一、分组求最大值

计算文本里面的每个key分组求最大值，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><!-- 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>

 

三、编程代码 

public class GroupByMaxApp implements SparkConfInfo {public static void main(String[] args) {String filePath = "E:\\spark\\groubByNumber.txt";SparkSession sparkSession = new GroupByMaxApp().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 -> {
//
//                    Integer max = null;
//                    Iterator<Integer> it = v.iterator();
//                    while (it.hasNext()) {
//                        Integer val = it.next();
//                        if(max==null || max<val){
//                            max = val;
//                        }
//                    }
//                    return max;
//                })
//                .collect()
//                .forEach(v -> System.out.println(v._1 + ":" + v._2));//这种聚合数据再计算numbers.combineByKey(max -> max,  // 将val映射为一个元组，作为分区内聚合初始值(max,val) -> {if (max < val) {max = val;}return max;}, //分区内聚合，(a, b) -> Math.max(a, b))   //分区间聚合.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:538
B:79
C:774
D:78
E:888
F:543
G:49
H:67

 

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)