# Scala編寫Spark的WorkCount ## 創建一個Maven項目在pom.xml中添加依賴和插件 ```XML 8 8 UTF-8 3.2.3 2.12.15 org.scala-lang scala-library ${scala.version} org.apache.spark ...

Scala編寫Spark的WorkCount

創建一個Maven項目

在pom.xml中添加依賴和插件

<!-- 定義的一些常量 -->
<properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <encoding>UTF-8</encoding>
    <spark.version>3.2.3</spark.version>
    <scala.version>2.12.15</scala.version>
</properties>

<dependencies>
    <!-- scala的依賴 -->
    <dependency>
        <groupId>org.scala-lang</groupId>
        <artifactId>scala-library</artifactId>
        <version>${scala.version}</version>
    </dependency>

    <!-- spark core 即為spark內核 ，其他高級組件都要依賴spark core -->
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.12</artifactId>
        <version>${spark.version}</version>
    </dependency>

</dependencies>

<!-- 配置Maven的鏡像庫 -->
<!-- 依賴下載國內鏡像庫 -->
<repositories>
    <repository>
        <id>nexus-aliyun</id>
        <name>Nexus aliyun</name>
        <layout>default</layout>
        <url>http://maven.aliyun.com/nexus/content/groups/public</url>
        <snapshots>
            <enabled>false</enabled>
            <updatePolicy>never</updatePolicy>
        </snapshots>
        <releases>
            <enabled>true</enabled>
            <updatePolicy>never</updatePolicy>
        </releases>
    </repository>
</repositories>

<!-- maven插件下載國內鏡像庫 -->
<pluginRepositories>
    <pluginRepository>
        <id>ali-plugin</id>
        <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        <snapshots>
            <enabled>false</enabled>
            <updatePolicy>never</updatePolicy>
        </snapshots>
        <releases>
            <enabled>true</enabled>
            <updatePolicy>never</updatePolicy>
        </releases>
    </pluginRepository>
</pluginRepositories>

<build>
    <pluginManagement>
        <plugins>
            <!-- 編譯scala的插件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
            </plugin>
            <!-- 編譯java的插件 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.5.1</version>
            </plugin>
        </plugins>
    </pluginManagement>
    <plugins>
        <plugin>
            <groupId>net.alchim31.maven</groupId>
            <artifactId>scala-maven-plugin</artifactId>
            <executions>
                <execution>
                    <id>scala-compile-first</id>
                    <phase>process-resources</phase>
                    <goals>
                        <goal>add-source</goal>
                        <goal>compile</goal>
                    </goals>
                </execution>
                <execution>
                    <id>scala-test-compile</id>
                    <phase>process-test-resources</phase>
                    <goals>
                        <goal>testCompile</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>

        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <executions>
                <execution>
                    <phase>compile</phase>
                    <goals>
                        <goal>compile</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>

        <!-- 打jar插件 -->
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-shade-plugin</artifactId>
            <version>2.4.3</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>
                    </configuration>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

創建一個scala目錄

選擇scala目錄，右鍵，將目錄轉成源碼包，或者點擊maven的刷新按鈕

編寫Spark程式

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

/**
  * 1.創建SparkContext
  * 2.創建RDD
  * 3.調用RDD的Transformation（s）方法
  * 4.調用Action
  * 5.釋放資源
  */
object WordCount {

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

    val conf: SparkConf = new SparkConf().setAppName("WordCount")
    //創建SparkContext，使用SparkContext來創建RDD
    val sc: SparkContext = new SparkContext(conf)
    //spark寫Spark程式，就是對抽象的神奇的大集合【RDD】編程，調用它高度封裝的API
    //使用SparkContext創建RDD
    val lines: RDD[String] = sc.textFile(args(0))

    //Transformation 開始 //
    //切分壓平
    val words: RDD[String] = lines.flatMap(_.split(" "))
    //將單詞和一組合放在元組中
    val wordAndOne: RDD[(String, Int)] = words.map((_, 1))
    //分組聚合，reduceByKey可以先局部聚合再全局聚合
    val reduced: RDD[(String, Int)] = wordAndOne.reduceByKey(_+_)
    //排序
    val sorted: RDD[(String, Int)] = reduced.sortBy(_._2, false)
    //Transformation 結束 //

    //調用Action將計算結果保存到HDFS中
    sorted.saveAsTextFile(args(1))
    //釋放資源
    sc.stop()
  }
}

