SparkStreaming整合flume

在实际开发中push会丢数据，因为push是由flume将数据发给程序，程序出错，丢失数据。所以不会使用不做讲解，这里讲解poll，拉去flume的数据，保证数据不丢失。

1.首先你得有flume

这里使用的flume的版本是apache1.6 cdh公司集成

这里需要下载

(1).我这里是将spark-streaming-flume-sink_2.11-2.0.2.jar放入到flume的lib目录下

cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/lib

　　(ps:我的flume安装目录，使用ftp工具上传上去,我使用的是finalShell支持ssh也支持ftp(需要的小伙伴，点我下载))

(2)修改flume/lib下的scala依赖包(保证版本一致)

我这里是将spark中jar安装路径的scala-library-2.11.8.jar替换掉flume下的scala-library-2.10.5.jar

删除scala-library-2.10.5.jar

rm -rf /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/lib/scala-library-2.10.5.jar

复制scala-library-2.11.8.jar

cp /export/servers/spark-2.0.2/jars/scala-library-2.11.8.jar /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/lib/

(3)编写flume-poll.conf文件

创建目录

mkdir /export/data/flume

创建配置文件

vim /export/logs/flume-poll.conf

编写配置，标注发绿光的地方需要注意更改为自己本机的(flume是基于配置执行任务）

a1.sources = r1
a1.sinks = k1
a1.channels = c1
#source
a1.sources.r1.channels = c1
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /export/data/flume
a1.sources.r1.fileHeader = true
#channel
a1.channels.c1.type =memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity=5000
#sinks
a1.sinks.k1.channel = c1
a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.hostname=192.168.52.110
a1.sinks.k1.port = 8888
a1.sinks.k1.batchSize= 2000

底行模式wq保存退出

执行flume

flume-ng agent -n a1 -c /opt/bigdata/flume/conf -f /export/logs/flume-poll.conf -Dflume.root.logger=INFO,console

在监视的/export/data/flume下放入文件　　　　　　　　　　　　　　　　　　　　(黄色对应的是之前创建的配置文件)

执行成功

代表你flume配置没有问题，接下来开始编写代码

1.导入相关依赖

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming-flume_2.11</artifactId>
    <version>2.0.2</version>
</dependency>

2.编码

package SparkStreaming
import SparkStreaming.DefinedFunctionAdds.updateFunc
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.flume.{FlumeUtils, SparkFlumeEvent}
object SparkStreamingFlume {
  def main(args: Array[String]): Unit = {
    //创建sparkContext
    val conf: SparkConf = new SparkConf().setAppName("DefinedFunctionAdds").setMaster("local[2]")
    val sc = new SparkContext(conf)
    //去除多余的log，提高可视率
    sc.setLogLevel("WARN")
    //创建streamingContext
    val scc = new StreamingContext(sc,Seconds(5))
    //设置备份
    scc.checkpoint("./flume")
    //receive(task)拉取数据
    val num1: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createPollingStream(scc,"192.168.52.110",8888)
    //获取flume中的body
    val value: DStream[String] = num1.map(x=>new String(x.event.getBody.array()))
    //切分处理,并附上数值1
    val result: DStream[(String, Int)] = value.flatMap(_.split(" ")).map((_,1))
    //结果累加
    val result1: DStream[(String, Int)] = result.updateStateByKey(updateFunc)
    result1.print()
    //启动并阻塞
    scc.start()
    scc.awaitTermination()
  }
  def updateFunc(currentValues:Seq[Int], historyValues:Option[Int]):Option[Int] = {
    val newValue: Int = currentValues.sum+historyValues.getOrElse(0)
    Some(newValue)
  }
}