Spark Streaming继承Flume

  Spark Streaming通过Push和Pull两种方式对接Flume数据源。以Spark Streaming的角度来看，Push方式属于推送(由Flume向Spark推送)，而Pull属于拉取(Spark拉取Flume的输出)。
  不论以何种方式，开发过程类似，都是由 Spark Streaming 对接 Flume 数据流，Flume 做为 Spark Streaming 的数据源。Push 和 Pull 两者的差别主要体现在Flume Sink 的不同，而 Flume Source 与 Channel 不会受影响。

Push方式

Push方式：Flume架构为netcat->memory->avro

1.编写Streaming数据简单处理的代码

pom文件maven依赖：

<dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-flume -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming-flume_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>

  </dependencies>

简单数据处理代码：

object FlumeSparkStreamingDemo {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("mytest")
    val ssc = new StreamingContext(conf,Seconds(5))

    val flumeStream = FlumeUtils.createStream(ssc,"192.168.56.101",55555)
    flumeStream.map(x=>new String(x.event.getBody.array()).trim).flatMap(x=>x.split(" ")).map((_,1)).reduceByKey(_+_).print()


    ssc.start()
    ssc.awaitTermination()
    }
}

2.将以上idea项目打包上传到Linux运行，因为Flume在Linux上。

scala项目打包方式的pom配置在我之前的博客里写过，有需要的可以去看，用默认的pom文件打包不能将scala项目打包，所以必须要换pom文件里的build配置

spark项目上传到Linux上运行的命令

spark-submit \
--class beijing.FlumeSparkStreamingDemo \             //改成自己的主类
--packages org.apache.spark:spark-streaming-flume_2.11:2.3.4 \
/opt/kafkasparkstreaming-1.0-SNAPSHOT.jar             //改成自己jar包所在的位置

3.Flume配置文件

a1.sources=s1
a1.sinks=k1
a1.channels=c1

a1.sources.s1.type=netcat
a1.sources.s1.bind=192.168.56.101
a1.sources.s1.port=44444


a1.channels.c1.type=memory
a1.channels.c1.capacity=1000

a1.sinks.k1.type=avro
a1.sinks.k1.hostname=192.168.56.101
a1.sinks.k1.port=55555

a1.sources.s1.channels=c1
a1.sinks.k1.channel=c1

4.启动程序运行

先启动spark，再启动idea项目，然后启动flume，最后启动44444端口，即可运行

Pull方式

Pull方式：Flume架构为：netcat->memory->SparkSink

1.编写Streaming数据简单处理的代码

pom文件maven依赖同上

object FlumeSparkStreamingDemo {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("mytest")
    val ssc = new StreamingContext(conf,Seconds(5))

    val flumeStream = FlumeUtils.createPollingStream(ssc,"192.168.56.101",55555)
    flumeStream.map(x=>new String(x.event.getBody.array()).trim).flatMap(x=>x.split(" ")).map((_,1)).reduceByKey(_+_).print()


    ssc.start()
    ssc.awaitTermination()
    }
}

2.将以上idea项目打包上传到Linux运行，因为Flume在Linux上。

scala项目打包方式的pom配置在我之前的博客里写过，有需要的可以去看，用默认的pom文件打包不能将scala项目打包，所以必须要换pom文件里的build配置

上传spark项目到Linux运行命令同上

3.Flume配置文件

a1.sources=s1
a1.sinks=k1
a1.channels=c1

a1.sources.s1.type=netcat
a1.sources.s1.bind=192.168.56.101
a1.sources.s1.port=44444

a1.channels.c1.type=memory
a1.channels.c1.capacity=1000

a1.sinks.k1.type=org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.hostname=192.168.56.101
a1.sinks.k1.port=55555

a1.sources.s1.channels=c1
a1.sinks.k1.channel=c1

4.注意：启动顺序和Push方式不同

先启动spark，再启动Flume，再运行idea项目，最后启动44444端口，即可运行

注意：pull方式读取Flume数据时需要将以下三个jar放到Flume文件夹下的lib目录中。并将原有的scala-library.jar删除。

jar包资源：
https://pan.baidu.com/s/19EMtXgf5N2QQf4lqSoNfPw
提取码：yrtx