Spark整合Kafka小项目 -

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Spark整合Kafka小项目

博客分类：

大数据，Spark，Kafka，日志分析

kafka spark 日志解析 sparkstream

SparkStreaming与kafka整合小项目实践含所有代码带详细注释

总流程：自制日志生成器生成含数据日志，使用kafkaAppender直接发送到kafka，SparkStreaming从kafka消费日志，并流式处理将结果发送到kafka另一个topic，Java后台从kafka消费日志分析结果，实现秒级大数据实时分析展示。

版本

kafka_2.11-0.11.0.1

spark-2.1.1-bin-hadoop2.7

scala-2.11.11

Jdk-1.8

Spark使用Intelij Idea

其余使用eclipse

第一步

日志生成器输出日志到kafka

重点jar包：

kafka-log4j-appender-0.11.0.1.jar //日志使用

kafka_2.11-0.11.0.1.jar //如果报错就加上吧

kafka-clients-0.11.0.1.jar //如果报错就加上吧

slf4j-api-1.7.25.jar //日志框架也可以用其他的

slf4j-log4j12-1.7.25.jar

配置文件内容及注意事项

文件名：log4j.properties

文件内容：

log4j.rootLogger=DEBUG,stdout,KAFKA
//appender Console
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss.SSS} %5p %x-%t %l  (message:%m)%n
 
## appender KAFKA
log4j.appender.KAFKA=org.apache.kafka.log4jappender.KafkaLog4jAppender
log4j.appender.KAFKA.topic=log-topic
log4j.appender.KAFKA.brokerList=master:9090
log4j.appender.KAFKA.compressionType=none
log4j.appender.KAFKA.syncSend=true
log4j.appender.KAFKA.layout=org.apache.log4j.PatternLayout
log4j.appender.KAFKA.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss.SSS} %5p %x-%t %l  (message:%m)

文件名：my.properties

#time interval of every times,unit is  ms,default 100ms
timeinterval=1000
#the count of log every times,default 1000
frequency=298
#runningtime unit is  ms,default 60000ms
runtime=6000000

代码解析：

LogWriterExcutor.java

import org.apache.log4j.Logger;
class LogWriterExcutor implements Runnable{
	
	Logger logger = Logger.getLogger(this.getClass().getName());
	private String []message;
	public LogWriterExcutor(String []message){
		this.message = message;
	}	
	@Override
	public void run() {
		// TODO Auto-generated method stub
		for(String e : message)
			logger.info(e);
	}
}

LogCreater.java

import java.io.FileInputStream;
import java.io.IOException;
import java.util.Properties;
import java.util.Random;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import org.apache.log4j.Logger;

class LogCreater extends Constant{
	
	Logger logger = Logger.getLogger(this.getClass().getName());
	
	ExecutorService executor = null;
	private int timeinterval = TIME_INTERVAL;		//间隔多久发送一批日志，单位毫秒
	private int frequency = FREQUENCY;				//每一批发送发送多少条数据，单位条
	private int sumOfChinese = SUM_CHINESE;			//自定义中文字集元素个数
	private int runtime = RUNTIME;					//程序运行总时间
	private long startTime = 0;
	private long endTime = 0;
	private long logCount = 0;						//日志已发条数
	private boolean stop = true;
	
	LogCreater(){
		init();
	}
	
	public void init(){
		Properties properties = new Properties();
		FileInputStream in;
		try {
			in = new FileInputStream("src\\source\\my.properties");
			properties.load(in);
			timeinterval = Integer.parseInt((String)properties.get("timeinterval"));
			frequency =Integer.parseInt((String)properties.get("frequency"));
			runtime =Integer.parseInt((String)properties.get("runtime"));
		} catch (IOException e) {
			logger.error("配置文件读取失败");
			e.printStackTrace();
		}
		executor = Executors.newCachedThreadPool();
		startTime = System.currentTimeMillis();
		printHint();
	}
	
	
	public void startCreate() {
		System.out.println("正在生成日志.....");
		
		if(executor == null){
			logger.error("线程池获取失败，日志生成器执行失败。执行结束");
			return;
		}
		while(stop){
			String []messages = getMessages(frequency);
			create(messages);
			try {
				Thread.sleep(timeinterval);
			} catch (InterruptedException e) {
				logger.error("线程睡眠执行出错");
				e.printStackTrace();
			}
			endTime = System.currentTimeMillis();
			if((endTime-startTime)>runtime)
				stop = false;
		}
		
