SparkStreaming整合kafka

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项目架构
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日志数据---->flume----->kafka-------->spark streaming---------->mysql/redis/hbase


前置条件

  • 安装zookeeper
  • 安装flume
  • 安装kafak
  • hadoop实现高可用

(1)实现flume收集数据到kafka

启动kafak:
nohup kafka-server-start.sh /application/kafka_2.11-1.1.0/config/server.properties 1>/home/hadoop/logs/kafka_std.log 2>/home/hadoop/logs/kafka_err.log &
创建一个没有的kafaktopic:
kafka-topics.sh --create --zookeeper hadoop01:2181,hadoop02:2181,hadoop03:2181/kafka --replication-factor 3 --partitions 3 --topic zy-flume-kafka
查看是否创建成功:
kafka-topics.sh --zookeeper hadoop01:2181,hadoop02:2181,hadoop03:2181/kafka --describe --topic zy-flume-kafka

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配置flume的采集方案
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第一级:exec-avro.conf

agent1.sources = r1
agent1.channels = c1
agent1.sinks = k1
#define sources
agent1.sources.r1.type = exec
agent1.sources.r1.command = tail -F /application/flume-1.8.0-bin/data/sample.log
#define channels
agent1.channels.c1.type = memory
agent1.channels.c1.capacity = 1000
agent1.channels.c1.transactionCapacity = 100
#define sink
agent1.sinks.k1.type = avro
agent1.sinks.k1.hostname = hadoop02
agent1.sinks.k1.port = 3212
#bind sources and sink to channel
agent1.sources.r1.channels = c1
agent1.sinks.k1.channel = c1

第二级:avro-kafka.conf

agent2.sources = r2
agent2.channels = c2
agent2.sinks = k2
#define sources
agent2.sources.r2.type = avro
agent2.sources.r2.bind = hadoop02
agent2.sources.r2.port = 3212
#define channels
agent2.channels.c2.type = memory
agent2.channels.c2.capacity = 1000
agent2.channels.c2.transactionCapacity = 100
#define sink
agent2.sinks.k2.type = org.apache.flume.sink.kafka.KafkaSink
agent2.sinks.k2.brokerList = hadoop01:9092,hadoop02:9092,hadoop03:9092
agent2.sinks.k2.topic = zy-flume-kafka
agent2.sinks.k2.batchSize = 4
agent2.sinks.k2.requiredAcks = 1
#bind sources and sink to channel
agent2.sources.r2.channels = c2
agent2.sinks.k2.channel = c2

启动flume
hadoop02:

flume-ng agent --conf /application/flume-1.8.0-bin/conf/ --name agent2 --conf-file /application/flume-1.8.0-bin/flume_sh/avro-kafka.conf -Dflume.root.logger=DEBUG,console

hadoop01:

flume-ng agent --conf /application/flume-1.8.0-bin/conf/ --name agent1 --conf-file /application/flume-1.8.0-bin/flume_sh/exec-avro.conf -Dflume.root.logger=DEBUG,console

注意:一定要先启动第二级在启动第一级


测试
启动一个kafakconsumer

kafka-console-consumer.sh --bootstrap-server hadoop01:9092,hadoop02:9092,hadoop03:9092 --from-beginning --topic zy-flume-kafka

向监控文件下添加数据:tail -10 sample.temp>>sample.log
观察kafkaconsumer:消费到数据!!
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(2)实现sparkStreaming读取kafka中数据并处理

