spark集群配置以及java操作spark小demo

• spark
  • 安装
  • 配置
  • 使用java来操作spark

spark

安装

tar -zxvf spark-2.4.0-bin-hadoop2.7.tgz
rm spark-2.4.0-bin-hadoop2.7.tgz
mv spark-2.4.0-bin-hadoop2.7 spark

sudo vim /etc/profile
    export SPARK_HOME=/usr/local/storm
    export PATH=$PATH:$SPARK_HOME/bin

source /etc/profile

准备 master worker1 worker2 worker3 这四台机器

首先确保你的Hadoop集群能够正常运行worker1 worker2 worker3为DataNode, master为NameNode
具体配置参照我的博客https://www.cnblogs.com/ye-hcj/p/10192857.html

配置

1. spark-env.sh

   进入spark的conf目录下，cp spark-env.sh.template spark-env.sh
   
   sudo vim spark-env.sh
       输入如下配置
       export JAVA_HOME=/usr/local/jdk/jdk-11.0.1
       export SCALA_HOME=/usr/local/scala/scala
       export HADOOP_HOME=/usr/local/hadoop/hadoop-3.1.1
       export SPARK_HOME=/usr/local/spark/spark
       export HADOOP_CONF_DIR=/usr/local/hadoop/hadoop-3.1.1/etc/hadoop
       export SPARK_MASTER_HOST=master
       export SPARK_WORKER_MEMORY=1g
       export SPARK_WORKER_CORES=1
   

2. slaves

   进入spark的conf目录下，cp slaves.template slaves
   
   sudo vim slaves
       输入如下配置
       master
       worker1
       worker2
       worker3
   

3. 启动

   在master中运行 sbin/start-all.sh 即可
   
   访问http://master:8080/即可看到spark的ui
   

使用java来操作spark

写个小demo,用来分析10万个数据中男女人数

1. 模拟数据的java代码

   // 模拟数据
   // 10万个人当中，统计青年男性和青年女性的比例，看看男女比例是否均衡
   FileOutputStream f = null;
   ThreadLocalRandom random = ThreadLocalRandom.current();
   String str = "";
   int count = 0;
   try {
       f = new FileOutputStream("C:\\Users\\26401\\Desktop\\data.txt", true);
       for(;count<100000;count++) {
           str = count + " " + random.nextInt(18, 28) + " " + (random.nextBoolean()?'M':'F');
           f.write((str + "\r\n").getBytes());
       }
   
   } catch (Exception e) {
       e.printStackTrace();
   } finally {
       try {
           if(f != null) f.close();
       } catch (IOException e) {
           e.printStackTrace();
       }
   }
   

2. 依赖

   <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
       <modelVersion>4.0.0</modelVersion>
       <groupId>test</groupId>
       <artifactId>test</artifactId>
       <version>1.0.0</version>
       <name>test</name>
       <description>Test project for spring boot mybatis</description>
       <packaging>jar</packaging>
   
       <properties>
           <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
           <maven.compiler.encoding>UTF-8</maven.compiler.encoding>
           <java.version>1.8</java.version>
           <maven.compiler.source>1.8</maven.compiler.source>
           <maven.compiler.target>1.8</maven.compiler.target>
       </properties> 
   
       <dependencies>
   
           <dependency>
               <groupId>org.apache.spark</groupId>
               <artifactId>spark-core_2.12</artifactId>
               <version>2.4.0</version>
           </dependency>
   
           <dependency>
               <groupId>org.slf4j</groupId>
               <artifactId>slf4j-api</artifactId>
               <version>1.7.25</version>
           </dependency>
   
           <dependency>
               <groupId>junit</groupId>
               <artifactId>junit</artifactId>
               <version>3.8.1</version>
           </dependency>
   
       </dependencies>
   
       <build>
           <plugins>
               <plugin>
                   <groupId>org.apache.maven.plugins</groupId>
                   <artifactId>maven-jar-plugin</artifactId>
                   <configuration>
                       <archive>
                           <manifest>
                               <addClasspath>true</addClasspath>
                               <useUniqueVersions>false</useUniqueVersions>
                               <classpathPrefix>lib/</classpathPrefix>
                           </manifest>
                       </archive>
                   </configuration>
               </plugin>
           </plugins>
       </build>
   </project>
   

3. java代码

   package test;
   
   import java.io.Serializable;
   
   import org.apache.spark.SparkConf;
   import org.apache.spark.api.java.JavaRDD;
   import org.apache.spark.api.java.JavaSparkContext;
   import org.apache.spark.api.java.function.Function;
   import org.slf4j.Logger;
   import org.slf4j.LoggerFactory;
   
   public class App implements Serializable
   {
   
       private static final long serialVersionUID = -7114915627898482737L;
   
       public static void main(String[] args) throws Exception {
           Logger logger=LoggerFactory.getLogger(App.class);
   
           SparkConf sparkConf = new SparkConf();
   
           sparkConf.setMaster("spark://master:7077");
           sparkConf.set("spark.submit.deployMode", "cluster");
           sparkConf.setAppName("FirstTest");
   
           JavaSparkContext sc = new JavaSparkContext(sparkConf);
           JavaRDD<String> file = sc.textFile("hdfs://master:9000/data.txt");
   
           JavaRDD<String> male = file.filter(new Function<String, Boolean>() {
               private static final long serialVersionUID = 1L;
   
               @Override
               public Boolean call(String s) throws Exception {
                   logger.info(s);
                   return s.contains("M");
               }
           });
           logger.info("**************************************");
           logger.info(male.count()+""); // 49991
           logger.info("**************************************");
           sc.close();
   
           // 其他的api请自行查阅，很简单，不想看，可以自己瞎点
       }
   }
   

4. 运行

   1. 将生成的测试数据data.txt上传至hdfs
   2. 将打包的jar上传到master机器
   3. 运行 bin/spark-submit --master spark://master:7077 --class test.App test-1.0.0.jar
   4. 进入spark的ui界面可以清楚的看到打印的消息