提交任务到Spark

栏目: 编程工具 · 发布时间: 7年前

内容简介：提交任务到Spark

1.场景

在搭建好Hadoop+Spark环境后，现准备在此环境上提交简单的任务到Spark进行计算并输出结果。搭建过程： http://www.linuxidc.com/Linux/2017-06/144926.htm

本人比较熟悉 Java 语言，现以Java的WordCount为例讲解这整个过程，要实现计算出给定文本中每个单词出现的次数。

2.环境测试

在讲解例子之前，我想先测试一下之前搭建好的环境。

2.1测试Hadoop环境

首先创建一个文件wordcount.txt 内容如下：

Hello hadoop
hello spark
hello bigdata
yellow banana
red apple

然后执行如下命令：

hadoop fs -mkdir -p /Hadoop/Input （在HDFS创建目录）

hadoop fs -put wordcount.txt /Hadoop/Input （将wordcount.txt文件上传到HDFS）

hadoop fs -ls /Hadoop/Input （查看上传的文件）

hadoop fs -text /Hadoop/Input/wordcount.txt （查看文件内容）

2.2Spark环境测试

我使用spark-shell，做一个简单的WordCount的测试。我就用上面Hadoop测试上传到HDFS的文件wordcount.txt。

首先启动spark-shell命令：

spark-shell

提交任务到Spark

然后直接输入scala语句：

val file=sc.textFile("hdfs://Master:9000/Hadoop/Input/wordcount.txt")

val rdd = file.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)

rdd.collect()

rdd.foreach(println)

提交任务到Spark

退出使用如下命令：

:quit

这样环境测试就结束了。

3.Java实现单词计数

package com.example.spark;

import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.regex.Pattern;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;

import scala.Tuple2;

public final class WordCount {
    private static final Pattern SPACE = Pattern.compile(" ");

    public static void main(String[] args) throws Exception {
        SparkConf conf = new SparkConf().setAppName("kevin's first spark app");
        JavaSparkContext sc = new JavaSparkContext(conf);
        JavaRDD<String> lines = sc.textFile(args[0]).cache();
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {

            private static final long serialVersionUID = 1L;

            @Override
            public Iterator<String> call(String s) {
                return Arrays.asList(SPACE.split(s)).iterator();
            }
        });

        JavaPairRDD<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {

            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2<String, Integer> call(String s) {
                return new Tuple2<String, Integer>(s, 1);
            }
        });

        JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {

            private static final long serialVersionUID = 1L;

            @Override
            public Integer call(Integer i1, Integer i2) {
                return i1 + i2;
            }
        });

        List<Tuple2<String, Integer>> output = counts.collect();
        for (Tuple2<?, ?> tuple : output) {
            System.out.println(tuple._1() + ": " + tuple._2());
        }

        sc.close();
    }
}

4.任务提交实现

将上面Java实现的单词计数打成jar包spark-example-0.0.1-SNAPSHOT.jar，并且将jar包上传到Master节点，我是将jar包上传到/opt目录下，本文将以两种方式提交任务到spark，第一种是以spark-submit命令的方式提交任务，第二种是以java web的方式提交任务。

4.1以spark-submit命令的方式提交任务

spark-submit --master spark://114.55.246.88:7077 --class com.example.spark.WordCount /opt/spark-example-0.0.1-SNAPSHOT.jar hdfs://Master:9000/Hadoop/Input/wordcount.txt

4.2以java web的方式提交任务

我是用spring boot搭建的java web框架，实现代码如下：

1）新建maven项目spark-submit

2）pom.xml文件内容， 这里要注意spark的依赖jar包要与scala的版本相对应，如spark-core_2.11，这后面2.11就是你安装的scala的版本 。 

