Spark Streaming整合log4j、Flume与Kafka的案例

1.框架

2.log4j完成模拟日志输出

设置模拟日志格式，log4j.properties：

log4j.rootLogger = INFO,stdout

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

模拟日志输出，LoggerGenerator.java：

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++);
        }
    }

}

运行结果：

2020-03-07 18:21:37,637 [main] [LoggerGenerator] [INFO] - current value is:0
2020-03-07 18:21:38,639 [main] [LoggerGenerator] [INFO] - current value is:1
2020-03-07 18:21:39,639 [main] [LoggerGenerator] [INFO] - current value is:2
2020-03-07 18:21:40,640 [main] [LoggerGenerator] [INFO] - current value is:3
2020-03-07 18:21:41,640 [main] [LoggerGenerator] [INFO] - current value is:4
2020-03-07 18:21:42,641 [main] [LoggerGenerator] [INFO] - current value is:5
2020-03-07 18:21:43,641 [main] [LoggerGenerator] [INFO] - current value is:6
2020-03-07 18:21:44,642 [main] [LoggerGenerator] [INFO] - current value is:7
2020-03-07 18:21:45,642 [main] [LoggerGenerator] [INFO] - current value is:8
2020-03-07 18:21:46,642 [main] [LoggerGenerator] [INFO] - current value is:9
2020-03-07 18:21:47,643 [main] [LoggerGenerator] [INFO] - current value is:10

3.Flume收集log4j日志

$FLUME_HOME/conf/streaming.conf：

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=0.0.0.0
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（注意输出到控制台上为INFO,console，不是点【.】）:

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

pom.xml加上一个jar包：

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

修改log4j.properties，使其与Flume链接：

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 = hadoop000
log4j.appender.flume.Port = 41414
log4j.appender.flume.UnsafeMode = true

启动log4j：

Flume采集成功

4.KafkaSink链接Kafka与Flume

使用Kafka第一件事是把Zookeeper启动起来~

./zkServer.sh start

启动Kafka

./kafka-server-start.sh -daemon /home/hadoop/app/kafka_2.11-0.9.0.0/config/server.properties 

看下Kafka列表（用./kafka-topics.sh会报错,用“./”加文件名.sh执行时，必须给.sh文件加x执行权限）：

kafka-topics.sh --list --zookeeper hadoop000:2181

创建一个topic：

kafka-topics.sh --create \
--zookeeper hadoop000:2181 \
--replication-factor 1 \
--partitions 1 \
--topic tp_streamingtopic

对接Flume与Kafka，设置Flume的conf，取名为streaming2.conf：

Kafka sink需要的参数有（每个版本不一样，具体可以查阅官网）：

• sink类型填KafkaSink
• 需要链接的Kafka topic
• Kafka中间件broker的地址与端口号
• 是否使用握手机制
• 每次发送的数据大小

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=0.0.0.0
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 = tp_streamingtopic
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

Kafka需要启动一个消费者消费Flume中Kafka sink来的数据：

./kafka-console-consumer.sh --zookeeper hadoop000:2181 --topic tp_streamingtopic

启动log4j：

成功传输~

5.Spark Streaming消费Kafka数据

package com.taipark.spark

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

/**
  * Spark Streaming 对接 Kafka
  */
object KafkaStreamingApp {
  def main(args: Array[String]): Unit = {
    if(args.length != 2){
      System.err.println("Userage:KafkaStreamingApp<brokers><topics>");
      System.exit(1);
    }
    val Array(brokers,topics) = args

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

    val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)
    val topicSet = topics.split(",").toSet
    val messages = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](
      ssc,kafkaParams,topicSet
    )
    //第二位是字符串的值
    messages.map(_._2).flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()

    ssc.start()
    ssc.awaitTermination()
  }

}

入参是Kafka的broker地址与topic名称：

本地Run一下：

成功接收，完成~