spark集群配置以及java操作spark小demo

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 + "
").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
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原文地址：https://www.cnblogs.com/ye-hcj/p/10280114.html