streaming 整合 flume
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notes/Spark_Streaming整合Flume.md
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notes/Spark_Streaming整合Flume.md
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# Spark Straming 整合 Flume
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<nav>
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<a href="#一简介">一、简介</a><br/>
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<a href="#二推送式方法">二、推送式方法</a><br/>
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<a href="#21-配置日志收集Flume">2.1 配置日志收集Flume</a><br/>
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<a href="#22-项目依赖">2.2 项目依赖</a><br/>
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<a href="#23-Spark-Streaming接收日志数据">2.3 Spark Streaming接收日志数据</a><br/>
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<a href="#24-项目打包">2.4 项目打包</a><br/>
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<a href="#25-启动服务和提交作业">2.5 启动服务和提交作业</a><br/>
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<a href="#26-测试">2.6 测试</a><br/>
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<a href="#27-注意事项">2.7 注意事项</a><br/>
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<a href="#三拉取式方法">三、拉取式方法</a><br/>
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<a href="#31--配置日志收集Flume">3.1 配置日志收集Flume</a><br/>
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<a href="#22-新增依赖">2.2 新增依赖</a><br/>
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<a href="#23-Spark-Streaming接收日志数据">2.3 Spark Streaming接收日志数据</a><br/>
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<a href="#24-启动测试">2.4 启动测试</a><br/>
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</nav>
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## 一、简介
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Apache Flume是一个分布式，高可用的数据收集系统，可以从不同的数据源收集数据，经过聚合后发送到分布式计算框架或者存储系统中。Spark Straming提供了以下两种方式用于Flume的整合。
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## 二、推送式方法
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在推送式方法(Flume-style Push-based Approach)中，Spark Streaming程序需要对某台服务器的某个端口进行监听，Flume通过`avro Sink`将数据源源不断推送到该端口。
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这里以日志文件内容为例，将不断新增的日志文件内容推送到Streaming程序中，具体整合方式如下：
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### 2.1 配置日志收集Flume
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新建配置`netcat-memory-avro.properties`，使用`tail`命令监听文件内容变化，然后将新的文件内容通过`avro sink`发送到hadoop001这台服务器的8888端口：
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```properties
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#指定agent的sources,sinks,channels
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a1.sources = s1
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a1.sinks = k1
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a1.channels = c1
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#配置sources属性
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a1.sources.s1.type = exec
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a1.sources.s1.command = tail -F /tmp/log.txt
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a1.sources.s1.shell = /bin/bash -c
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a1.sources.s1.channels = c1
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#配置sink
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a1.sinks.k1.type = avro
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a1.sinks.k1.hostname = hadoop001
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a1.sinks.k1.port = 8888
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a1.sinks.k1.batch-size = 1
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a1.sinks.k1.channel = c1
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#配置channel类型
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a1.channels.c1.type = memory
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a1.channels.c1.capacity = 1000
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a1.channels.c1.transactionCapacity = 100
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```
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### 2.2 项目依赖
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项目采用Maven工程进行构建，主要依赖为`spark-streaming`和`spark-streaming-flume`。
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```xml
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<properties>
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<scala.version>2.11</scala.version>
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<spark.version>2.4.0</spark.version>
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</properties>
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<dependencies>
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<!-- Spark Streaming-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-streaming_${scala.version}</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!-- Spark Streaming整合Flume依赖-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-streaming-flume_${scala.version}</artifactId>
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<version>2.4.3</version>
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</dependency>
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</dependencies>
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```
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### 2.3 Spark Streaming接收日志数据
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调用 FlumeUtils工具类的`createStream`方法，对hadoop001的8888端口进行监听，获取到流数据并进行打印：
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```scala
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import org.apache.spark.SparkConf
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import org.apache.spark.streaming.{Seconds, StreamingContext}
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import org.apache.spark.streaming.flume.FlumeUtils
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object PushBasedWordCount {
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def main(args: Array[String]): Unit = {
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val sparkConf = new SparkConf()
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val ssc = new StreamingContext(sparkConf, Seconds(5))
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// 1.获取输入流
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val flumeStream = FlumeUtils.createStream(ssc, "hadoop001", 8888)
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// 2.打印输入流的数据
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flumeStream.map(line => new String(line.event.getBody.array()).trim).print()
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ssc.start()
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ssc.awaitTermination()
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}
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}
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```
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### 2.4 项目打包
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因为Spark安装目录下是不含有`spark-streaming-flume`依赖包的，所以在提交到集群运行时候必须提供该依赖包，你可以在提交命令中使用`--jar`指定上传到服务器的该依赖包，或者使用`--packages org.apache.spark:spark-streaming-flume_2.12:2.4.3`指定依赖包的完整名称，这样程序在启动时会先去中央仓库进行下载，这要求你的生产环境必须网络畅通。
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这里我采用的是第三种方式：使用`maven-shade-plugin`插件进行`ALL IN ONE`打包，把所有依赖的Jar一并打入最终包中。需要注意的是`spark-streaming`包在Spark安装目录的`jars`目录中已经提供，所以不需要打入。插件配置如下：
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> 关于大数据应用常用打包方式单独整理至：[大数据应用常用打包方式](https://github.com/heibaiying/BigData-Notes/blob/master/notes/大数据应用常用打包方式.md)
