(一个）kafka-jstorm集群实时日志分析它 ---------kafka实时日志处理

package com.doctor.logbackextend;

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

import kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;

import org.apache.commons.lang.RandomStringUtils;
import org.junit.Test;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * zookeeper 和kafka环境准备好。
本地端口号默认设置
 * 
 * @author doctor
 *
 * @time   2014年10月24日 下午3:14:01
 */
public class KafkaAppenderTest {
	private static final Logger LOG = LoggerFactory.getLogger(KafkaAppenderTest.class);
	

	/** 先启动此測试方法，模拟log日志输出到kafka */
	@Test
	public void test_log_producer() {
		while(true){
			LOG.info("test_log_producer : "  + RandomStringUtils.random(3, "hello doctro,how are you,and you"));
		}
	}
	
	
	/** 再启动此測试方法。模拟消费者获取日志，进而分析，此方法不过打印打控制台，不是log。防止模拟log測试方法数据混淆 */
	@Test
	public void test_comsumer(){
		Properties props = new Properties();
		props.put("zookeeper.connect", "127.0.0.1:2181,127.0.0.1:2182,127.0.0.1:2183");
		props.put("group.id", "kafkatest-group");
//		props.put("zookeeper.session.timeout.ms", "400");
//		props.put("zookeeper.sync.time.ms", "200");
//		props.put("auto.commit.interval.ms", "1000");
		ConsumerConfig paramConsumerConfig = new ConsumerConfig(props );
		ConsumerConnector consumer = Consumer.createJavaConsumerConnector(paramConsumerConfig );
		
		Map<String, Integer> topicCountMap = new HashMap<>();
		topicCountMap.put("kafka-test", new Integer(1));
		Map<String, List<KafkaStream<byte[], byte[]>>> consumerStream = consumer.createMessageStreams(topicCountMap);
		List<KafkaStream<byte[], byte[]>> streams = consumerStream.get("kafka-test");
		
		for (KafkaStream<byte[], byte[]> stream : streams) {
			ConsumerIterator<byte[], byte[]> it = stream.iterator();
			while(it.hasNext())
			System.out.println(new String("test_comsumer: " + new String(it.next().message())));
		}
		
		
	}

}

为了实时日志处理互联网系统的日志，对于电商来说具有非常重要的意义，比方，淘宝购物时候，你浏览某些商品的时候。系统后台实时日志处理分析后，系统能够向用户实时推荐给用户相关商品。来引导用户的选择等等。

为了实时日志处理。我们选择kafka集群，日志的处理分析选择jstorm集群，至于jstorm处理的结果，你能够选择保存到数据库里。入hbase、mysql。maridb等。

系统的日志接口选择了slf4j，logback组合，为了让系统的日志可以写入kafka集群，选择扩展logback Appender。在logback里配置一下。就行自己主动输出日志到kafka集群。

kafka的集群安装，在此不介绍了，为了模拟真实性，zookeeper本地集群也安装部署了。

以下是怎样扩展logback Appender

package com.doctor.logbackextend;

import java.util.Properties;

import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
import ch.qos.logback.classic.spi.ILoggingEvent;
import ch.qos.logback.core.AppenderBase;

public class KafkaAppender extends AppenderBase<ILoggingEvent> {

	private String topic;
	private String zookeeperHost;
	

	private String broker;
	private Producer<String, String> producer;
	private Formatter formatter;
	
	public String getBroker() {
		return broker;
	}

	public void setBroker(String broker) {
		this.broker = broker;
	}
	@Override
	protected void append(ILoggingEvent eventObject) {
		String message = this.formatter.formate(eventObject);
		this.producer.send(new KeyedMessage<String, String>(this.topic, message));

	}

	@Override
	public void start() {
		if (this.formatter == null) {
			this.formatter = new MessageFormatter();
		}
		
		super.start();
		Properties props = new Properties();
		props.put("zk.connect", this.zookeeperHost);
		props.put("metadata.broker.list", this.broker);
		props.put("serializer.class", "kafka.serializer.StringEncoder");
		
		ProducerConfig config = new ProducerConfig(props);
		this.producer = new Producer<String, String>(config);
	}

