spark入门-本地wordcount-java版

本地开发环境说明

java：1.8
开发工具：Intelli IDEA
构建工具：maven 3.5.2

步骤一

新建maven项目

填写groupId,和artifactId,一直next知道finish
步骤二：配置pom文件

<?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>cn.spark</groupId>
  <artifactId>spark-study-java</artifactId>
  <version>1.0-SNAPSHOT</version>

  <name>spark-study-java</name>
  <!-- FIXME change it to the project's website -->
  <url>http://www.example.com</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <spark.version>2.4.0</spark.version>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.12</artifactId>
      <version>${spark.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.12</artifactId>
      <version>${spark.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-hive_2.12</artifactId>
      <version>${spark.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming_2.12</artifactId>
      <version>${spark.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.7.6</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
      <version>${spark.version}</version>
      <!--<scope>provided</scope>-->
    </dependency>

    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.6</version>
    </dependency>
    <dependency>
      <groupId>com.thoughtworks.paranamer</groupId>
      <artifactId>paranamer</artifactId>
      <version>2.8</version>
    </dependency>
  </dependencies>

  <build>
    <sourceDirectory>src/main/java</sourceDirectory>
    <testSourceDirectory>src/test</testSourceDirectory>
    <pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) -->
      <plugins>
        <!-- clean lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle -->
        <plugin>
          <artifactId>maven-clean-plugin</artifactId>
          <version>3.1.0</version>
        </plugin>
        <!-- default lifecycle, jar packaging: see https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging -->
        <plugin>
          <artifactId>maven-resources-plugin</artifactId>
          <version>3.0.2</version>
        </plugin>
        <plugin>
          <artifactId>maven-compiler-plugin</artifactId>
          <version>3.8.0</version>
        </plugin>
        <plugin>
          <artifactId>maven-surefire-plugin</artifactId>
          <version>2.22.1</version>
        </plugin>
        <plugin>
          <artifactId>maven-jar-plugin</artifactId>
          <version>3.0.2</version>
        </plugin>
        <plugin>
          <artifactId>maven-install-plugin</artifactId>
          <version>2.5.2</version>
        </plugin>
        <plugin>
          <artifactId>maven-deploy-plugin</artifactId>
          <version>2.8.2</version>
        </plugin>
        <!-- site lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#site_Lifecycle -->
        <plugin>
          <artifactId>maven-site-plugin</artifactId>
          <version>3.7.1</version>
        </plugin>
        <plugin>
          <artifactId>maven-project-info-reports-plugin</artifactId>
          <version>3.0.0</version>
        </plugin>
      </plugins>
    </pluginManagement>
<plugins>
    <plugin>
      <artifactId>maven-assembly-plugin</artifactId>
      <configuration>
        <descriptorRefs>
          <descriptorRef>jar-with-dependencies</descriptorRef>
        </descriptorRefs>
        <archive>
          <manifest>
            <mainClass></mainClass>
          </manifest>
        </archive>
      </configuration>
      <executions>
        <execution>
          <id>make-assembly</id>
          <phase>package</phase>
          <goals>
            <goal>single</goal>
          </goals>
        </execution>
      </executions>
    </plugin>

    <plugin>
      <groupId>org.codehaus.mojo</groupId>
      <artifactId>exec-maven-plugin</artifactId>
      <version>1.6.0</version>
      <executions>
        <execution>
          <goals>
            <goal>exec</goal>
          </goals>
        </execution>
      </executions>
      <configuration>
        <executable>java</executable>
        <includeProjectDependencies>true</includeProjectDependencies>
        <includePluginDependencies>false</includePluginDependencies>
        <classpathScope>compile</classpathScope>
        <mainClass>cn.spark.App</mainClass>
      </configuration>
    </plugin>

    <plugin>
      <groupId>org.apache.maven.plugins</groupId>
      <artifactId>maven-compiler-plugin</artifactId>
      <configuration>
        <source>1.8</source>
        <target>1.8</target>
      </configuration>
    </plugin>
    <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-eclipse-plugin</artifactId>
        <version>2.4</version>
        <configuration>
            <downloadSources>true</downloadSources>
        </configuration>
    </plugin>
</plugins>
  </build>
</project>