		System.out.println("共生成 "+logCount+" 条日志。");
	}
	
	private void create(String []messages) {
		executor.execute(new Thread(new LogWriterExcutor(messages)));
		logCount += messages.length;
	}
	
	private String[] getMessages(Integer frequency) {
		Random rand = new Random();
		String []massages = new String[frequency];
		for(int i=0;i<frequency;i++){
			massages[i] = REGRET[rand.nextInt(sumOfChinese)];
		}
		return massages;
	}
	
	private void printHint(){
		System.out.println("每次时间间隔\t"+timeinterval+"ms");
		System.out.println("每次日志数量\t"+frequency+"条/次");
		System.out.println("预计运行时间\t"+runtime/1000+"s");
	}
}

Constant .java

public class Constant {

	/*
	 * 这个文件中存放的全部是常量
	 */
	
	/*
	 * 日志生成器隔多少时间写一批日志,默认值
	 */
	public static Integer TIME_INTERVAL = 100;
	
	/*
	 * 日志生成器每一批次生成多少条日志，默认值
	 */
	public static Integer FREQUENCY = 100;
	
	/*
	 * 运行时间，默认一分钟，默认值
	 */
	public static Integer RUNTIME = 60000;
	
	/*
	 * 298个中文字，来自楚辞《惜誓》
	 */
	public static String[]REGRET = {"一","言","老","调","清","者","舆","昆","合","渊","下","而","同","不","明","与",
			"昏","谏","小","騑","少","我","气","谔","世","或","尚","丝","鸟","逢","瀣","中","是","鸱","就","水","临","制",
			"举","砾","鸾","所","乃","鹄","久","居","陆","之","虎","乎","乐","虑","乔","虖","剖","遗","虚","聚","江","吸",
			"瑟","象","乡","衡","周","息","虯","衰","驰","山","驱","乱","干","年","并","恶","穷","偷","顺","登","白","幽",
			"驾","岁","蚁","节","梅","沆","皆","皇","骋","二","于","隐","源","麒","骖","骛","墟","功","麟","纡","纫","被",
			"身","犬","躯","悲","河","蚴","犹","人","难","裁","仁","狂","黄","集","哉","背","苍","从","风","仑","黑","盖",
			"高","飙","仙","四","盛","惜","飞","回","苟","因","以","拥","苦","独","竭","曲","直","相","建","固","国","攀",
			"异","儃","处","茅","月","夏","霑","休","众","北","圜","生","索","謣","圣","贤","伤","大","在","用","木","天",
			"眩","太","夫","伯","地","朱","失","贵","然","贼","放","愿","流","权","充","故","商","均","先","浊","子","何",
			"余","神","非","止","赤","此","来","车","革","兮","佯","数","女","杳","海","睹","蝼","彼","载","松","使","长",
			"极","羁","如","概","历","玉","涉","冉","枉","羊","王","後","厌","再","美","箕","得","龙","原","龟","审","醢",
			"群","冥","推","循","讬","枭","况","德","容","方","澹","离","去","旁","见","观","係","心","寄","又","反","重",
			"野","藏","量","发","翔","比","俗","志","诚","进","远","川","察","忠","无","濡","矣","凤","日","知","左","自",
			"矫","可","称","翱","深","已","右","至","石","念","时","迻","忽","寿","丹","根","为","尽",};
	
