spark-streaming-kafka怎样通过KafkaUtils.createDirectStream的方式处理数据
spark-streaming-kafka怎样通过KafkaUtils.createDirectStream的方式处理数据,相信很多没有经验的人对此束手无策,为此本文总结了问题出现的原因和解决方法,通过这篇文章希望你能解决这个问题。
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package hgs.spark.streaming import org.apache.spark.SparkConf import org.apache.spark.SparkContext import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.Seconds import org.apache.spark.streaming.kafka.KafkaUtils import org.apache.spark.streaming.kafka.KafkaCluster import scala.collection.immutable.Map import java.util.NoSuchElementException import org.apache.spark.SparkException import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata import org.codehaus.jackson.map.deser.std.PrimitiveArrayDeserializers.StringDeser import kafka.serializer.StringDecoder import org.apache.spark.streaming.kafka.DirectKafkaInputDStream import org.apache.spark.rdd.RDD import org.apache.spark.streaming.kafka.HasOffsetRanges import org.apache.spark.HashPartitioner object SparkStreamingKafkaDirectWordCount { def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("KafkaWordCount").setMaster("local[5]") conf.set("spark.streaming.kafka.maxRatePerPartition", "1") val sc = new SparkContext(conf) val ssc = new StreamingContext(sc,Seconds(1)) ssc.checkpoint("d:\\checkpoint") val kafkaParams = Map[String,String]( "metadata.broker.list"->"bigdata01:9092,bigdata02:9092,bigdata03:9092", "group.id"->"group_hgs", "zookeeper.connect"->"bigdata01:2181,bigdata02:2181,bigdata03:2181") val kc = new KafkaCluster(kafkaParams) val topics = Set[String]("test") //每个rdd返回的数据是(K,V)类型的,该函数规定了函数返回数据的类型 val mmdFunct = (mmd: MessageAndMetadata[String, String])=>(mmd.topic+" "+mmd.partition,mmd.message()) val rds = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder,(String,String)](ssc, kafkaParams, getOffsets(topics,kc,kafkaParams),mmdFunct) val updateFunc=(iter:Iterator[(String,Seq[Int],Option[Int])])=>{ //iter.flatMap(it=>Some(it._2.sum+it._3.getOrElse(0)).map((it._1,_)))//方式一 //iter.flatMap{case(x,y,z)=>{Some(y.sum+z.getOrElse(0)).map((x,_))}}//方式二 iter.flatMap(it=>Some(it._1,(it._2.sum.toInt+it._3.getOrElse(0))))//方式三 } val words = rds.flatMap(x=>x._2.split(" ")).map((_,1)) //val wordscount = words.map((_,1)).updateStateByKey(updateFunc, new HashPartitioner(sc.defaultMinPartitions), true) //println(getOffsets(topics,kc,kafkaParams)) rds.foreachRDD(rdd=>{ if(!rdd.isEmpty()){ //对每个dataStreamoffset进行更新 upateOffsets(topics,kc,rdd,kafkaParams) } } ) words.print() ssc.start() ssc.awaitTermination() } def getOffsets(topics : Set[String],kc:KafkaCluster,kafkaParams:Map[String,String]):Map[TopicAndPartition, Long]={ val topicAndPartitionsOrNull = kc.getPartitions(topics) if(topicAndPartitionsOrNull.isLeft){ throw new SparkException(s"$topics in the set may not found") } else{ val topicAndPartitions = topicAndPartitionsOrNull.right.get val groups = kafkaParams.get("group.id").get val offsetOrNull = kc.getConsumerOffsets(groups, topicAndPartitions) if(offsetOrNull.isLeft){ println(s"$groups you assignment may not exists!now redirect to zero!") //如果没有消费过,则从最开始的位置消费 val erliestOffset = kc.getEarliestLeaderOffsets(topicAndPartitions) if(erliestOffset.isLeft) throw new SparkException(s"Topics and Partions not definded not found!") else erliestOffset.right.get.map(x=>(x._1,x._2.offset)) } else{ //如果消费组已经存在则从记录的地方开始消费 offsetOrNull.right.get } } } //每次拉取数据后存储offset到ZK def upateOffsets(topics : Set[String],kc:KafkaCluster,directRDD:RDD[(String,String)],kafkaParams:Map[String,String]){ val offsetRanges = directRDD.asInstanceOf[HasOffsetRanges].offsetRanges for(offr <-offsetRanges){ val topicAndPartitions = TopicAndPartition(offr.topic,offr.partition) val yesOrNo = kc.setConsumerOffsets(kafkaParams.get("group.id").get, Map(topicAndPartitions->offr.untilOffset)) if(yesOrNo.isLeft){ println(s"Error when update offset of $topicAndPartitions") } } } } /* val conf = new SparkConf().setAppName("KafkaWordCount").setMaster("local[2]") val sc = new SparkContext(conf) val ssc = new StreamingContext(sc,Seconds(4)) val kafkaParams = Map[String,String]( "metadata.broker.list"->"bigdata01:9092,bigdata02:9092,bigdata03:9092") val kc = new KafkaCluster(kafkaParams) //获取topic与paritions的信息 //val tmp = kc.getPartitions(Set[String]("test7")) //结果:topicAndPartitons=Set([test7,0], [test7,1], [test7,2]) //val topicAndPartitons = tmp.right.get //println(topicAndPartitons) //每个分区对应的leader信息 //val tmp = kc.getPartitions(Set[String]("test7")) //val topicAndPartitons = tmp.right.get //结果:leadersPerPartitions= Right(Map([test7,0] -> (bigdata03,9092), [test7,1] -> (bigdata01,9092), [test7,2] -> (bigdata02,9092))) //val leadersPerPartitions = kc.findLeaders(topicAndPartitons) //println(leadersPerPartitions) //每增加一条消息,对应的partition的offset都会加1,即LeaderOffset(bigdata02,9092,23576)第三个参数会加一 //val tmp = kc.getPartitions(Set[String]("test")) //val topicAndPartitons = tmp.right.get //结果t= Right(Map([test7,0] -> LeaderOffset(bigdata03,9092,23568), [test7,2] -> LeaderOffset(bigdata02,9092,23576), [test7,1] -> LeaderOffset(bigdata01,9092,23571))) //val t = kc.getLatestLeaderOffsets(topicAndPartitons) // println(t) //findLeader需要两个参数 topic 分区编号 //val tmp = kc.findLeader("test7",0) //结果leader=RightProjection(Right((bigdata03,9092))) //val leader = tmp.right //val tp = leader.flatMap(x=>{Either.cond(false, None,(x._1,x._2))}) val tmp = kc.getPartitions(Set[String]("test")) val ttp = tmp.right.get while(true){ try{ val tp = kc.getConsumerOffsets("group_test1", ttp) val maps = tp.right.get println(maps) Thread.sleep(2000) } catch{ case ex:NoSuchElementException=>{println("test")} } }*/
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