使用flume+kafka+storm构建实时日志分析系统

使用flume+kafka+storm构建实时日志分析系统

本文只会涉及flume和kafka的结合，kafka和storm的结合可以参考其他博客
1. flume安装使用
下载flume安装包http://www.apache.org/dyn/closer.cgi/flume/1.5.2/apache-flume-1.5.2-bin.tar.gz
解压$ tar -xzvf apache-flume-1.5.2-bin.tar.gz -C /opt/flume
flume配置文件放在conf文件目录下，执行文件放在bin文件目录下。
1）配置flume
进入conf目录将flume-conf.properties.template拷贝一份，并命名为自己需要的名字
$ cp flume-conf.properties.template flume.conf
修改flume.conf的内容，我们使用file sink来接收channel中的数据，channel采用memory channel，source采用exec source，配置文件如下：

agent.sources = seqGenSrc
agent.channels = memoryChannel
agent.sinks = loggerSink
# For each one of the sources, the type is defined
agent.sources.seqGenSrc.type = exec
agent.sources.seqGenSrc.command = tail -F /data/mongodata/mongo.log
#agent.sources.seqGenSrc.bind = 172.168.49.130
# The channel can be defined as follows.
agent.sources.seqGenSrc.channels = memoryChannel
# Each sink's type must be defined
agent.sinks.loggerSink.type = file_roll
agent.sinks.loggerSink.sink.directory = /data/flume
#Specify the channel the sink should use
agent.sinks.loggerSink.channel = memoryChannel
# Each channel's type is defined.
agent.channels.memoryChannel.type = memory
# Other config values specific to each type of channel(sink or source)
# can be defined as well
# In this case, it specifies the capacity of the memory channel
agent.channels.memoryChannel.capacity = 1000
agent.channels.memory4log.transactionCapacity = 100

2）运行flume agent
切换到bin目录下，运行一下命令：
$ ./flume-ng agent --conf ../conf -f ../conf/flume.conf --n agent -Dflume.root.logger=INFO,console
在/data/flume目录下可以看到生成的日志文件。

2. 结合kafka
由于flume1.5.2没有kafka sink，所以需要自己开发kafka sink
可以参考flume 1.6里面的kafka sink，但是要注意使用的kafka版本，由于有些kafka api不兼容的
这里只提供核心代码，process()内容。

Sink.Status status = Status.READY;
 

Channel ch = getChannel();
Transaction transaction = null;
Event event = null;
String eventTopic = null;
String eventKey = null;

try {
transaction = ch.getTransaction();
transaction.begin();
messageList.clear();

if (type.equals("sync")) {
event = ch.take();

 if (event != null) {
        byte[] tempBody = event.getBody();
 String eventBody = new String(tempBody,"UTF-8");
 Map headers = event.getHeaders();

 if ((eventTopic = headers.get(TOPIC_HDR)) == null) {
          eventTopic = topic;
 }

        eventKey = headers.get(KEY_HDR);

 if (logger.isDebugEnabled()) {
 logger.debug("{Event} " + eventTopic + " : " + eventKey + " : "
 + eventBody);
 }
 
        ProducerData data = new ProducerData
 (eventTopic, new Message(tempBody));
 
 long startTime = System.nanoTime();
 logger.debug(eventTopic+"++++"+eventBody);
        producer.send(data);
 long endTime = System.nanoTime(); 
 }
} else {
long processedEvents = 0;
for (; processedEvents < batchSize; processedEvents += 1) {
event = ch.take();

 if (event == null) {
 break;
 }

 byte[] tempBody = event.getBody();
 String eventBody = new String(tempBody,"UTF-8");
 Map headers = event.getHeaders();

 if ((eventTopic = headers.get(TOPIC_HDR)) == null) {
          eventTopic = topic;
 }

        eventKey = headers.get(KEY_HDR);

 if (logger.isDebugEnabled()) {
 logger.debug("{Event} " + eventTopic + " : " + eventKey + " : "
 + eventBody);
 logger.debug("event #{}", processedEvents);
 }

 // create a message and add to buffer
        ProducerData data = new ProducerData
 (eventTopic, eventBody);
        messageList.add(data);
}

// publish batch and commit.
 if (processedEvents > 0) {
 long startTime = System.nanoTime(); 
 long endTime = System.nanoTime(); 
 }
}

transaction.commit();
} catch (Exception ex) {
String errorMsg = "Failed to publish events";
logger.error("Failed to publish events", ex);
status = Status.BACKOFF;
if (transaction != null) {
try {
transaction.rollback(); 
} catch (Exception e) {
logger.error("Transaction rollback failed", e);
throw Throwables.propagate(e);
}
}
throw new EventDeliveryException(errorMsg, ex);
} finally {
if (transaction != null) {
transaction.close();
}
}

return status; 

下一步，修改flume配置文件，将其中sink部分的配置改成kafka sink，如：

producer.sinks.r.type = org.apache.flume.sink.kafka.KafkaSink
 
producer.sinks.r.brokerList = bigdata-node00:9092
producer.sinks.r.requiredAcks = 1
producer.sinks.r.batchSize = 100
#producer.sinks.r.kafka.producer.type=async
#producer.sinks.r.kafka.customer.encoding=UTF-8
producer.sinks.r.topic = testFlume1

type指向kafkasink所在的完整路径
下面的参数都是kafka的一系列参数，最重要的是brokerList和topic参数

现在重新启动flume，就可以在kafka的对应topic下查看到对应的日志