	/*
	 * 中文字个数，用作随机数范围使用
	 */
	public static Integer SUM_CHINESE = 100;
}

MyUtil.java

import java.util.Random;
public class MyUtil {
	public static int[] getRand(int n,int range){
		Random ran = new Random();
		int []arr = new int[n];
		while(n-->0){
			arr[n] = ran.nextInt(range);
		}
		return arr;
	}
}

Demo.java

/*
 * 日志生成器
 */

public class Demo{
	public static void main(String[] args){
		new LogCreater().startCreate();
		System.exit(0);
	}
}

目录结构：就普通java project，

第二步

创建kafka topic

安装跳过

配置%KAFKA_HOME%conf/server.properties：

网上教程很多，此处不再赘述

启动kafka

kafka-server-start.sh config/server.properties &

创建topic:

kafka-topics.sh --create --zookeeper master:2181,slave1:2181,slave2:2181 --replication-factor 1 --partitions 1 --topic log-topic

查看topic:

kafka-topics.sh --describe --zookeeper master:2181 --topic log-topic

创建控制台消费者:

kafka-console-consumer.sh --bootstrap-server master:9090 --from-beginning --topic log-topic

启动顺序：

1.启动kafka Server，2.创建topic,3.查看创建的topic(可选)，4.创建控制台消费者，5.启动日志生成器程序。

注意事项：在启动控制台消费者的终端会将接收的日志打印出来，命令最后面加上 & 符号可将进程调至后台运行。关闭消费者使用Ctrl+c

第三步

spark消费kafka的日志

重点jar包:

kafka_2.11-0.11.0.1.jar

kafka-clients-0.11.0.1.jar

spark-streaming-kafka_2.11-1.6.3.jar

Spark所有自带jar包

Scala的SDK

报异常：

如果运行报java.lang.NoClassDefFoundError: org/apache/spark/Logging

这个Logging截止存在于spark-core_2.11-1.5.2中。

2.1.1版本saprk无此class文件，被org.apache.spark.internal.Logging取代。

解决办法

把1.5.2版本里面的这个class提出来单独用java -xvf new_name.jar class_dir 打包成一个jar包，然后当做常规jar工具包使用

过程解析：

Spark创建Receiver从kafka消费日志数据。

代码解析：Kafka.scala

import java.util.Properties
import java.util.logging.{Level, Logger}

import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import org.apache.kafka.common.serialization.StringSerializer
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext, Time}
//import com.trigl.spark.util.{DataUtil, LauncherMultipleTextOutputFormat}
import org.apache.spark.Logging
object Kafka extends Logging{

  private var producer: KafkaProducer[String, String] = _
  private var props : Properties = _

  def main(args: Array[String]) {

    Logger.getLogger("org.apache.spark").setLevel(Level.WARNING)
    System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkConf = new SparkConf().setAppName("LauncherStreaming")
    val ssc = new StreamingContext(sparkConf, Seconds(3))

    /*
        provider的参数
     */
    val brokerAddress = "master:9090"
    val topic = "pro-topic"
    props = new Properties()
    props.put("bootstrap.servers", brokerAddress)
    props.put("value.serializer", classOf[StringSerializer].getName)
    // Key serializer is required.
    props.put("key.serializer", classOf[StringSerializer].getName)
    // wait for all in-sync replicas to ack sends
    props.put("acks", "all")

	//创建kafka生产者，后面可以直接使用它发送数据  
    producer = new KafkaProducer[String, String](props)
    if(producer == null) {
      println("producer为空")
      ssc.stop()
    }

    /*
    *消费者参数
     */
    val zkQuorum = "master:2181,slave1:2181,slave2:2181"
	//这个group本来是随意创建，但是不能与已存在的重复，否在接收不到数据。每次运行请务必修改，或者做成参数，这个问题我尚未解决，但不影响流程///测试
    val group = "log-group21"		
    val topicMap = Map[String, Int]("log-topic" -> 1)

