Spark入门（九）之PI估值

悠悠 2023-02-24 03:43 23阅读 0赞

### 一、Pi估值算法 ###

通过在圆上“投掷飞镖”来估计π。我们在单位平方（（0，0）到（1，1））中随机选取点，看看有多少点落在单位圆内。分数应该是π/4，所以我们用这个来得到我们的估计值。

### 二、项目maven依赖 ###

<?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>
        
        <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>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>

### 三、编程代码 ###

public class PiApp implements SparkConfInfo {
    
        public static void main(String[] args) {
    
            SparkSession sparkSession = new PiApp().getSparkConf("Pi");
            int slices = 2;
            int n = (int) Math.min(100_000L * slices, Integer.MAX_VALUE);
            JavaSparkContext sparkContext = new JavaSparkContext(sparkSession.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);
            sparkSession.stop();
    
        }
    }
    
    
    public interface SparkConfInfo {
    
        default SparkSession getSparkConf(String appName){
            SparkConf sparkConf = new SparkConf();
            if(System.getProperty("os.name").toLowerCase().contains("win")) {
                sparkConf.setMaster("local[4]");
                System.out.println("使用本地模拟是spark");
            }else
            {
                sparkConf.setMaster("spark://hadoop01:7077,hadoop02:7077,hadoop03:7077");
                sparkConf.set("spark.driver.host","192.168.150.1");//本地ip，必须与spark集群能够相互访问，如：同一个局域网
                sparkConf.setJars(new String[] {".\\out\\artifacts\\spark_test\\spark-test.jar"});//项目构建生成的路径
            }
    
            SparkSession session = SparkSession.builder().appName(appName).config(sparkConf).config(sparkConf).getOrCreate();
            return session;
        }
    }

输出

PI:3.14296