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 Android教程網 >> Android技術 >> Android開發 >> 關於android開發 >> 使用flume+kafka+storm構建實時日志分析系統

使用flume+kafka+storm構建實時日志分析系統

編輯:關於android開發

使用flume+kafka+storm構建實時日志分析系統


本文只會涉及flume和kafka的結合,kafka和storm的結合可以參考其他博客
1. flume安裝使用
下載flume安裝包http://www.apache.org/dyn/closer.cgi/flume/1.5.2/apache-flume-1.5.2-bin.tar.gz
解壓$ tar -xzvf apache-flume-1.5.2-bin.tar.gz -C /opt/flume
flume配置文件放在conf文件目錄下,執行文件放在bin文件目錄下。
1)配置flume
進入conf目錄將flume-conf.properties.template拷貝一份,並命名為自己需要的名字
$ cp flume-conf.properties.template flume.conf
修改flume.conf的內容,我們使用file sink來接收channel中的數據,channel采用memory channel,source采用exec source,配置文件如下:
  1. agent.sources = seqGenSrc
  2. agent.channels = memoryChannel
  3. agent.sinks = loggerSink
  4. # For each one of the sources, the type is defined
  5. agent.sources.seqGenSrc.type = exec
  6. agent.sources.seqGenSrc.command = tail -F /data/mongodata/mongo.log
  7. #agent.sources.seqGenSrc.bind = 172.168.49.130
  8. # The channel can be defined as follows.
  9. agent.sources.seqGenSrc.channels = memoryChannel
  10. # Each sink's type must be defined
  11. agent.sinks.loggerSink.type = file_roll
  12. agent.sinks.loggerSink.sink.directory = /data/flume
  13. #Specify the channel the sink should use
  14. agent.sinks.loggerSink.channel = memoryChannel
  15. # Each channel's type is defined.
  16. agent.channels.memoryChannel.type = memory
  17. # Other config values specific to each type of channel(sink or source)
  18. # can be defined as well
  19. # In this case, it specifies the capacity of the memory channel
  20. agent.channels.memoryChannel.capacity = 1000
  21. agent.channels.memory4log.transactionCapacity = 100
2)運行flume agent
切換到bin目錄下,運行一下命令:
$ ./flume-ng agent --conf ../conf -f ../conf/flume.conf --n agent -Dflume.root.logger=INFO,console
在/data/flume目錄下可以看到生成的日志文件。

2. 結合kafka
由於flume1.5.2沒有kafka sink,所以需要自己開發kafka sink
可以參考flume 1.6裡面的kafka sink,但是要注意使用的kafka版本,由於有些kafka api不兼容的
這裡只提供核心代碼,process()內容。

  1. Sink.Status status = Status.READY;

  2. Channel ch = getChannel();
  3. Transaction transaction = null;
  4. Event event = null;
  5. String eventTopic = null;
  6. String eventKey = null;

  7. try {
  8. transaction = ch.getTransaction();
  9. transaction.begin();
  10. messageList.clear();

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

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

  17. if ((eventTopic = headers.get(TOPIC_HDR)) == null) {
  18. eventTopic = topic;
  19. }

  20. eventKey = headers.get(KEY_HDR);

  21. if (logger.isDebugEnabled()) {
  22. logger.debug("{Event} " + eventTopic + " : " + eventKey + " : "
  23. + eventBody);
  24. }

  25. ProducerData<String, Message> data = new ProducerData<String, Message>
  26. (eventTopic, new Message(tempBody));

  27. long startTime = System.nanoTime();
  28. logger.debug(eventTopic+"++++"+eventBody);
  29. producer.send(data);
  30. long endTime = System.nanoTime();
  31. }
  32. } else {
  33. long processedEvents = 0;
  34. for (; processedEvents < batchSize; processedEvents += 1) {
  35. event = ch.take();

  36. if (event == null) {
  37. break;
  38. }

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

  42. if ((eventTopic = headers.get(TOPIC_HDR)) == null) {
  43. eventTopic = topic;
  44. }

  45. eventKey = headers.get(KEY_HDR);

  46. if (logger.isDebugEnabled()) {
  47. logger.debug("{Event} " + eventTopic + " : " + eventKey + " : "
  48. + eventBody);
  49. logger.debug("event #{}", processedEvents);
  50. }

  51. // create a message and add to buffer
  52. ProducerData<String, String> data = new ProducerData<String, String>
  53. (eventTopic, eventBody);
  54. messageList.add(data);
  55. }

  56. // publish batch and commit.
  57. if (processedEvents > 0) {
  58. long startTime = System.nanoTime();
  59. long endTime = System.nanoTime();
  60. }
  61. }

  62. transaction.commit();
  63. } catch (Exception ex) {
  64. String errorMsg = "Failed to publish events";
  65. logger.error("Failed to publish events", ex);
  66. status = Status.BACKOFF;
  67. if (transaction != null) {
  68. try {
  69. transaction.rollback();
  70. } catch (Exception e) {
  71. logger.error("Transaction rollback failed", e);
  72. throw Throwables.propagate(e);
  73. }
  74. }
  75. throw new EventDeliveryException(errorMsg, ex);
  76. } finally {
  77. if (transaction != null) {
  78. transaction.close();
  79. }
  80. }

  81. return status;
下一步,修改flume配置文件,將其中sink部分的配置改成kafka sink,如:

  1. producer.sinks.r.type = org.apache.flume.sink.kafka.KafkaSink
  2. producer.sinks.r.brokerList = bigdata-node00:9092
  3. producer.sinks.r.requiredAcks = 1
  4. producer.sinks.r.batchSize = 100
  5. #producer.sinks.r.kafka.producer.type=async
  6. #producer.sinks.r.kafka.customer.encoding=UTF-8
  7. producer.sinks.r.topic = testFlume1
type指向kafkasink所在的完整路徑
下面的參數都是kafka的一系列參數,最重要的是brokerList和topic參數

現在重新啟動flume,就可以在kafka的對應topic下查看到對應的日志

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