SparkStream整合Flume和Kafka

版本说明

• apache-flume-1.6.0-cdh5.15.1-bin
• kafka_2.11-0.9.0.0
• scala-2.11.12
• apache-maven-3.3.9
• spark-2.4.4-bin-2.6.0-cdh5.15.1
• zookeeper-3.4.5-cdh5.15.1

处理流程

模拟日志生成

package com.hlsijx.spark;

import org.apache.log4j.Logger;

/**
 * 日志生成器，启动后即可模拟日志信息
 */
public class LoggerGenerator {

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

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

配置Flume

Flume部署

• 引入flume-ng-log4jappender

  
      org.apache.flume.flume-ng-clients</groupId>
      flume-ng-log4jappender</artifactId>
      1.6.0</version>
  </dependency>
  

• 配置avro_memory_kafka.conf

  avro_memory_kafka.sources = avro_source
  avro_memory_kafka.sinks = kafka_sink
  avro_memory_kafka.channels = memory_channel
  
  avro_memory_kafka.sources.avro_source.type = avro
  avro_memory_kafka.sources.avro_source.bind = hlsijx
  avro_memory_kafka.sources.avro_source.port = 44444
  
  avro_memory_kafka.sinks.kafka_sink.type = org.apache.flume.sink.kafka.KafkaSink
  avro_memory_kafka.sinks.kafka_sink.kafka.bootstrap.servers = hlsijx:9092
  avro_memory_kafka.sinks.kafka_sink.kafka.topic = hello-spark
  avro_memory_kafka.sinks.kafka_sink.flumeBatchSize = 5
  
  avro_memory_kafka.channels.memory_channel.type = memory
  
  avro_memory_kafka.sources.avro_source.channels = memory_channel
  avro_memory_kafka.sinks.kafka_sink.channel = memory_channel
  

• 配置log4j.properties

  log4j.rootLogger=info,stdout,flume
  
  #console
  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
  
  #flume log4jappender
  log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
  log4j.appender.flume.Hostname = hlsijx
  log4j.appender.flume.Port = 44444
  log4j.appender.flume.UnsafeMode = true
  

• 启动Flume

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

配置Kafka
Kafka部署

package com.hlsijx.spark.stream.kafka

import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.{Seconds, StreamingContext}


	object KafkaDirect {
	  //参数：hlsijx:9092 hello-spark
	  def main(args: Array[String]): Unit = {
	
	    if (args.length != 2){
	      System.err.print("Usage: KafkaDirect  ")
	      System.exit(1)
	    }
	
	    val Array(brokerList, topic) = args
	
	    val sparkConf = new SparkConf().setAppName("KafkaDirect").setMaster("local[2]")
	    val ssc = new StreamingContext(sparkConf, Seconds(5))
	
	    val kafkaParams = Map("metadata.broker.list" -> brokerList)
	    val topics = topic.split(",").toSet
	    val directKafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
	
	    val wordCount = directKafkaStream.map(_._2).count()
	
	    wordCount.print()
	
	    ssc.start()
	    ssc.awaitTermination()
	  }
	}

启动后，就能看到消费的日志条数记录