<Spark Streaming><Flume><Integration>

Overview

• Flume：一个分布式的，可靠的，可用的服务，用于有效地收集、聚合、移动大规模日志数据
• 我们搭建一个flume + Spark Streaming的平台来从Flume获取数据，并处理它。
• 有两种方法实现：使用flume-style的push-based方法，或者使用自定义的sink来实现pull-based方法。

Approach 1: Flume-style Push-based Approach

• flume被设计用来在Flume agents之间推信息，在这种方式下，Spark Streaming安装一个receiver that acts like an Avro agent for Flume, to which Flume can push the data.

General Requirement

• 当你启动flume + spark streaming应用时，该机器上必须运行一个Spark workers。
• flume可以向该机器的某一个port push数据。
• 基于这种push机制，streaming应用必须有一个receiver scheduled and listening on the chosen port.

Configuring Flume

• 配置flume以向Avro sink发送数据

• agent.sinks = avroSink
  agent.sinks.avroSink.type = avro
  agent.sinks.avroSink.channel = memoryChannel
  agent.sinks.avroSink.hostname = <chosen machine's hostname>
  agent.sinks.avroSink.port = <chosen port on the machine>

Configuring Spark Streaming Application

1. Linking: 在maven项目中配置依赖

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming-flume-sink_2.10</artifactId>
    <version>2.1.0</version>
</dependency>

　　2. Programming：import FlumeUtils, 创建input DStream

 import org.apache.spark.streaming.flume._

 val flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])

• 注意：应该与cluster中的resourceManager使用同一个hostname，这样的话资源分配可以匹配names，并在正确的机器上launch receiver
• 一个简单的Spark Streaming统计Flume event个数的demo代码：

• object FlumeEventCount {
    def main(args: Array[String]) {
      if (args.length < 2) {
        System.err.println(
          "Usage: FlumeEventCount <host> <port>")
        System.exit(1)
      }
  
      StreamingExamples.setStreamingLogLevels()
  
      val Array(host, IntParam(port)) = args
  
      val batchInterval = Milliseconds(2000)
  
      // Create the context and set the batch size
      val sparkConf = new SparkConf().setAppName("FlumeEventCount")
      val ssc = new StreamingContext(sparkConf, batchInterval)
  
      // Create a flume stream
      val stream = FlumeUtils.createStream(ssc, host, port, StorageLevel.MEMORY_ONLY_SER_2)
  
      // Print out the count of events received from this server in each batch
      stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
  
      ssc.start()
      ssc.awaitTermination()
    }
  }