kafka直连方式消费多个topic

一个消费者组可以消费多个topic，以前写过一篇一个消费者消费一个topic的，这次的是一个消费者组通过直连方式消费多个topic,做了小测试，结果是正确的，通过查看zookeeper的客户端，zookeeper记录了偏移量

package day04

/*
消费多个topic
*/
import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import kafka.utils.{ZKGroupTopicDirs, ZkUtils}
import scala.collection.mutable.ListBuffer
import org.I0Itec.zkclient.ZkClient
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaUtils, OffsetRange}
import org.apache.spark.streaming.{Duration, StreamingContext}

object OrderDemoYY1 {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("yy").setMaster("local[*]")
val ssc = new StreamingContext(conf,Duration(5000))
//消费3个topic
val topic1 = "wc"
val topic2 ="wc1"
val topic3 ="wc2"
//组名
val groupid ="GPMMVV"
//zookeeper地址
val zkQuorum = "hadoop01:2181,hadoop02:2181,hadoop03:2181"
//brokerList
val brokerList = "hadoop01:9092,hadoop02:9092,hadoop03:9092"
//把消费的分区放到Set集合中，可以在第一次读取时作为参数传入
val topics = Set(topic1,topic2,topic3)
//ListBuffer时有序的，按下标有序
val topicsList = ListBuffer[String](topic1,topic2,topic3)
//设置kafka的参数
val kafkaParams = Map(
"metadata.broker.list"->brokerList,
"groupid"->groupid,
"auto.offset.reset"->kafka.api.OffsetRequest.SmallestTimeString
//默认时从头开始读的
)

//new ListBuffer用来存放ZKGroupTopicDirs, 用来保存偏移量的地址
//因为有多个topic,对应的也就有多个ZKGroupTopicDirs
var zkGTList:ListBuffer[ZKGroupTopicDirs] =new ListBuffer[ZKGroupTopicDirs]()
//根据topicList 新建 ZKGroupTopicDirs 添加到zkGTList
for(tp <- topicsList){
val topicDirs = new ZKGroupTopicDirs(groupid,tp)
zkGTList += topicDirs
}
//新建zkClient，用来获取偏移量和更新偏移量
val zkClient = new ZkClient(zkQuorum)
//新建一个InputDStream,要是var,因为有两种情况，消费过？ 没有消费过？ 根据情况赋值
var kafkaDStream :InputDStream[(String,String)] = null
//创建一个Map，（key，value）-》( 对应的时Topic和分区 ，偏移量)
var fromOffset = Map[TopicAndPartition,Long]()

//获取每个topic是否被消费过
var childrens:ListBuffer[Int] =new ListBuffer[Int]()
var flag = false //有topic被消费过则为true
for (topicDir <- zkGTList){ //循环存放偏移量的
//通过zkClient.countChidren来获取每个topic对应的分区中的偏移量ZKGroupTopicDirs的对象
val child: Int = zkClient.countChildren(topicDir.consumerOffsetDir)
childrens +www.mhylpt.com= child
if(child>0){
flag = true
}
}

if(flag){//消费过
for(z <- 0 until topics.size){ //根据topicsList的的下表获取相应的child和ZKGroupTopicDirs
val child = childrens(z)
val gpDirs = zkGTList(z)
val topicn = topicsList(z)
for(i <- 0 until child)www.mcyllpt.com/{
//循环child， 根据使用zkClient.readData方法，u获取topic的每个分区的偏移量
val offset = zkClient.readData[String](gpDirs.consumerOffsetDir+"/"+i)
val tp = new TopicAndPartition(www.michenggw.com/ topicn,i)
fromOffset += tp -> offset.toLong
}
}
//返回的而结果是 kafka的key,默认是null, value是kafka中的值
val messageHandler =www.gcyl159.com/ (mmd:MessageAndMetadata[String,String])=www.gcyl152.com>{
(mmd.key(),mmd.message())
}
//创建kafkaDStream
kafkaDStream = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder,(String,String)](
ssc,kafkaParams,fromOffset,messageHandler
)
}else{//以前没有读取过
kafkaDStream = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](
ssc,kafkaParams,topics
)
}

/*val children1 = zkClient.countChildren(zKGroupTopicDirs1.consumerOffsetDir)
val children2 = zkClient.countChildren(zKGroupTopicDirs2.consumerOffsetDir)
if(children1>0 || children2>0){
if(children1>0){
for (i <- 0 until children1){
val offset = zkClient.readData[String](zKGroupTopicDirs1.consumerOffsetDir+"/"+i)
val tp = new TopicAndPartition(topic1,i)
fromOffset += tp ->offset.toLong
}
}
if(children2>0){
for (i <- 0 until children1){
val offset = zkClient.readData[String](zKGroupTopicDirs2.consumerOffsetDir+"/"+i)
val tp = new TopicAndPartition(topic2,i)
fromOffset += tp ->offset.toLong
}
}
val messageHandler =(mmd:MessageAndMetadata[String,String])=>{
(mmd.key(),mmd.message())
}
kafkaDStream = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder,(String,String)](ssc,
kafkaParams,fromOffset,messageHandler)
}else{
kafkaDStream = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](ssc,kafkaParams,topics)
}*/

var offsetRanges = Array[OffsetRange]www.hjpt521.com() //用来记录更新的每个topic的分区偏移量

kafkaDStream.foreachRDD(kafkaRDD=>{
//kafkaRDD是一个KafkaRDD,可以转换成HasOffsetRanges对象，从而获取offsetRanges
offsetRanges= kafkaRDD.asInstanceOf[HasOffsetRanges].offsetRanges
kafkaRDD.foreach(println)www.365soke.com //打印

for(o <- offsetRanges){
val topicNN: String = o.topic //获取topic
val offset: Long = o.untilOffset //获取偏移量
val partition: Int = o.partition //获取分区
val i = topicsList.indexOf(topicNN) //通过topicList查找topic的下标，找到与之对应的ZKGroupTopicDirs
val gpDir = zkGTList(i)
//通过ZkUtils更新偏移量
ZkUtils.updatePersistentPath(zkClient,gpDir.consumerOffsetDir+"/"+partition,offset.toString)
/*if(topicNN.equals(topic1)){
ZkUtils.updatePersistentPath(zkClient,zKGroupTopicDirs1.consumerOffsetDir+"/"+partition,offset.toString)
}else if(topicNN.equals(topic2)){
ZkUtils.updatePersistentPath(zkClient,zKGroupTopicDirs2.consumerOffsetDir+"/"+partition,offset.toString)
}*/
}
})

ssc.start()
ssc.awaitTermination(www.dfgjyl.cn)

可以通过zookeeper的客户端，在/consumers中查看偏移量，
我的3个topic中，其中wc和wc1只有1个分区，可以通过下图可看出wc1的0分区偏移量13