<?xml version="1.0"?>
<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>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>1.4.1.RELEASE</version>
    </parent>
    <artifactId>spark-submit</artifactId>
    <description>spark-submit</description>
    <properties>
        <start-class>com.example.spark.SparkSubmitApplication</start-class>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <java.version>1.8</java.version>
        <commons.version>3.4</commons.version>
        <org.apache.spark-version>2.1.0</org.apache.spark-version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-lang3</artifactId>
            <version>${commons.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.tomcat.embed</groupId>
            <artifactId>tomcat-embed-jasper</artifactId>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>com.jayway.jsonpath</groupId>
            <artifactId>json-path</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
            <exclusions>
                <exclusion>
                    <artifactId>spring-boot-starter-tomcat</artifactId>
                    <groupId>org.springframework.boot</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-jetty</artifactId>
            <exclusions>
                <exclusion>
                    <groupId>org.eclipse.jetty.websocket</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-jetty</artifactId>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>javax.servlet</groupId>
            <artifactId>jstl</artifactId>
        </dependency>
        <dependency>
            <groupId>org.eclipse.jetty</groupId>
            <artifactId>apache-jsp</artifactId>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-solr</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>javax.servlet</groupId>
            <artifactId>jstl</artifactId>
        </dependency>
        
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${org.apache.spark-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${org.apache.spark-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>${org.apache.spark-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${org.apache.spark-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka_2.11</artifactId>
            <version>1.6.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-graphx_2.11</artifactId>
            <version>${org.apache.spark-version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>3.0.0</version>
        </dependency>

        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-core</artifactId>
            <version>2.6.5</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.6.5</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-annotations</artifactId>
            <version>2.6.5</version>
        </dependency>

    </dependencies>
    <packaging>war</packaging>

    <repositories>
        <repository>
            <id>spring-snapshots</id>
            <name>Spring Snapshots</name>
            <url>https://repo.spring.io/snapshot</url>
            <snapshots>
                <enabled>true</enabled>
            </snapshots>
        </repository>
        <repository>
            <id>spring-milestones</id>
            <name>Spring Milestones</name>
            <url>https://repo.spring.io/milestone</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
        </repository>
        <repository>
            <id>maven2</id>
            <url>http://repo1.maven.org/maven2/</url>
        </repository>
    </repositories>
    <pluginRepositories>
        <pluginRepository>
            <id>spring-snapshots</id>
            <name>Spring Snapshots</name>
            <url>https://repo.spring.io/snapshot</url>
            <snapshots>
                <enabled>true</enabled>
            </snapshots>
        </pluginRepository>
        <pluginRepository>
            <id>spring-milestones</id>
            <name>Spring Milestones</name>
            <url>https://repo.spring.io/milestone</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
        </pluginRepository>
    </pluginRepositories>

    <build>
        <plugins>
            <plugin>
                <artifactId>maven-war-plugin</artifactId>
                <configuration>
                    <warSourceDirectory>src/main/webapp</warSourceDirectory>
                </configuration>
            </plugin>

            <plugin>
                <groupId>org.mortbay.jetty</groupId>
                <artifactId>jetty-maven-plugin</artifactId>
                <configuration>
                    <systemProperties>
                        <systemProperty>
                            <name>spring.profiles.active</name>
                            <value>development</value>
                        </systemProperty>
                        <systemProperty>
                            <name>org.eclipse.jetty.server.Request.maxFormContentSize</name>
                            <!-- -1代表不作限制 -->
                            <value>600000</value>
                        </systemProperty>
                    </systemProperties>
                    <useTestClasspath>true</useTestClasspath>
                    <webAppConfig>
                        <contextPath>/</contextPath>
                    </webAppConfig>
                    <connectors>
                        <connector implementation="org.eclipse.jetty.server.nio.SelectChannelConnector">
                            <port>7080</port>
                        </connector>
                    </connectors>
                </configuration>
            </plugin>
        </plugins>

    </build>
</project>

3）SubmitJobToSpark.java

package com.example.spark;

import org.apache.spark.deploy.SparkSubmit;