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>
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> 本项目完整源码见：[spark-streaming-flume](https://github.com/heibaiying/BigData-Notes/tree/master/code/spark/spark-streaming-flume)
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```xml
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<build>
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<plugins>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-compiler-plugin</artifactId>
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<configuration>
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<source>8</source>
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<target>8</target>
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</configuration>
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</plugin>
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<!--使用shade进行打包-->
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-shade-plugin</artifactId>
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<configuration>
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<createDependencyReducedPom>true</createDependencyReducedPom>
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<filters>
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<filter>
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<artifact>*:*</artifact>
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<excludes>
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<exclude>META-INF/*.SF</exclude>
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<exclude>META-INF/*.sf</exclude>
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<exclude>META-INF/*.DSA</exclude>
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<exclude>META-INF/*.dsa</exclude>
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<exclude>META-INF/*.RSA</exclude>
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<exclude>META-INF/*.rsa</exclude>
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<exclude>META-INF/*.EC</exclude>
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<exclude>META-INF/*.ec</exclude>
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<exclude>META-INF/MSFTSIG.SF</exclude>
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<exclude>META-INF/MSFTSIG.RSA</exclude>
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</excludes>
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</filter>
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</filters>
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<artifactSet>
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<excludes>
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<exclude>org.apache.spark:spark-streaming_${scala.version}</exclude>
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<exclude>org.scala-lang:scala-library</exclude>
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<exclude>org.apache.commons:commons-lang3</exclude>
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</excludes>
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</artifactSet>
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</configuration>
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<executions>
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<execution>
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<phase>package</phase>
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<goals>
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<goal>shade</goal>
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</goals>
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<configuration>
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<transformers>
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<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
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<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
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</transformer>
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</transformers>
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</configuration>
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</execution>
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</executions>
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</plugin>
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<!--打包.scala文件需要配置此插件-->
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<plugin>
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<groupId>org.scala-tools</groupId>
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<artifactId>maven-scala-plugin</artifactId>
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<version>2.15.1</version>
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<executions>
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<execution>
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<id>scala-compile</id>
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<goals>
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<goal>compile</goal>
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</goals>
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<configuration>
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<includes>
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<include>**/*.scala</include>
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</includes>
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</configuration>
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</execution>
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<execution>
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<id>scala-test-compile</id>
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<goals>
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<goal>testCompile</goal>
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</goals>
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</execution>
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</executions>
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</plugin>
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</plugins>
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</build>
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```
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使用`mvn clean package`命令打包后会生产以下两个Jar包，提交`非original`开头的Jar即可。
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-streaming-flume-jar.png"/> </div>
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### 2.5 启动服务和提交作业
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启动Flume服务：
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```shell
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flume-ng agent \
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--conf conf \
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--conf-file /usr/app/apache-flume-1.6.0-cdh5.15.2-bin/examples/netcat-memory-avro.properties \
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--name a1 -Dflume.root.logger=INFO,console
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```
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提交Spark Streaming作业：
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```shell
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spark-submit \
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--class com.heibaiying.flume.PushBasedWordCount \
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--master local[4] \
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/usr/appjar/spark-streaming-flume-1.0.jar