	//创建kafka消费者，如果不使用窗口将每隔【StreamingContext第二个参数定义时间】创建一个rdd
    val kafkaStream = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap, StorageLevel.MEMORY_AND_DISK_SER).map(_._2)

    kafkaStream.window(Seconds(12),Seconds(6)).foreachRDD((rdd: RDD[String], time: Time) => {
		//使用窗口每隔6秒钟处理一次前12秒区段的数据，此处6秒钟位置所在参数必须为StreamingContext（），第二个参数的倍数
		//这12秒时间区段的数据全在这一个rdd里面，直接迭代计算wordcount,将最终生成的数据发送到kafka另一个topic
      val re = rdd.flatMap(t => t.reverse.charAt(1).toString).map(m => (m,1L)).reduceByKey(_+_)
      val a = re.collect().toMap
      producer.send(new ProducerRecord[String, String](topic, a.mkString(",")))
    })

/*
    //这个可以用
    kafkaStream.foreachRDD((rdd: RDD[String], time: Time) => {

      //下面这个可以用，直接转发
      //rdd.collect().foreach(t => producer.send(new ProducerRecord[String, String](topic, t)))

      //下面这个可以用，微处理然后发送
      rdd.collect().foreach(t =>{
        println("正在发送: "+t)
        var s = t.reverse.charAt(1).toString		//提取前面夹杂在日志中的一个汉字
        producer.send(new ProducerRecord[String, String](topic, s))
      })

    })
*/
    ssc.start()
        // 等待实时流
    ssc.awaitTermination()
	
	//这条语句建议写上。
    producer.close()	
    println("它发生了")
  }

运行命令及注意事项

spark-submit --master spark://master:7077 --class streaming.Kafka libra.jar

如果缺包可以用--jars或者其他参数加上

特别注意：

每次运行请修改scala消费者的group消费组名，否则会接收不到数据，这个问题我还没解决

第四步

spark生成处理结果发送给kafka

jar包：

与第三步一样

创建新的topic:

创建命令请看第二步，新的topic请配置到spark的Producer中

，创建控制台消费者

第五步

Java后台消费kafka日志

重点ar包：

kafka-clients-0.11.0.1.jar

kafka_2.11-0.11.0.1.jar

slf4j-api-1.7.25.jar

slf4j-log4j12-1.7.25.jar

log4j-1.2.17.jar

普通Java工程

代码解析：

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import java.util.Collections;
import java.util.Properties;

public class Consumer{

	//0.11.0.0版本后使用KafkaConsumer，，版本0.11.0.0之前使用ConsumerConnector
    private final KafkaConsumer<Integer, String> consumer;
    private String topic;

    public Consumer(String topic) {
        Properties props = new Properties();
		//KafkaProperties是自定义接口文件，用于存放静态参数
        props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT);
		
		//这里消费组名貌似也有不能重复的嫌疑，每次运行建议修改一下
        props.put(ConsumerConfig.GROUP_ID_CONFIG, "log-group101");
        props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");
        props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
        props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, "30000");
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.IntegerDeserializer");
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");

        consumer = new KafkaConsumer<>(props);
        this.topic = topic;
    }

    public void doWork() {
	
		//设置topic
        consumer.subscribe(Collections.singletonList(topic));
        ConsumerRecords<Integer, String> records = null;
		
		//循环消费数据，每次请求都会把还没消费过的数据全部请求回来
        while(true) {
			//这里7秒是每次请求数据的最大等待时间，因为前面spark设置的6秒处理一次，这里用6秒，kafka中转可能延迟
        	records = consumer.poll(7000);
        	System.out.println("===========================");
        	System.out.println("接收数据条数："+records.count());
        	  for (ConsumerRecord<Integer, String> record : records) {
                  System.out.println(record.value()+"=="+ record.offset());
              }
        	  System.out.println("===========================");
        }
    }
}