如何在IDEA上编写Spark程序?(本地+集群+java三种模式书写代码)

        本篇博客，Alice为大家带来关于如何在IDEA上编写Spark程序的教程。

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写在前面

        本次讲解我会通过一个非常经典的案例，同时也是在学MapReduce入门时少不了的一个例子——WordCount 来完成不同场景下Spark程序代码的书写。大家可以在敲代码时可以思考这样一个问题，用Spark是不是真的比MapReduce简便?

准备材料

wordcount.txt

hello me you her
hello you her
hello her
hello

图解WordCount

pom.xml

• 创建Maven项目并补全目录、配置pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<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>com.czxy</groupId>
    <artifactId>spark_demo</artifactId>
    <version>1.0-SNAPSHOT</version>


    <!-- 指定仓库位置，依次为aliyun、cloudera和jboss仓库 -->
    <repositories>
        <repository>
            <id>aliyun</id>
            <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        </repository>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
        <repository>
            <id>jboss</id>
            <url>http://repository.jboss.com/nexus/content/groups/public</url>
        </repository>
    </repositories>
    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <scala.version>2.11.8</scala.version>
        <scala.compat.version>2.11</scala.compat.version>
        <hadoop.version>2.7.4</hadoop.version>
        <spark.version>2.2.0</spark.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive-thriftserver_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!-- <dependency>
             <groupId>org.apache.spark</groupId>
             <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
             <version>${spark.version}</version>
         </dependency>-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--<dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0-mr1-cdh5.14.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.0-cdh5.14.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.2.0-cdh5.14.0</version>
        </dependency>-->

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.3.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.3.1</version>
        </dependency>
        <dependency>
            <groupId>com.typesafe</groupId>
            <artifactId>config</artifactId>
            <version>1.3.3</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>
    </dependencies>

    <build>
        <sourceDirectory>src/main/java</sourceDirectory>
        <testSourceDirectory>src/test/scala</testSourceDirectory>
        <plugins>
            <!-- 指定编译java的插件 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.5.1</version>
            </plugin>
            <!-- 指定编译scala的插件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                        <configuration>
                            <args>
                                <arg>-dependencyfile</arg>
                                <arg>${project.build.directory}/.scala_dependencies</arg>
                            </args>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.18.1</version>
                <configuration>
                    <useFile>false</useFile>
                    <disableXmlReport>true</disableXmlReport>
                    <includes>
                        <include>**/*Test.*</include>
                        <include>**/*Suite.*</include>
                    </includes>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <filter>
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <transformers>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass></mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

• maven-assembly-plugin和maven-shade-plugin的区别

可以参考这篇博客https://blog.csdn.net/lisheng19870305/article/details/88300951

本地执行

package com.czxy.scala

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/*
 * @Auther: Alice菌
 * @Date: 2020/2/19 08:39
 * @Description:
    流年笑掷 未来可期。以梦为马,不负韶华!
 */
/**
  * 本地运行
  */
object Spark_wordcount {

  def main(args: Array[String]): Unit = {

    // 1.创建SparkContext
    var config = new SparkConf().setAppName("wc").setMaster("local[*]")
    val sc = new SparkContext(config)
    sc.setLogLevel("WARN")

    // 2.读取文件
    // A Resilient Distributed Dataset (RDD)弹性分布式数据集
    // 可以简单理解为分布式的集合，但是Spark对它做了很多的封装
    // 让程序员使用起来就像操作本地集合一样简单，这样大家就很happy了
    val fileRDD: RDD[String] = sc.textFile("G:\\2020干货\\Spark\\wordcount.txt")

    // 3.处理数据
    // 3.1 对每一行数据按空格切分并压平形成一个新的集合中
    // flatMap是对集合中的每一个元素进行操作，再进行压平
    val wordRDD: RDD[String] = fileRDD.flatMap(_.split(" "))
    // 3.2 每个单词记为1
    val wordAndOneRDD: RDD[(String, Int)] = wordRDD.map((_,1))
    // 3.3 根据key进行聚合，统计每个单词的数量
    // wordAndOneRDD.reduceByKey((a,b)=>a+b)
    // 第一个_: 之前累加的结果
    // 第二个_: 当前进来的数据
    val wordAndCount: RDD[(String, Int)] = wordAndOneRDD.reduceByKey(_+_)
    // 4. 收集结果
    val result: Array[(String, Int)] = wordAndCount.collect()
    // 控制台打印结果
    result.foreach(println)

