文章来自： http://lxw1234.com/?p=217
软件环境：
flume-ng-core-1.4.0-cdh5.0.0
spark-1.2.0-bin-hadoop2.3
流程说明：
- Spark Streaming: 使用spark-streaming-flume_2.10-1.2.0插件，启动一个avro source，用来接收数据，并做相应的处理；
- Flume agent：source监控本地文件系统的一个目录，当文件发生变化时候，由avro sink发送至Spark Streaming的监听端口
Flume配置：
flume-lxw-conf.properties
#-->设置sources名称 agent_lxw.sources = sources1 #--> 设置channel名称 agent_lxw.channels = fileChannel #--> 设置sink 名称 agent_lxw.sinks = sink1 # source 配置 ## 一个自定义的Source，实现类似tail -f 的功能，比exec source更可靠 agent_lxw.sources.sources1.type = org.apache.flume.source.taildirectory.DirectoryTailSource agent_lxw.sources.sources1.dirs = lxwlog ## 监控的目录 agent_lxw.sources.sources1.dirs.lxwlog.path = file:///tmp/lxw-source #监控文件的正则规则，此正则用java的正则 agent_lxw.sources.sources1.dirs.lxwlog.file-pattern = ^lxw_.*log$ agent_lxw.sources.sources1.first-line-pattern = ^(.*)$ agent_lxw.sources.sources1.channels = fileChannel # sink 1 配置 将数据发送至slave004.lxw1234.com的44444端口 agent_lxw.sinks.sink1.type = avro agent_lxw.sinks.sink1.hostname = slave004.lxw1234.com agent_lxw.sinks.sink1.port = 44444 agent_lxw.sinks.sink1.channel = fileChannel agent_lxw.sinks.sink1.batch-size = 500 agent_lxw.sinks.sink1.connect-timeout = 40000 agent_lxw.sinks.sink1.request-timeout = 40000 agent_lxw.channels.fileChannel.type = file #-->检测点文件所存储的目录 agent_lxw.channels.fileChannel.checkpointDir = /tmp/flume/checkpoint/site #-->数据存储所在的目录设置 agent_lxw.channels.fileChannel.dataDirs = /tmp/flume/data/site #-->隧道的最大容量 agent_lxw.channels.fileChannel.capacity = 10000 #-->事务容量的最大值设置 agent_lxw.channels.fileChannel.transactionCapacity = 100
Spark Streaming程序：
Spark_Flume.scala
package com.lxw.test
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.flume.FlumeUtils
object Spark_Flume {
def main (args : Array[String]) {
if(args.length < 2) {
println("Usage: Spark_Flume <hostname> <port>")
System.exit(1)
}
val hostname = args(0)
val port = Integer.parseInt(args(1))
val sc = new SparkContext(new SparkConf().setAppName("Spark_Flume"))
val ssc = new StreamingContext(sc, Seconds(10))
val flumeStream = FlumeUtils.createStream(ssc, hostname, port,StorageLevel.MEMORY_AND_DISK)
flumeStream.map(e => "Event:header:" + e.event.get(0).toString + "body: " + new String(e.event.getBody.array)).print()
ssc.start()
ssc.awaitTermination()
}
}
启动：
- 先启动Spark Streaming程序：
./spark-submit \ --name "spark-flume" \ --master spark://192.168.1.130:7077 \ --executor-memory 1G \ --class com.lxw.test.Spark_Flume \ /home/liuxiaowen/spark-flume.jar slave004.lxw1234.com 44444
- 再启动Flume agent:
flume-ng agent -n agent_lxw --conf . -f flume-lxw-conf.properties