 SparkStreaming整合kafka有两种方式:
   - receiver +checkpoint方式
   - direct +zookeeper方式

1)receiver +checkpoint方式

代码

/**
  * 基于Receiver的方式去读取kafka中的数据
  */
object _01SparkKafkaReceiverOps {
    def main(args: Array[String]): Unit = {
        //判断程序传入的参数个数是否正确
        //2 hadoop01:2181,hadoop02:2181,hadoop03:2181/kafka first zy-flume-kafka
        if (args == null || args.length < 4) {
            println(
                """
                  |Parameter Errors! Usage: <batchInterval> <zkQuorum> <groupId> <topics>
                  |batchInterval        : 批次间隔时间
                  |zkQuorum             : zookeeper url地址
                  |groupId              : 消费组的id
                  |topic                : 读取的topic
                """.stripMargin)
            System.exit(-1)
        }
        //获取程序传入的参数
        val Array(batchInterval, zkQuorum, groupId, topic) = args
        //1.构建程序入口
        val conf: SparkConf = new SparkConf()
            .setMaster("local[2]")
            .setAppName("_01SparkKafkaReceiverOps")
        val ssc =new StreamingContext(conf,Seconds(2))
        /**2.使用Receiver方式读取数据
          * @param ssc
          * @param zkQuorum
          * @param groupId
          * @param topics
          * @param storageLevel  default: StorageLevel.MEMORY_AND_DISK_SER_2
          * @return DStream of (Kafka message key, Kafka message value)
          */
        val topics = topic.split("\s+").map((_,3)).toMap
        //2.读取数据
        val message: ReceiverInputDStream[(String, String)] = KafkaUtils.createStream(ssc,zkQuorum,groupId,topics)
        //3.打印数据
        message.print()
        //4.提交任务
        ssc.start()
        ssc.awaitTermination()
    }
}

注意(receiver +checkpoint):
 - kafka中的topic和sparkstreaming中生成的RDD分区没有关系,在KafkaUtils.createStream中增加分区数只会增加单个receiver的线程数,不会增加spark的并行度
 - 可以创建多个kafka的输入DStream,使用不同的group和topic,使用多个receiver并行接收数据
 - 如果启用了HDFS等有容错的存储系统,并且启用了写入日,则接收到的数据已经被复制到日志中。

2)direct +zookeeper方式

代码实现

package com.zy.streaming

import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import org.apache.curator.framework.{CuratorFramework, CuratorFrameworkFactory}
import org.apache.curator.retry.ExponentialBackoffRetry
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaUtils, OffsetRange}
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * 使用zk来管理的消费的偏移量,确保当SparkStreaming挂掉之后在重启的时候,
  * 能够从正确的offset偏移量的位置开始消费,而不是从头开始消费
  */
object  SparkStreamingDriverHAOps {
    //设置zookeeper中存放偏移量的位置
    val zkTopicOffsetPath="/offset"
    //获取zookeeper的编程入口
    val client:CuratorFramework={
        val client=CuratorFrameworkFactory.builder()
                .connectString("hadoop01:2181,hadoop02:2181,hadoop03:2181/kafka")
                .namespace("2019_1_7")
            .retryPolicy(new ExponentialBackoffRetry(1000,3))
            .build()
        client.start()
        client
    }

    def main(args: Array[String]): Unit = {
        //屏蔽日志
        Logger.getLogger("org.apache.hadoop").setLevel(Level.WARN)
        Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
        Logger.getLogger("org.project-spark").setLevel(Level.WARN)
        //2 direct zy-flume-kafka
        if(args==null||args.length<3){
            println(
                """
                  |Parameter Errors! Usage: <batchInterval> <groupId> <topics>
                  |batchInterval        : 批次间隔时间
                  |groupId              : 消费组的id
                  |topic                : 读取的topic
                """.stripMargin)
            System.exit(-1)
        }
        //获取传入的参数
        val Array(batchInterval,groupId,topic)=args
        //1.构建程序入口
        val conf: SparkConf = new SparkConf()
            .setMaster("local[2]")
            .setAppName("SparkStreamingDriverHAOps")
        val ssc =new StreamingContext(conf,Seconds(batchInterval.toLong))
        //连接kafka的参数
        val kafkaParams=Map(
            "bootstrap.servers"->"hadoop01:9092,hadoop02:9092,hadoop03:9092", //集群入口
            "auto.offset.reset"->"smallest" //消费方式
        )
        //2.创建kafka的message
        val message:DStream[(String,String)]=createMessage(topic,groupId,ssc,kafkaParams)
        //3.业务处理,这里主要是介绍如何kafka整合sparkStreaming,所以这里不做业务处理
        message.foreachRDD(rdd=>{
            if(!rdd.isEmpty()){
                println(
                    """
                      |####################>_<####################
                    """.stripMargin+rdd.count())
            }
            //更新偏移量
            storeOffsets(rdd.asInstanceOf[HasOffsetRanges].offsetRanges,groupId)
        })
        //4.启动程序
        ssc.start()
        ssc.awaitTermination()
    }