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```
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### 2.6 测试
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这里使用`echo`命令模拟日志产生的场景，往日志文件中追加数据，然后查看程序的输出：
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-flume-input.png"/> </div>
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Spark Streaming程序成功接收到数据并打印输出：
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-flume-console.png"/> </div>
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### 2.7 注意事项
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#### 1. 启动顺序
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这里需要注意的，不论你先启动Spark程序还是Flume程序，由于两者的启动都需要一定的时间，此时先启动的程序会短暂地抛出端口拒绝连接的异常，此时不需要进行任何操作，等待两个程序都启动完成即可。
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/flume-retry.png"/> </div>
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#### 2. 版本一致
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最好保证用于本地开发和编译的Scala版本和Spark的Scala版本一致，至少保证大版本一致，如都是`2.11`。
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<br/>
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## 三、拉取式方法
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拉取式方法(Pull-based Approach using a Custom Sink)是将数据推送到SparkSink接收器中，此时数据会保持缓冲状态，Spark Streaming定时从接收器中拉取数据。这种方式是基于事务的，即只有在Spark Streaming接收和复制数据完成后，才会删除缓冲的数据。与第一种方式相比，具有更强的可靠性和容错保证。整合步骤如下：
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### 3.1 配置日志收集Flume
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新建Flume配置文件`netcat-memory-sparkSink.properties`，配置和上面基本一致，只是把`a1.sinks.k1.type`的属性修改为`org.apache.spark.streaming.flume.sink.SparkSink`，即采用Spark接收器。
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```properties
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#指定agent的sources,sinks,channels
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a1.sources = s1
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a1.sinks = k1
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a1.channels = c1
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#配置sources属性
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a1.sources.s1.type = exec
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a1.sources.s1.command = tail -F /tmp/log.txt
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a1.sources.s1.shell = /bin/bash -c
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a1.sources.s1.channels = c1
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#配置sink
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a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
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a1.sinks.k1.hostname = hadoop001
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a1.sinks.k1.port = 8888
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a1.sinks.k1.batch-size = 1
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a1.sinks.k1.channel = c1
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#配置channel类型
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a1.channels.c1.type = memory
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a1.channels.c1.capacity = 1000
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a1.channels.c1.transactionCapacity = 100
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```
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### 2.2 新增依赖
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使用拉取式方法需要额外添加以下两个依赖：
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```xml
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<dependency>
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<groupId>org.scala-lang</groupId>
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<artifactId>scala-library</artifactId>
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<version>2.12.8</version>
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</dependency>
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<dependency>
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<groupId>org.apache.commons</groupId>
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<artifactId>commons-lang3</artifactId>
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<version>3.5</version>
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</dependency>
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```
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注意：添加这两个依赖只是为了本地开发测试，Spark的安装目录下已经提供了这两个依赖，所以在最终打包时需要进行排除。
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### 2.3 Spark Streaming接收日志数据
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这里和上面推送式方法的代码基本相同，只是将调用方法改为`createPollingStream`。
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```scala
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import org.apache.spark.SparkConf
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import org.apache.spark.streaming.{Seconds, StreamingContext}
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import org.apache.spark.streaming.flume.FlumeUtils
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object PullBasedWordCount {
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def main(args: Array[String]): Unit = {
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val sparkConf = new SparkConf()
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val ssc = new StreamingContext(sparkConf, Seconds(5))
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// 1.获取输入流
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val flumeStream = FlumeUtils.createPollingStream(ssc, "hadoop001", 8888)
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// 2.打印输入流中的数据
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flumeStream.map(line => new String(line.event.getBody.array()).trim).print()
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ssc.start()
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ssc.awaitTermination()
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}
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}
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```
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### 2.4 启动测试
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启动和提交作业流程与上面相同，这里给出执行脚本，过程不再赘述。
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启动Flume进行日志收集：
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```shell
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flume-ng agent \
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--conf conf \
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--conf-file /usr/app/apache-flume-1.6.0-cdh5.15.2-bin/examples/netcat-memory-sparkSink.properties \
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--name a1 -Dflume.root.logger=INFO,console
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```
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提交Spark Streaming作业：
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```shel
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spark-submit \
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--class com.heibaiying.flume.PullBasedWordCount \
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--master local[4] \
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/usr/appjar/spark-streaming-flume-1.0.jar
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```
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## 参考资料
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1. [streaming-flume-integration](https://spark.apache.org/docs/latest/streaming-flume-integration.html)
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