{
      println("不是第一次启动,从上次的offest开始消费数据-----------------------------------------------")
      stream = KafkaUtils.createDirectStream[String, String](
        scc,
        LocationStrategies.PreferConsistent,
        ConsumerStrategies.Subscribe[String, String](topics, kafkaParams, RedisKeysListUtils.getRedisOffest(topics,jedis)))
    }
    jedis.close()
    stream.foreachRDD(rdd=>{
      val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
      rdd.foreachPartition(partition=>{
        val o = offsetRanges(TaskContext.get.partitionId)
        println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
        val jedis_jason = RedisPool.getPool.getResource
        jedis_jason.select(dbIndex)
        partition.foreach(pair=>{
          //自己的计算逻辑;
        })
        offsetRanges.foreach { offsetRange =>
          println("partition : " + offsetRange.partition + " fromOffset:  " + offsetRange.fromOffset + " untilOffset: " + offsetRange.untilOffset)
          val topic_partition_key_new = offsetRange.topic + "_" + offsetRange.partition
          jedis_jason.set(topic_partition_key_new, offsetRange.untilOffset + "")
        }
        jedis_jason.close()
      })
    })
    scc.start()
    scc.awaitTermination()
  }
}

如果有写的不对的地方,欢迎大家指正,如果有什么疑问,可以加QQ群:340297350,谢谢