Spark学习笔记-Streaming-Flume
Spark Streaming 与 Flume集成有两种模式：
1、基于推模式
Flume 配置文件如下：
a1.sources = r1 a1.channels = c1 a1.sinks = k1 a1.sources.r1.type = netcat a1.sources.r1.bind = centos.host1 a1.sources.r1.port = 22222 a1.sources.r1.channels = c1 a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.sinks.k1.type = avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname = localhost a1.sinks.k1.port = 11111
当然也可以配置其他source，比如exec、thrift、avro等
a1.sources.r1.type = avro a1.sources.r1.bind = localhost a1.sources.r1.port = 22222 a1.sources.r1.channels = c1
Spark 代码如下：
import org.apache.spark.streaming.flume._
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.storage.StorageLevel
object SparkStreamingFlume1 {
def main(args: Array[String]) {
if (args.length < 2) {
print("please enter host and port")
System.exit(1)
}
val sc = new SparkContext("spark://centos.host1:7077", "Spark Streaming Flume Integration")
//创建StreamingContext，20秒一个批次
val ssc = new StreamingContext(sc, Seconds(20))
val hostname = args(0)
val port = args(1).toInt
val storageLevel = StorageLevel.MEMORY_ONLY
val flumeStream = FlumeUtils.createStream(ssc, hostname, port, storageLevel)
flumeStream.count().map(cnt => "Received " + cnt + " flume events." ).print()
//开始运行
ssc.start()
//计算完毕退出
ssc.awaitTermination()
sc.stop()
}
}
提交Spark，这里需要注意的添加必要的jar包，可以在提交的时候加上--jars来指定相关的jar包，也可以在sc中调用addJar()添加
[hadoop@centos spark-1.1.0-bin-hadoop2.4]$bin/spark-submit --class org.project.modules.streaming.SparkStreamingFlume1 --jars lib/spark-streaming-flume-sink_2.10-1.1.0.jar,lib/spark-examples-1.1.0-hadoop2.4.0.jar --master spark://centos.host1:7077 /home/hadoop/temp/flume.jar centos.host1 11111
启动flume
[hadoop@centos flume-1.5.0.1]$bin/flume-ng agent --conf conf --conf-file conf/example5.properties --name a1 -Dflume.root.logger=INFO,console
source类型是netcat的话，可以通过telnet centos.host1 22222测试，如果是avro类型的话，可以通过flume内部提供了一个avro client测试，方式如下
[hadoop@centos flume-1.5.0.1]$bin/flume-ng avro-client --conf conf -H localhost -p 44444 -F a.xml -Dflume.root.logger=DEBUG,console
2、基于拉模式
Flume 配置文件如下：
a1.sources = r1 a1.channels = c1 a1.sinks = k1 a1.sources.r1.type = netcat a1.sources.r1.bind = centos.host1 a1.sources.r1.port = 22222 a1.sources.r1.channels = c1 a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink a1.sinks.k1.hostname = centos.host1 a1.sinks.k1.port = 11111 a1.sinks.k1.channel = c1
Spark代码如下：
import org.apache.spark.streaming.flume._
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.storage.StorageLevel
object SparkStreamingFlume2 {
def main(args: Array[String]) {
if (args.length < 2) {
print("please enter host and port")
System.exit(1)
}
val sc = new SparkContext("spark://centos.host1:7077", "Spark Streaming Flume Integration")
//创建StreamingContext，20秒一个批次
val ssc = new StreamingContext(sc, Seconds(20))
val hostname = args(0)
val port = args(1).toInt
val storageLevel = StorageLevel.MEMORY_ONLY
val flumeStream = FlumeUtils.createPollingStream(ssc, hostname, port, storageLevel)
flumeStream.count().map(cnt => "Received " + cnt + " flume events." ).print()
//开始运行
ssc.start()
//计算完毕退出
ssc.awaitTermination()
sc.stop()
}
}
启动Flume，这里主要需要添加 scala-library.jar 、spark-streaming-flume-sink_2.10-1.1.0.jar 到$FLUME_HOME/lib目录下
[hadoop@centos flume-1.5.0.1]$bin/flume-ng agent --conf conf --conf-file conf/example6.properties --name a1 -Dflume.root.logger=INFO,console
可以通过telnet centos.host1 22222测试
提交Spark，这里需要注意的和上面一样添加必要的jar包
[hadoop@centos spark-1.1.0-bin-hadoop2.4]$bin/spark-submit --class org.project.modules.streaming.SparkStreamingFlume2 --jars lib/spark-streaming-flume-sink_2.10-1.1.0.jar,lib/spark-examples-1.1.0-hadoop2.4.0.jar --master spark://centos.host1:7077 /home/hadoop/temp/flume.jar centos.host1 11111