    /**
      * 创建kafka对应的message
      * 分两种情况:
      *  1.第一次消费的时候,从zk中读取不到偏移量
      *  2.之后的消费从zk中才能读取到偏移量
      */
    def createMessage(topic: String, groupId: String, ssc: StreamingContext, kafkaParams: Map[String, String]): InputDStream[(String, String)] = {
        //获取偏移量,以及判断是否是第一次消费
        val (fromOffsets,flag)=getFromOffsets(topic, groupId)
        var message:InputDStream[(String, String)] = null
        //构建kafka对应的message
        if(flag){ //标记位,使用zk中得到的对应的partition偏移量信息,如果有为true
            /**
              * recordClass: Class[R],
              * kafkaParams: JMap[String, String],
              * fromOffsets: JMap[TopicAndPartition, JLong],
              * messageHandler: JFunction[MessageAndMetadata[K, V], R]
              */
            val messageHandler = (mmd: MessageAndMetadata[String, String]) => (mmd.key, mmd.message)
            message = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder,(String,String)](ssc,kafkaParams,fromOffsets,messageHandler)
        }else{  //如果是第一次读取为false
            /**
              * createDirectStream[
              * String, key的类型
              * String, value的类型
              * StringDecoder, key的序列化的类型
              * StringDecoder] value的序列化的类型
              *
              */
            message=KafkaUtils.createDirectStream[String,
                String,
                StringDecoder
                , StringDecoder](ssc,kafkaParams,topic.split("\s+").toSet)
        }
        message
    }

    //获取对应的topic中的每一个partition的偏移量信息
    def getFromOffsets(topic: String, groupId: String):(Map[TopicAndPartition, Long], Boolean)= {
        //构建存储offset的位置信息的路径
        val zkPath=s"${zkTopicOffsetPath}/${topic}/${groupId}"
        //判断当前路径是否存在,不存在则创建
        nsureZKPathExists(zkPath)

        //获取所有分区中存储的offset信息
        import scala.collection.JavaConversions._
        val offsets=for{p<-client.getChildren.forPath(zkPath)}yield{
            val offset=new String(client.getData.forPath(s"${zkPath}/${p}")).toLong
            (new TopicAndPartition(topic,p.toInt),offset)
        }
        //如果未空表示第一次读取,无偏移量信息
        if(offsets.isEmpty){
            (offsets.toMap,false)
        }else{
            (offsets.toMap,true)
        }
    }

    def storeOffsets(offsetRanges: Array[OffsetRange], groupId: String): Unit = {
        for(offsetRange<-offsetRanges){
            val partition=offsetRange.partition
            val topic=offsetRange.topic
            //获取偏移量
            val offset=offsetRange.untilOffset
            //构建存放偏移量的znode
            val path=s"${zkTopicOffsetPath}/${topic}/${groupId}/${partition}"
            //判断是否存在,不存在则创建
            nsureZKPathExists(path)
            client.setData().forPath(path,(""+offset).getBytes())
        }
    }
    def nsureZKPathExists(zkPath: String) = {
        //如果为空的话就创建
        if(client.checkExists().forPath(zkPath)==null){
            //如果父目录不存在,连父目录一起创建
            client.create().creatingParentsIfNeeded().forPath(zkPath)
        }
    }
}

注意(direct +zookeeper):
 - 不需要创建多个输入kafka流并将其合并,使用directStream,spark Streaming将创建于使用kafka分区一样多的RDD分区,这些分区的数据全部从kafka并行读取数据,kafka和RDD分区之间有一对一的映射关系。
 - Direct方式没有接收器,不需要预先写入日志,只要kafka数据保留时间足够长就行
 - 保证了正好一次的消费语义(offset保存在zookeeper中)

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