	@Override
	public void stop() {
		super.stop();
		this.producer.close();
	}

	
	
	public String getTopic() {
		return topic;
	}

	public void setTopic(String topic) {
		this.topic = topic;
	}

	public String getZookeeperHost() {
		return zookeeperHost;
	}

	public void setZookeeperHost(String zookeeperHost) {
		this.zookeeperHost = zookeeperHost;
	}

	public Producer<String, String> getProducer() {
		return producer;
	}

	public void setProducer(Producer<String, String> producer) {
		this.producer = producer;
	}


	public Formatter getFormatter() {
		return formatter;
	}

	public void setFormatter(Formatter formatter) {
		this.formatter = formatter;
	}
	
	
	
	/**
	 * 格式化日志格式
	 * @author doctor
	 *
	 * @time   2014年10月24日 上午10:37:17
	 */
	interface Formatter{
		String formate(ILoggingEvent event);
	}
	
	public static class MessageFormatter implements Formatter{

		@Override
		public String formate(ILoggingEvent event) {
			
			return event.getFormattedMessage();
		}
		
	}
}

对于日志的输出格式MessageFormatter没有特殊处理，由于仅仅是模拟一下，你能够制定你的格式，入json等。

在logback.xml的配置例如以下：

<appender name="kafka" class="com.doctor.logbackextend.KafkaAppender">
 		<topic>kafka-test</topic>
 		<!-- <zookeeperHost>127.0.0.1:2181</zookeeperHost> -->
 		<!-- <broker>127.0.0.1:9092</broker> -->
 		<zookeeperHost>127.0.0.1:2181,127.0.0.1:2182,127.0.0.1:2183</zookeeperHost>
 		<broker>127.0.0.1:9092,127.0.0.1:9093</broker>
 	</appender>
 	
 	
	<root level="all">
		<appender-ref ref="stdout" />
		<appender-ref ref="defaultAppender" />
		<appender-ref ref="kafka" />
	</root>

  <zookeeperHost>

我本地启动了三个zookeer。依据配置。你能够知道是怎样配置的吧。

kafka集群的broker我配置了两个，都是在本地机器。

測试代码：

package com.doctor.logbackextend;

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

import kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;

import org.apache.commons.lang.RandomStringUtils;
import org.junit.Test;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * zookeeper 和kafka环境准备好。本地端口号默认设置
 * 
 * @author doctor
 *
 * @time   2014年10月24日 下午3:14:01
 */
public class KafkaAppenderTest {
	private static final Logger LOG = LoggerFactory.getLogger(KafkaAppenderTest.class);
	

	/** 先启动此測试方法，模拟log日志输出到kafka */
	@Test
	public void test_log_producer() {
		while(true){
			LOG.info("test_log_producer : "  + RandomStringUtils.random(3, "hello doctro,how are you,and you"));
		}
	}
	
	
	/** 再启动此測试方法，模拟消费者获取日志，进而分析，此方法不过打印打控制台，不是log。防止模拟log測试方法数据混淆 */
	@Test
	public void test_comsumer(){
		Properties props = new Properties();
		props.put("zookeeper.connect", "127.0.0.1:2181,127.0.0.1:2182,127.0.0.1:2183");
		props.put("group.id", "kafkatest-group");
//		props.put("zookeeper.session.timeout.ms", "400");
//		props.put("zookeeper.sync.time.ms", "200");
//		props.put("auto.commit.interval.ms", "1000");
		ConsumerConfig paramConsumerConfig = new ConsumerConfig(props );
		ConsumerConnector consumer = Consumer.createJavaConsumerConnector(paramConsumerConfig );
		
		Map<String, Integer> topicCountMap = new HashMap<>();
		topicCountMap.put("kafka-test", new Integer(1));
		Map<String, List<KafkaStream<byte[], byte[]>>> consumerStream = consumer.createMessageStreams(topicCountMap);
		List<KafkaStream<byte[], byte[]>> streams = consumerStream.get("kafka-test");
		
		for (KafkaStream<byte[], byte[]> stream : streams) {
			ConsumerIterator<byte[], byte[]> it = stream.iterator();
			while(it.hasNext())
			System.out.println(new String("test_comsumer: " + new String(it.next().message())));
		}
		
		
	}

}

结果，明天再附上截图。

(一个）kafka-jstorm集群实时日志分析 它 ---------kafka实时日志处理

(一个）kafka-jstorm集群实时日志分析它 ---------kafka实时日志处理