任务

一个在线商品购买记录数据集，约40M,格式如下：

Jack,iphone cover,9,99Jack,iphone cover,9,99Jack,iphone cover,9,99Jack,iphone cover,9,99

完成统计：

代码

import java.util.Collections;import java.util.Comparator;import java.util.List;import org.apache.commons.collections.comparators.ComparableComparator;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.DoubleFunction;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.Function2;import org.apache.spark.api.java.function.PairFunction;import scala.Tuple2;/** *  * @author jinhang * */public class JavaApp {    public static void main(String[] args) {        SparkConf sparkConf = new SparkConf().setAppName("ShopInfoAnalysis").setMaster("local[*]");        JavaSparkContext sc = new JavaSparkContext(sparkConf);        JavaRDD
     
       data = sc.textFile("data/UserPurchaseHistory.csv").map(s -> s.split(","));        /**         * 统计         */        long numPurchases = data.count();        long uniqueUsers = data.map(s->s[0]).distinct().count();        double totalRevenue = data.mapToDouble(s -> Double.parseDouble(s[2])).sum();        JavaPairRDD
      
        product = data.mapToPair(s->new Tuple2(s[1],1));        List
       
        
         > pairs= product.reduceByKey((x,y)->(x+y)).sortByKey().collect();        System.out.println(pairs);        String mostPopular = pairs.get(pairs.size()-1)._1();        int purchases = pairs.get(0)._2();        System.out.println("Total purchases: " + numPurchases);        System.out.println("Unique users: " + uniqueUsers);        System.out.println("Total revenue: " + totalRevenue);        System.out.println(String.format("Most popular product: %s with %d purchases",                mostPopular, purchases));        sc.stop();    }}
        
       
      
     

简单的RDD转换和执行就可以简单解决大数据的问题，java实现的代码方便和以前的hadoop代码结合执行。