使用maven打包

提交任務

• 上傳jar包到伺服器，然後使用sparksubmit命令提交任務

/bigdata/spark-3.2.3-bin-hadoop3.2/bin/spark-submit \
--master spark://node-1.51doit.cn:7077 \
--executor-memory 1g --total-executor-cores 4 \
--class cn._51doit.spark.day01.WordCount \
/root/spark-in-action-1.0.jar hdfs://node-1.51doit.cn:9000/words.txt hdfs://node-1.51doit.cn:9000/out
 
參數說明：
--master 指定masterd地址和埠，協議為spark://，埠是RPC的通信埠
--executor-memory 指定每一個executor的使用的記憶體大小
--total-executor-cores指定整個application總共使用了cores
--class 指定程式的main方法全類名
jar包路徑 args0 args1

Java編寫Spark的WordCount

使用匿名實現類方式

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;

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

public class JavaWordCount {

    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");
        //創建JavaSparkContext
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        //使用JavaSparkContext創建RDD
        JavaRDD<String> lines = jsc.textFile(args[0]);
        //調用Transformation（s）
        //切分壓平
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public Iterator<String> call(String line) throws Exception {
                return Arrays.asList(line.split(" ")).iterator();
            }
        });
        //將單詞和一組合在一起
        JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(
                new PairFunction<String, String, Integer>() {
                    @Override
                    public Tuple2<String, Integer> call(String word) throws Exception {
                        return Tuple2.apply(word, 1);
                    }
        });
        //分組聚合
        JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey(
                new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer v1, Integer v2) throws Exception {
                return v1 + v2;
            }
        });
        //排序，先調換KV的順序VK
        JavaPairRDD<Integer, String> swapped = reduced.mapToPair(
                new PairFunction<Tuple2<String, Integer>, Integer, String>() {
            @Override
            public Tuple2<Integer, String> call(Tuple2<String, Integer> tp) throws Exception {
                return tp.swap();
            }
        });
        //再排序
        JavaPairRDD<Integer, String> sorted = swapped.sortByKey(false);
        //再調換順序
        JavaPairRDD<String, Integer> result = sorted.mapToPair(
                new PairFunction<Tuple2<Integer, String>, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(Tuple2<Integer, String> tp) throws Exception {
                return tp.swap();
            }
        });
        //觸發Action，將數據保存到HDFS
        result.saveAsTextFile(args[1]);
        //釋放資源
        jsc.stop();
    }
}

使用Lambda表達式方式

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;

public class JavaLambdaWordCount {

    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("JavaLambdaWordCount");
        //創建SparkContext
        JavaSparkContext jsc = new JavaSparkContext(conf);
        //創建RDD
        JavaRDD<String> lines = jsc.textFile(args[0]);
        //切分壓平
        JavaRDD<String> words = lines.flatMap(line -> Arrays.asList(line.split(" ")).iterator());
        //將單詞和一組合
        JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(word -> Tuple2.apply(word, 1));
        //分組聚合
        JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey((a, b) -> a + b);
        //調換順序
        JavaPairRDD<Integer, String> swapped = reduced.mapToPair(tp -> tp.swap());
        //排序
        JavaPairRDD<Integer, String> sorted = swapped.sortByKey(false);
        //調換順序
        JavaPairRDD<String, Integer> result = sorted.mapToPair(tp -> tp.swap());
        //將數據保存到HDFS
        result.saveAsTextFile(args[1]);
        //釋放資源
        jsc.stop();
    }
}

本地運行Spark和Debug

spark程式每次都打包上在提交到集群上比較麻煩且不方便調試，Spark還可以進行Local模式運行，方便測試和調試

在本地運行

 //Spark程式local模型運行,local[*]是本地運行，並開啟多個線程
val conf: SparkConf = new SparkConf()
  .setAppName("WordCount")
  .setMaster("local[*]") //設置為local模式執行

並輸入運行參數
hdfs://linux01:9000/words.txt hdfs://linux01:9000/out/out01

讀取HDFS中的數據

由於往HDFS中的寫入數據存在許可權問題，所以在代碼中設置用戶為HDFS目錄的所屬用戶

//往HDFS中寫入數據，將程式的所屬用戶設置成更HDFS一樣的用戶
System.setProperty("HADOOP_USER_NAME", "root")

Spark編程基礎