标签：kafka jstorm 集群 实时日志分析
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);
/** 先启动此测试方法，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"));
}
}
/** 再启动此测试方法，模拟消费者获取日志，进而分析，此方法仅仅是打印打控制台，不是log，防止模拟log测试方法数据混淆 */
@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，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，你可以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，为了让系统的日志能够写入kafka集群，选择扩展logback Appender，在logback里配置一下，就可以自动输出日志到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。
下面是如何扩展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();
}
}
}
<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，根据配置，你可以知道是如何配置的吧。
kafka集群的broker我配置了两个，都是在本地机器。
测试代码：
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);
/** 先启动此测试方法，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"));
}
}
/** 再启动此测试方法，模拟消费者获取日志，进而分析，此方法仅仅是打印打控制台，不是log，防止模拟log测试方法数据混淆 */
@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())));
}
}
}
(一）kafka-jstorm集群实时日志分析 之 ---------kafka实时日志处理
标签：kafka jstorm 集群 实时日志分析
原文地址：http://blog.csdn.net/doctor_who2004/article/details/40461099
