------------------------------------------SparkStreaming第二种方式整合Flume-----------------------------------
详细学习地址：https://spark.apache.org/docs/latest/streaming-flume-integration.html
注意这种方式相比第一种方式更可靠，支持容错，工作环境中更常用（事务机制保障），条件是接收到，而且有副本才会去处理
Pull-based Approach using a Custom Sink
Pull方式整合
步骤一：编写flume配置文件（建议参考官方文档）
Flume Agent的编写： flume_pull_streaming.conf
simple-agent.sources = netcat-source
simple-agent.sinks = spark-sink
simple-agent.channels = memory-channel
simple-agent.sources.netcat-source.type = netcat
simple-agent.sources.netcat-source.bind = hadoop000
simple-agent.sources.netcat-source.port = 44444
simple-agent.sinks.spark-sink.type = org.apache.spark.streaming.flume.sink.SparkSink
simple-agent.sinks.spark-sink.hostname = hadoop000
simple-agent.sinks.spark-sink.port = 41414
simple-agent.channels.memory-channel.type = memory
simple-agent.sources.netcat-source.channels = memory-channel
simple-agent.sinks.spark-sink.channel = memory-channel
步骤二：编写测试类（wordcount）
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测试类：
package com.imooc.spark
import org.apache.spark.SparkConf
import org.apache.spark.streaming.flume.FlumeUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Spark Streaming整合Flume的第二种方式
*/
object FlumePullWordCount {
def main(args: Array[String]): Unit = {
if(args.length != 2) {
System.err.println("Usage: FlumePullWordCount <hostname> <port>")
System.exit(1)
}
val Array(hostname, port) = args
val sparkConf = new SparkConf() //.setMaster("local[2]").setAppName("FlumePullWordCount")
val ssc = new StreamingContext(sparkConf, Seconds(5))
//TODO... 如何使用SparkStreaming整合Flume
val flumeStream = FlumeUtils.createPollingStream(ssc, hostname, port.toInt)
flumeStream.map(x=> new String(x.event.getBody.array()).trim)
.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()
ssc.start()
ssc.awaitTermination()
}
}
pom.xml依赖：
<!-- Spark Streaming整合Flume 依赖-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume-sink_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>3.5</version>
</dependency>
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本地联调：
注意点：先启动flume 后启动Spark Streaming应用程序
注意在idea配置参数，edit configurations ----->programe arguments hadoop000 41414
flume-ng agent \--name simple-agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/flume_pull_streaming.conf \
-Dflume.root.logger=INFO,console
spark-submit \
--class com.imooc.spark.FlumePullWordCount \
--master local[2] \
--packages org.apache.spark:spark-streaming-flume_2.11:2.2.0 \
/home/hadoop/lib/sparktrain-1.0.jar \
hadoop000 41414