Spark Streaming 整合Flume 和Kafka

前言

首先会使用Java开发一个日志产生器，使用Flume收集此信息到Kafka，然后Spark Streaming 从Kafka读取消息。

完整项目代码：
https://github.com/GYT0313/Spark-Learning/tree/master/sparkstream

1. 日志产生器

首先你应该创建一个Maven 项目，参考：https://blog.csdn.net/qq_38038143/article/details/89926205

在test 目录下创建java 项目，并创建LoggerGenerator 类，以及resources目录和log4j.properties：
在 Project structure中设置：

目录结构：

LoggerGenerator.class

import org.apache.log4j.Logger;

/**
 * 模拟日志产生
 */

public class LoggerGenerator {

    private  static Logger logger = Logger.getLogger(LoggerGenerator.class.getName());

    public static void main(String[] args) throws Exception {
        int index = 0;
        while (true) {
            Thread.sleep((1000));
            logger.info("value:" + index++);
        }
    }
}

log4j.properties

log4j.rootCategory=INFO, console, flume
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.out
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yyyy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# log to flume
log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
log4j.appender.flume.Hostname = master
log4j.appender.flume.Port = 41414
log4j.appender.flume.UnsafeMode = true
log4j.appender.flume.layout = org.apache.log4j.PatternLayout

pom.xml 依赖：

  
    2.11.8
    2.1.1
    2.3.2
    2.7.3
    1.4.8
    5.1.46
    1.9.0
  


  
    
      
      
        io.netty
        netty-all
        4.1.17.Final
      
    
  



  
    
      org.jboss.netty
      netty
      3.2.4.Final
    

    
    
      org.apache.spark
      spark-streaming_2.11
      ${spark.version}
    

    
    
      org.apache.spark
      spark-sql_2.11
      ${spark.version}
    

    
    
      org.apache.flume
      flume-ng-auth
      ${flume.version}
    
    
      org.mortbay.jetty
      servlet-api
      2.5-20110124
    
    
      org.apache.spark
      spark-streaming-flume-sink_2.11
      ${spark.version}
    
    
    
      org.apache.commons
      commons-lang3
      3.5
    

    
    
      org.apache.spark
      spark-streaming-flume_2.11
      ${spark.version}
    

    
    
      org.apache.avro
      avro
      1.7.4
    

    
    
      org.apache.spark
      spark-streaming-kafka-0-10_2.11
      ${spark.version}
    


    
      com.fasterxml.jackson.module
      jackson-module-scala_2.11
      2.6.7.1
    


    
      org.scala-lang
      scala-library
      ${scala.version}
    
    
      org.scalatest
      scalatest
      0.9.1
    



    
    
      org.apache.kafka
      kafka_2.11
      ${kafka.version}
    
    
      org.apache.kafka
      kafka-clients
      ${kafka.version}
    

    
    
      org.apache.hadoop
      hadoop-client
      ${hadoop.version}
    

    
    
      org.apache.hbase
      hbase-client
      ${hbase.version}
    
    
      org.apache.hbase
      hbase-server
      ${hbase.version}
    

    
    
      mysql
      mysql-connector-java
      ${mysql.version}
    

    
      org.specs
      specs
      1.2.5
      test
    

    
    
      org.apache.flume.flume-ng-clients
      flume-ng-log4jappender
      ${flume.version}
    
    
      org.apache.flume
      flume-ng-sdk
      ${flume.version}
    

    
    
      net.jpountz.lz4
      lz4
      1.3.0
    


  

  
  。。。。

测试，先输出到flume：
在flume-1.9.0/conf 目录下创建：
streaming.conf

# Name the components on this agent
agent1.sources = avro-source
agent1.sinks = log-sink
agent1.channels = logger-channel

# Describe/configure the source
agent1.sources.avro-source.type = avro
agent1.sources.avro-source.bind = master
agent1.sources.avro-source.port = 41414

# Describe the sink
agent1.sinks.log-sink.type = logger

# Use a channel which buffers events in memory
agent1.channels.logger-channel.type = memory

# Bind the source and sink to the channel
agent1.sources.avro-source.channels = logger-channel
agent1.sinks.log-sink.channel = logger-channel

启动flume：

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

运行LoggerGeneraotr：

日志产生器开发完成。。。

2. Spark Streaming 从kakfa读取数据

首先启动zookeeper，再启动kafka：
创建kafka主题：

# kafka 创建主题
kafka-topics.sh --create --zookeeper slave1:2181 \
--replication-factor 1 --partitions 1 --topic streaming_topic

创建StreamingKafkaApp.scala
从指定的topic 消费消息，并且计数输出到控制台：

package com.gyt.sparkstream

import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka010._

object StreamingKafkaApp {
  def main(args: Array[String]) {
    if (args.length < 3) {
      System.err.println("Usage: StreamingKafkaApp   ")
      System.exit(1)
    }

    val Array(brokers, groupId, topics) = args

    // Create context with 2 second batch interval
    val sparkConf = new SparkConf().setAppName("StreamingKafkaApp").setMaster("local[2]")
    val ssc = new StreamingContext(sparkConf, Seconds(5))

    // Create direct kafka stream with brokers and topics
    val topicsSet = topics.split(",").toSet
    val kafkaParams = Map[String, Object](
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,
      ConsumerConfig.GROUP_ID_CONFIG -> groupId,
      ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],
      ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer])
    // Create direct inputStream
    val messages = KafkaUtils.createDirectStream[String, String](
      ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](topicsSet, kafkaParams))

    // Get the lines, split them into words, count the words and print
    messages.map(_.value()).count().print()


    // Start the computation
    ssc.start()
    ssc.awaitTermination()
  }
}

测试程序：
启动kafka生产者和消费者：
博主使用的是集群，如果你是但机器，启动但机器即可，slave1修改为你主机名

# 终端1-生产者
kafka-console-producer.sh --broker-list slave1:9092 \
--topic streaming_topic


# kafka消费
kafka-console-consumer.sh --bootstrap-server slave1:9092 \
--topic streaming_topic --from-beginning

测试：

运行spark 程序，第一次报错，然后设置参数：

参数：broker group topic

在生产者出输入消息：

3. 启动日志生成器-Fluem-Kafka-Spark Streaming

运行Flume：为什么这样配置，可以查看Flume官网
flume-1.9.0/conf/streaming2.conf

# Name the components on this agent
agent1.sources = avro-source
agent1.channels = logger-channel
agent1.sinks = kafka-sink

# Describe/configure the source
agent1.sources.avro-source.type = avro
agent1.sources.avro-source.bind = master
agent1.sources.avro-source.port = 41414

# Describe the sink
agent1.sinks.kafka-sink.type = org.apache.flume.sink.kafka.KafkaSink
agent1.sinks.kafka-sink.kafka.topic = streaming_topic
agent1.sinks.kafka-sink.kafka.bootstrap.servers = slave1:9092
agent1.sinks.kafka-sink.kafka.flumeBatchSize = 20
agent1.sinks.kafka-sink.kafka.producer.acks = 1

# Use a channel which buffers events in memory
agent1.channels.logger-channel.type = memory

# Bind the source and sink to the channel
agent1.sources.avro-source.channels = logger-channel
agent1.sinks.kafka-sink.channel = logger-channel

启动：

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

启动Kafka 服务：博主使用的是集群，如果你是但机器，启动但机器即可

运行Spark Streaming：

最后，运行日志生成器LoggerGenerator：

完！