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
    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<String, String> 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<String, Message> data = new ProducerData<String, Message>
         (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<String, String> 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<String, String> data = new ProducerData<String, String>
         (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