Spark-ML-01-小试spark分析离线商品信息
任务 一个在线商品购买记录数据集，约40M,格式如下： Jack,iphone cover,9,99 Jack,iphone cover,9,99 Jack,iphone cover,9,99 Jack,iphone cover,9,99 完成统计： 1.购买总次数 2.客户总个数 3.总收入 4.最畅销的商品 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<String[]> 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<String, Integer> product = data.mapToPair(s->new Tuple2(s[1],1)); List<Tuple2<String, Integer>> 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。
