Spark入门（十三）之分组求平均值

超、凢脫俗 2023-02-24 08:36 8阅读 0赞

###  一、分组求平均值 ###

计算文本里面的每个key分组求平均值，输出结果。

### 二、maven设置 ###

<?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 GroupByAvgApp implements SparkConfInfo {
    
        public static void main(String[] args) {
    
            String filePath = "E:\\spark\\groubByNumber.txt";
            SparkSession sparkSession = new GroupByAvgApp().getSparkConf("groubByNumber");
            JavaPairRDD<String, Integer> numbers = sparkSession.sparkContext()
                    .textFile(filePath, 4)
                    .toJavaRDD()
                    .flatMap(v -> Arrays.asList(v.split("\n")).iterator())
                    .mapToPair(v -> {
                        String[] data = v.split("\\s+");
                        if (data.length != 2) {
                            return null;
                        }
                        if (!data[1].matches("-?[0-9]+(.[0-9]+)?"))
                            return null;
                        return new Tuple2<>(data[0], Integer.valueOf(data[1]));
                    }).filter(v -> v != null).cache();
    
            //数据量大会溢出内存无法计算
    //        numbers.groupByKey()
    //                .sortByKey(true)
    //                .mapValues(v -> {
    //                    double sum = 0;
    //                    double count = 0;
    //                    Iterator<Integer> it = v.iterator();
    //                    while (it.hasNext()) {
    //                        sum += it.next();
    //                        count++;
    //                    }
    //                    double avg = sum / count;
    //                    return avg;
    //                })
    //                .collect()
    //                .forEach(v -> System.out.println(v._1 + ":" + v._2));
    
            //这种聚合数据再计算
            numbers.combineByKey(val -> new Tuple2<>(val, 1),  // 将val映射为一个元组，作为分区内聚合初始值
                    (t, val) -> new Tuple2<>(t._1() + val, t._2() + 1), //分区内聚合，
                    (a, b) -> new Tuple2<>(a._1() + b._1(), a._2() + b._2()))   //分区间聚合
                    .mapToPair(keyValPair -> {
                        double avg = keyValPair._2()._2() == 0 ? 0 : keyValPair._2()._1() * 1.0 / keyValPair._2()._2();
                        return new Tuple2<>(keyValPair._1(), avg);
                    })
                    .sortByKey(true)
                    .collect()
                    .forEach(v -> System.out.println(v._1 + ":" + v._2));
    
            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;
        }
    }

groubByNumber.txt文件内容

A 100
    A 24
    B 43
    C 774
    D 43
    D 37
    D 78
    E 42
    C 68
    F 89
    G 49
    F 543
    H 36
    E 888
    A 258
    A 538
    B 79
    B 6
    H 67
    C 99

输出

A:230.0
    B:42.666666666666664
    C:313.6666666666667
    D:52.666666666666664
    E:465.0
    F:316.0
    G:49.0
    H:51.5

### 四、combineByKey方法 ###

<C> JavaPairRDD<K, C> combineByKey(Function<V, C> createCombiner, 
                                       Function2<C, V, C> mergeValue, 
                                       Function2<C, C, C> mergeCombiners);

首先介绍一下上面三个参数：

\* Users provide three functions:  
\*  - \`createCombiner\`, which turns a V into a C (e.g., creates a one-element list)  
这个函数把当前的值作为参数，此时我们可以对其做些附加操作(类型转换)并把它返回 (这一步类似于初始化操作)  
\*  - \`mergeValue\`, to merge a V into a C (e.g., adds it to the end of a list)  
该函数把元素V合并到之前的元素C(createCombiner)上 (这个操作在每个分区内进行)  
\*  - \`mergeCombiners\`, to combine two C's into a single one.  
该函数把2个元素C合并 (这个操作在不同分区间进行)