1、 安装flume
flume安装，解压后修改flume_env.sh配置文件，指定java_home即可。
cp hdfs jar包到flume lib目录下（否则无法抽取数据到hdfs上）：
$ cp /opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/share/hadoop/hdfs/hadoop-hdfs-2.5.0-cdh5.3.6.jar
/opt/cdh-5.3.6/flume-1.5.0-cdh5.3.6-bin/lib/
2、 spark streaming集成flume
2.1）编译spark，获得集成flume jar包：
说明：spark streaming集成flume或者kafka需要一些支持jar包，这些jar包在编译spark过程中会自动在external目录下生成相应的jar文件，因此，这里需要编译spark来获得这些jar包。
Spark streaming集成flume主要需要：spark-streaming-flume_2.10-1.3.0.jar包。
2.2）集成jar包
$mkdir –p /opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs
$cp spark-1.3.0/external/flume/target/spark-streaming-flume_2.10-1.3.0.jar
/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/
$ pwd
/opt/cdh-5.3.6/flume-1.5.0-cdh5.3.6-bin/lib
$ cp flume-avro-source-1.5.0-cdh5.3.6.jar flume-ng-sdk-1.5.0-cdh5.3.6.jar
/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/
$ cd /opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/
$ ll
flume-avro-source-1.5.0-cdh5.3.6.jar
flume-ng-sdk-1.5.0-cdh5.3.6.jar
spark-streaming-flume_2.10-1.3.0.jar
3、 编译flume配置文件（配置sources、channel、sink）：
$ cat flume-spark-push.conf
ss.sources = sss
ss.channels = ssc
ss.sinks = ssk
ss.sources.sss.type = exec
ss.sources.sss.command = tail -f /opt/cdh-5.3.6/flume-1.5.0-cdh5.3.6-bin/wctotal.log
ss.sources.sss.shell = /bin/bash -c
ss.channels.ssc.type = memory
ss.channels.ssc.capacity = 1000
ss.channels.ssc.transactionCapacity = 100
ss.sinks.ssk.type = avro
ss.sinks.ssk.hostname = chavin.king
ss.sinks.ssk.port = 9999
ss.sources.sss.channels = ssc
ss.sinks.ssk.channel = ssc
4、 编写spark streaming程序：
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.flume._
import org.apache.spark.storage.StorageLevel
val ssc = new StreamingContext(sc, Seconds(5))
// read data
val stream = FlumeUtils.createStream(ssc, "chavin.king", 9999, StorageLevel.MEMORY_ONLY_SER_2)
stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
ssc.start() // Start the computation
ssc.awaitTermination() // Wait for the computation to terminate
5、 spark-shell local模式测试spark streaming集成flume
$ bin/spark-shell --master local[2] --jars \
/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/spark-streaming-flume_2.10-1.3.0.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/flume-avro-source-1.5.0-cdh5.3.6.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/flume-ng-sdk-1.5.0-cdh5.3.6.jar
执行步骤4中程序：
scala> import org.apache.spark._
import org.apache.spark._
scala> import org.apache.spark.streaming._
import org.apache.spark.streaming._
scala> import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.StreamingContext._
scala> import org.apache.spark.streaming.flume._
import org.apache.spark.streaming.flume._
scala> import org.apache.spark.storage.StorageLevel
import org.apache.spark.storage.StorageLevel
scala> val ssc = new StreamingContext(sc, Seconds(5))
ssc: org.apache.spark.streaming.StreamingContext = org.apache.spark.streaming.StreamingContext@412dea3c
scala> val stream = FlumeUtils.createStream(ssc, "chavin.king", 9999, StorageLevel.MEMORY_ONLY_SER_2)
stream: org.apache.spark.streaming.dstream.ReceiverInputDStream[org.apache.spark.streaming.flume.SparkFlumeEvent] = org.apache.spark.streaming.flume.FlumeInputDStream@2bf9884
scala> stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
//输入以下命令启动spark streaming
scala> ssc.start()
scala> ssc.awaitTermination()
6、 启动flume
bin/flume-ng agent -c conf -n ss -f conf/flume-spark-push.conf -Dflume.root.logger=DEBUG,console
7、 测试spark streaming集成flume：
$ echo “hadoop oracle mysql” >>/opt/cdh-5.3.6/flume-1.5.0-cdh5.3.6-bin/wctotal.log
执行上边命令，可以在spark streaming命令行界面下看到如下内容：
-------------------------------------------
Time: 1499976790000 ms
-------------------------------------------
Received 1 flume events.
8、参考文档：http://spark.apache.org/docs/1.3.0/streaming-flume-integration.html
