大数据入门之分布式计算框架Spark(4) -- Spark Streaming整合Flume、Kafka

1.概述

从AppServer产生log4j，Flume通过hostname/port收集日志信息，利用KafkaSink发送给Kafka，Kafka通过两种模式，传给Spark Streaming，完成各个业务维度的统计和分析，统计结果入库。

2.整合流程

vim streaming.conf（仅供测试）

agent1.sources=avro-source
agent1.channels=logger-channel
agent1.sinks=log-sink

#define source
agent1.sources.avro-source.type=avro
agent1.sources.avro-source.bind=192.168.6.130
agent1.sources.avro-source.port=41414

#define channel
agent1.channels.logger-channel.type=memory

#define sink
agent1.sinks.log-sink.type=logger

agent1.sources.avro-source.channels=logger-channel
agent1.sinks.log-sink.channel=logger-channel

启动flume
flume-ng agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/streaming.conf \
--name agent1 \
-Dflume.root.logger=INFO,console

编写日志的配置文件  log4j.properties

引入依赖

        
        
            org.apache.flume.flume-ng-clients
            flume-ng-log4jappender
            1.6.0
        

# INFO级别以上的日志 控制台、Flume都会有
log4j.rootLogger=INFO,stdout,flume

log4j.appender.stdout = org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target = System.out
log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern = %d{yyyy-MM-dd HH:mm:ss,SSS} [%t] [%c] [%p] - %m%n

log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
log4j.appender.flume.Hostname = 192.168.6.130
log4j.appender.flume.Port = 41414
# 发送消息失败，不会抛出异常
log4j.appender.flume.UnsafeMode = true

这样启动程序，我们就会在Flume端控制台收集到相应日志信息。

启动Zookeeper，对接Kafka

# 启动Zookeeper
zkServer.sh start

# 启动Kafka
kafka-server-start.sh -daemon /home/Kiku/app/kafka_2.11-0.9.0.0/config/server.properties

# 创建topic
kafka-topics.sh --create --zookeeper hadoop000:2181 --replication-factor 1 --partitions 1 --topic test-topic

vim streaming2.conf

agent1.sources=avro-source
agent1.channels=logger-channel
agent1.sinks=kafka-sink

#define source
agent1.sources.avro-source.type=avro
agent1.sources.avro-source.bind=192.168.6.130
agent1.sources.avro-source.port=41414

#define channel
agent1.channels.logger-channel.type=memory

#define sink
agent1.sinks.kafka-sink.type=org.apache.flume.sink.kafka.KafkaSink
agent1.sinks.kafka-sink.topic=test-topic
agent1.sinks.kafka-sink.brokerList=hadoop000:9092
agent1.sinks.kafka-sink.requiredAcks=1
agent1.sinks.kafka-sink.batchSize=20

agent1.sources.avro-source.channels=logger-channel
agent1.sinks.kafka-sink.channel=logger-channel

启动Flume

flume-ng agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/streaming2.conf \
--name agent1 \
-Dflume.root.logger=INFO,console

Spark Streaming接收Kafka数据

/**
  * Spark Streaming 对接 Kafka
  */
object KafkaStreamingApp {

  def main(args: Array[String]): Unit = {

    //因为主机没有配置hadoop环境，所以需要加上这句话
    System.setProperty("hadoop.home.dir", "E:/winutils/")

    if (args.length != 4) {
      System.err.println("Usage: KafkaStreamingApp    ")
    }

    val Array(zkQuorum, group, topics, numThreads) = args

    val sparkConf = new SparkConf()
        .setMaster("local[2]").setAppName("KafkaStreamingApp")
    val ssc = new StreamingContext(sparkConf, Seconds(5))


    val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap
    //Spark Streaming 如何对接Kafka
    val messages = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap)

    //第二位是我们字符串的值，输出结果
    messages.map(_._2).count().print()


    ssc.start()
    ssc.awaitTermination()
  }
}

流程

在本地测试，首先运行LoggerGenerator，生成日志信息，使用Flume指定hostname、port去接收数据，分批次传给Kafka，在程序中，用Spark Streaming从Kafka消费数据。

在生产中，把LoggerGenerator打成jar包，Flume、Kafka类似，Spark Streaming代码也需要打成jar包，使用spark-submit提交到YARN上执行。（可以根据实际情况，选择运行模式 local/yarn/standalone）