一、依赖包配置
scala与spark的相关依赖包，spark包后尾下划线的版本数字要跟scala的版本第一二位要一致，即2.11
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.mk</groupId><artifactId>spark-test</artifactId><version>1.0</version><name>spark-test</name><url>http://spark.mk.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><scala.version>2.11.1</scala.version><spark.version>2.4.4</spark.version><hadoop.version>2.6.0</hadoop.version></properties><dependencies><!-- scala依赖--><dependency><groupId>org.scala-lang</groupId><artifactId>scala-library</artifactId><version>${scala.version}</version></dependency><!-- spark依赖--><dependency><groupId>org.apache.spark</groupId><artifactId>spark-core_2.11</artifactId><version>${spark.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.11</artifactId><version>${spark.version}</version></dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>4.11</version><scope>test</scope></dependency></dependencies><build><pluginManagement><plugins><plugin><artifactId>maven-clean-plugin</artifactId><version>3.1.0</version></plugin><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></plugins></pluginManagement></build>
</project>
二、PI例子
java重新编写scala的PI例子
package com.mk;import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SparkSession;import java.util.ArrayList;
import java.util.List;public class App
{public static void main( String[] args ){SparkConf sparkConf = new SparkConf();if(System.getProperty("os.name").toLowerCase().contains("win")) {sparkConf.setMaster("local[2]");//本地模拟System.out.println("使用本地模拟是spark");}SparkSession session = SparkSession.builder().appName("Pi").config(sparkConf).config(sparkConf).getOrCreate();int slices =2;int n = (int)Math.min(100_000L * slices, Integer.MAX_VALUE);JavaSparkContext sparkContext = new JavaSparkContext(session.sparkContext());List<Integer> list = new ArrayList<>(n);for (int i = 0; i < n; i++)list.add(i + 1);int count = sparkContext.parallelize(list, slices).map(v -> {double x = Math.random() * 2 - 1;double y = Math.random() * 2 - 1;if (x * x + y * y < 1)return 1;return 0;}).reduce((Integer a, Integer b) ->a+b);System.out.println("PI:"+ 4.0 * count / n);session.stop();}
}
三、直接在idea本地运行
输出PI
四、spark集群提交
项目打成jar，把spark-test.jar上传到~目录，执行shell命令
~/software/spark-2.4.4-bin-hadoop2.6/bin/spark-submit --master spark://hadoop01:7077,hadoop02:7077,hadoop03:7077 --class com.mk.App ~/spark-test.jar 
)
)