/**
 * @author kevin
 *
 */
public class SubmitJobToSpark {

    public static void submitJob() {
        String[] args = new String[] { "--master", "spark://114.55.246.88:7077", "--name", "test java submit job to spark", "--class", "com.example.spark.WordCount", "/opt/spark-example-0.0.1-SNAPSHOT.jar", "hdfs://Master:9000/Hadoop/Input/wordcount.txt" };
        SparkSubmit.main(args);
    }
}

4）SparkController.java

package com.example.spark.web.controller;

import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestMethod;
import org.springframework.web.bind.annotation.ResponseBody;

import com.example.spark.SubmitJobToSpark;

@Controller
@RequestMapping("spark")
public class SparkController {
    private Logger logger = LoggerFactory.getLogger(SparkController.class);

    @RequestMapping(value = "sparkSubmit", method = { RequestMethod.GET, RequestMethod.POST })
    @ResponseBody
    public String sparkSubmit(HttpServletRequest request, HttpServletResponse response) {
        logger.info("start submit spark tast...");
        SubmitJobToSpark.submitJob();
        return "hello";
    }

}

5）将项目spark-submit打成war包部署到Master节点tomcat上，访问如下请求：

http://114.55.246.88:9090/spark-submit/spark/sparkSubmit

在tomcat的log中能看到计算的结果。

更多 Spark 相关教程见以下内容 ：

CentOS 7.0下安装并配置Spark  http://www.linuxidc.com/Linux/2015-08/122284.htm

Spark1.0.0部署指南 http://www.linuxidc.com/Linux/2014-07/104304.htm

Spark2.0安装配置文档 http://www.linuxidc.com/Linux/2016-09/135352.htm

Spark 1.5、Hadoop 2.7 集群环境搭建 http://www.linuxidc.com/Linux/2016-09/135067.htm

Spark官方文档 - 中文翻译 http://www.linuxidc.com/Linux/2016-04/130621.htm

CentOS 6.2(64位)下安装Spark0.8.0详细记录 http://www.linuxidc.com/Linux/2014-06/102583.htm

Spark2.0.2 Hadoop2.6.4全分布式配置详解 http://www.linuxidc.com/Linux/2016-11/137367.htm

Ubuntu 14.04 LTS 安装 Spark 1.6.0 （伪分布式） http://www.linuxidc.com/Linux/2016-03/129068.htm

Spark 的详细介绍 ： 请点这里

Spark 的下载地址 ： 请点这里

本文永久更新链接地址 ： http://www.linuxidc.com/Linux/2017-06/144928.htm


以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

本站部分资源来源于网络，本站转载出于传递更多信息之目的，版权归原作者或者来源机构所有，如转载稿涉及版权问题，请联系我们。

码农书籍
创业时代

创业时代

付遥 / 中信出版社 / 2015-7 / 39.8元

香港人郭鑫年酷爱赛车，在驾车穿越隧道的时候，因为收发短信发生意外，他从被撞得破烂的车里爬出来时，兴奋地高喊：我有一个伟大的想法，手机上的对讲机，将要改变世界！他随即辞职来到北京，开始艰难的创业历程。 移动技术迅猛发展，正在颠覆互联网行业，郭鑫年误打误撞，对讲机用户数量急增，竟成为移动互联网的明星，他也因此置身于风口浪尖。三大互联网巨头为了抢夺手机入口大打出手，无不希望争夺这张通往未来移动市场......一起来看看 《创业时代》 这本书的介绍吧!

码农工具
HTML 压缩/解压工具
HTML 压缩/解压工具

在线压缩/解压 HTML 代码

UNIX 时间戳转换
UNIX 时间戳转换

UNIX 时间戳转换

  • New
  • 文章
  • 话题
  • 教程
    • 关注 码农网 公众号
    版权所有，保留一切权利！© 2018-2024 码农网 粤ICP备17054400号-3