  }
}

运行的结果:

集群上运行

package com.czxy.scala

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/*
 * @Auther: Alice菌
 * @Date: 2020/2/19 09:12
 * @Description:
    流年笑掷 未来可期。以梦为马,不负韶华!
 */
/**
  * 集群运行
  */
object Spark_wordcount_cluster {
  def main(args: Array[String]): Unit = {

    // 1. 创建SparkContext
    val config = new SparkConf().setAppName("wc")
    val sc = new SparkContext(config)
    sc.setLogLevel("WARN")
    // 2. 读取文件
    // A Resilient Distributed Dataset (RDD) 弹性分布式数据集
    // 可以简单理解为分布式的集合，但是spark对它做了很多的封装
    // 让程序员使用起来就像操作本地集合一样简单，这样大家就很happy了
    val fileRDD: RDD[String] = sc.textFile(args(0)) // 文件输入路径
    // 3. 处理数据
    // 3.1对每一行数据按照空格进行切分并压平形成一个新的集合
    // flatMap是对集合中的每一个元素进行操作，再进行压平
    val wordRDD: RDD[String] = fileRDD.flatMap(_.split(" "))
    // 3.2 每个单词记为1
    val wordAndOneRDD = wordRDD.map((_,1))
    // 3.3 根据key进行聚合，统计每个单词的数量
    // wordAndOneRDD.reduceByKey((a,b)=>a+b)
    // 第一个_:之前累加的结果
    // 第二个_:当前进来的数据
    val wordAndCount: RDD[(String, Int)] = wordAndOneRDD.reduceByKey(_+_)
    wordAndCount.saveAsTextFile(args(1)) // 文件输出路径

  }
}

• 打包

• 上传
  
• 执行命令提交到Spark-HA集群

/export/servers/spark/bin/spark-submit \
--class cn.itcast.sparkhello.WordCount \
--master spark://node01:7077,node02:7077 \
--executor-memory 1g \
--total-executor-cores 2 \
/root/wc.jar \
hdfs://node01:8020/wordcount/input/words.txt \
hdfs://node01:8020/wordcount/output4

• 执行命令提交到YARN集群

/export/servers/spark/bin/spark-submit \
--class cn.itcast.sparkhello.WordCount \
--master yarn \
--deploy-mode cluster \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores 2 \
--queue default \
/root/wc.jar \
hdfs://node01:8020/wordcount/input/words.txt \
hdfs://node01:8020/wordcount/output5

这里我们提交到YARN集群

运行结束后在hue中查看结果

Java8版[了解]

        Spark是用Scala实现的，而scala作为基于JVM的语言，与Java有着良好集成关系。用Java语言来写前面的案例同样非常简单，只不过会有点冗长。

package com.czxy.scala;

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 scala.Tuple2;

import java.util.Arrays;

/**
 * @Auther: Alice菌
 * @Date: 2020/2/21 09:48
 * @Description: 流年笑掷 未来可期。以梦为马,不负韶华!
 */
public class Spark_wordcount_java8 {


    public static void main(String[] args){
        SparkConf conf = new SparkConf().setAppName("wc").setMaster("local[*]");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        JavaRDD<String> fileRDD = jsc.textFile("G:\\2020干货\\Spark\\wordcount.txt");
        JavaRDD<String> wordRDD = fileRDD.flatMap(s -> Arrays.asList(s.split(" ")).iterator());
        JavaPairRDD<String, Integer> wordAndOne = wordRDD.mapToPair(w -> new Tuple2<>(w, 1));
        JavaPairRDD<String, Integer> wordAndCount = wordAndOne.reduceByKey((a, b) -> a + b);
        //wordAndCount.collect().forEach(t->System.out.println(t));
        wordAndCount.collect().forEach(System.out::println);
        //函数式编程的核心思想:行为参数化!
    }

}

运行后的结果是一样的。

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        本次的分享就到这里，受益的小伙伴或对大数据技术感兴趣的朋友记得点赞关注Alice哟(＾Ｕ＾)ノ~ＹＯ