1 目的

　　Spark Streaming整合Flume。参考官方整合文档（http://spark.apache.org/docs/2.2.0/streaming-flume-integration.html）

2 整合方式一：基于推

2.1 基本要求

• flume和spark一个work节点要在同一台机器上，flume会在本机器上通过配置的端口推送数据
• streaming应用必须先启动，receive必须要先监听推送数据的端口后，flume才能推送数据
• 添加如下依赖

 groupId = org.apache.spark artifactId = spark-streaming-flume_2.11 version = 2.2.0

2.2 配置Flume

　　我们知道flume 的使用就是如何配置它的配置文件，使用本地的netcat source来模拟数据，本次配置如下：

# Name the components on this agenta1.sources = r1a1.sinks = k1a1.channels = c1# Describe/configure the sourcea1.sources.r1.type = netcata1.sources.r1.bind = hadoopa1.sources.r1.port = 5900# Describe the sinka1.sinks.k1.type = avroa1.sinks.k1.hostname = hadoopa1.sinks.k1.port = 5901#a1.sinks.k1.type = logger# Use a channel which buffers events in memorya1.channels.c1.type = memory#a1.channels.c1.capacity = 1000#a1.channels.c1.transactionCapacity = 100# Bind the source and sink to the channela1.sources.r1.channels = c1a1.sinks.k1.channel = c1

2.3 在服务器上运行

思路如下：

• 用maven打包工程
• 使用saprk-submit提交
• 开启flume
• 发送模拟数据
• 验证

验证代码如下：功能简单的做一个单词统计：

 1 package flume_streaming 2  3 import org.apache.spark.SparkConf 4 import org.apache.spark.streaming.flume.FlumeUtils 5 import org.apache.spark.streaming.{Durations, StreamingContext} 6  7 /** 8  * @Author: SmallWild 9  * @Date: 2019/11/2 9:4210  * @Desc: 基于flumePushWordCount11 */12 object flumePushWordCount {13 def main(args: Array[String]): Unit = {14 if (args.length != 2) {15 System.err.println("错误参数，用法：flumePushWordCount <hostname> <port>")16 System.exit(1)17  }18 //传入参数19 val Array(hostname, port) = args20 //一定不能使用local[1]21 val sparkConf = new SparkConf() //.setMaster("local[2]").setAppName("kafkaDirectWordCount")22 val ssc = new StreamingContext(sparkConf, Durations.seconds(5))23 //设置日志级别24 ssc.sparkContext.setLogLevel("WARN")25 //TODO 简单的进行单词统计26 val flumeStream = FlumeUtils.createStream(ssc, hostname, port.toInt)27 flumeStream.map(x => new String(x.event.getBody.array()).trim)28 .flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _).print()29  ssc.start()30  ssc.awaitTermination()31  }32 }

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验证具体步骤如下：

 1 1）打包工程 2 mvn clean package -DskipTest 3 2)spark-submit提交（这里使用local模式） 4 ./spark-submit --class flume_streaming.flumePushWordCount / 5 --master local[2] / 6 --packages org.apache.spark:spark-streaming-flume_2.11:2.2.0 / 7 /smallwild/app/SparkStreaming-1.0.jar hadoop 5901 8 3）开启flume 9 flume-ng agent --name simple-agent --conf $FLUME_HOME/conf --conf-file $FLUME_HOME/conf -Dflume.root.logger=INFO,console10 4）发送模式数据11  这里使用本地5900端口发送数据12 telnet hadoop 590013 5）验证14 查看streaming应用程序是否能出现对应的单词计数字样

验证结果：能正确统计从端口发送过来的某一批次的单词的数量

3 整合方式二：基于拉（常用）

这种方式和上面基本一致

3.1 注意事项

• 先启动flume
• 使用自定义的sink，streaming主动去拉取数据，数据会先存放在缓冲区
• 事务保障机制，副本机制和数据被接收（Transactions succeed only after data is received and replicated by Spark Streaming.）
• 高容错保证
• 添加如下依赖

   groupId = org.apache.spark artifactId = spark-streaming-flume-sink_2.11 version = 2.2.0 groupId = org.scala-lang artifactId = scala-library version = 2.11.8 groupId = org.apache.commons artifactId = commons-lang3 version = 3.5

3.2 配置Flume

和前面差别在配置sink，需要使用自定义的sink

 1 # Name the components on this agent 2 a1.sources = r1 3 a1.sinks = k1 4 a1.channels = c1 5  6 # Describe/configure the source 7 a1.sources.r1.type = netcat 8 a1.sources.r1.bind = hadoop 9 a1.sources.r1.port = 590010 11 # Describe the sink12 a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink13 a1.sinks.k1.hostname = hadoop14 a1.sinks.k1.port = 590115 #a1.sinks.k1.type = logger16 17 # Use a channel which buffers events in memory18 a1.channels.c1.type = memory19 #a1.channels.c1.capacity = 100020 #a1.channels.c1.transactionCapacity = 10021 22 # Bind the source and sink to the channel23 a1.sources.r1.channels = c124 a1.sinks.k1.channel = c1

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3.3 在服务上运行

　　业务逻辑大致和前面一样，这里使用下面的类

 import org.apache.spark.streaming.flume._ val flumeStream = FlumeUtils.createPollingStream(streamingContext, [sink machine hostname], [sink port])

3.4 提交验证

思路如下：

• 用maven打包工程
• 开启flume
• 使用saprk-submit提交
• 发送模拟数据
• 验证

和前面基本一致

4 总结

　　整理两种整合flume的实践。