步骤三：编写程序

package cn.spark.study.core;

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.*;
import scala.Tuple2;

import java.util.Arrays;
import java.util.Iterator;

/**
 * @author jiangxl
 * @description 本地测试的worldcount程序
 * @date 2019-03-12 16:36
 **/
public class WorldCountLocal {
    public static void main(String[] args) {
        /**第一步：创建SparkConf对象，设置spark应用的配置信息
         *使用setMaster可以设置spark应用程序要连接的spark集群的master节点的url，但是如果设置为local，则代表在本地运行
         **/
        SparkConf conf = new SparkConf().setAppName("WorldCountLocal").setMaster("local");
        /**
         * 第二步：创建JavaSparkContext对象，SparkContext 是spark所有功能的入口，不管语言是java，scala，python
         *主要作用包括：初始化spark应用程序所需的一些核心组件（调度器DAGScheduler,TaskScheduler)，还回到spark master节点上进行注册等
         *不同语言编写的spark程序，sparkContext不同
         * scala：原生SparkContext
         * java：JavaSparakContext
         * 如果开发spark sql，使用SQLContext，HiveContext
         *  如果开发spark streaming程序，就是它独有的SparkContext
         */
        JavaSparkContext jsc = new JavaSparkContext(conf);
        /**
         * 第三步：针对输入源(hdfs,本地文件)，创建一个初始的rdd
         * 输入源的数据被打散， 分配到rdd的每个partition中，从而形成一个初始的分布式数据集
         * 本地测试就是针对本地文件
         * SparkContext中，根据文件类型的输入源创建RDD的方法，叫做textFile()
         * java中，创建的普通RDD，都叫javaRDD
         * RDD中有元素的概念，如果是hdfs或者本地文件，每一个元素相当于文件中的一行
         */
        JavaRDD<String> lines = jsc.textFile("D://spark//java//study1.txt");
        /**
         * 第四步：对初始RDD进行tranformation操作（计算操作)
         * 现将每一行拆分成单个单词
         * 通常操作会创建function配合rdd的map，flatmap算子来执行
         * function如果简单可以使用匿名函数，如果复杂，就使用单独类继承
         * flatMap将RDD的一个元素，拆分成一个或多个元素
         */
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public Iterator<String> call(String line) throws Exception {
                return Arrays.asList(line.split(" ")).iterator();
            }
        });

        /**
         * 接着将每个单词映射为(word，1),然后将word作为可以，计算出现次数
         * mapToPair将每个元素映射为一个tuple2类型的元素
         * T代表输入类型
         * K，V:tuple2的类型
         */
        JavaPairRDD<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() {

            @Override
            public Tuple2<String, Integer> call(String word) throws Exception {
                return new Tuple2(word, 1);
            }
        });
        /**
         * 需要以单词作为key，统计单词的出现次数，使用reduceByKey算子，对每个key和value，都进行reduce操作
         * reduce 操作是将第一个值与第二值进行计算，然后再将结果与第三个值进行计算
         */
        JavaPairRDD<String, Integer> wordCounts = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer v1, Integer v2) throws Exception {
                return v1 + v2;
            }
        });
        /**
         * flatMap,mapToPair,reduceByKey都叫transformation操作，
         * 之后需要一个action操作来出发程序的执行,例如foreach
         */
        wordCounts.foreach(new VoidFunction<Tuple2<String, Integer>>() {
            @Override
    public void call(Tuple2<String, Integer> wordCount) throws Exception {
        System.out.println(wordCount._1 + " appeared " + wordCount._2 + " times");
    }
});
        }
}

注意事项：

如果使用java1.8，则paranamer jar的版本必须是2.8以上，否则在jsc.textFile（...）会报数组越界

spark入门-本地wordcount-java版

本地开发环境说明

步骤一

步骤三：编写程序

注意事项：

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