spark mvnコンパイルエラー[エラー]目標org.apache.maven.pluginsを実行できませんでした：maven-compiler-plugin：3.1：コンパイル（デフォルトコンパイル）

Question

私は学習しています。 「

のgroupIdを学ぶ：火花core_2.11 バージョン：2.0.1

JavaApp.java

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; 
import java.util.Collections; 
import java.util.Comparator; 
import java.util.List; 

/** 
* A simple Spark app in Java 
*/ 
public class JavaApp { 

    public static void main(String[] args) { 
     JavaSparkContext sc = new JavaSparkContext("local[2]", "First Spark App"); 
     // we take the raw data in CSV format and convert it into a set of records of the form (user, product, price) 
     JavaRDD<String[]> data = sc.textFile("data/UserPurchaseHistory.csv") 
       .map(new Function<String, String[]>() { 
        @Override 
        public String[] call(String s) throws Exception { 
         return s.split(","); 
        } 
       }); 

     // let's count the number of purchases 
     long numPurchases = data.count(); 

     // let's count how many unique users made purchases 
     long uniqueUsers = data.map(new Function<String[], String>() { 
      @Override 
      public String call(String[] strings) throws Exception { 
       return strings[0]; 
      } 
     }).distinct().count(); 

     // let's sum up our total revenue 
     double totalRevenue = data.map(new DoubleFunction<String[]>() { 
      @Override 
      public double call(String[] strings) throws Exception { 
       //double ret=Double.parseDouble(strings[2]); 
       //return ret; 
       //return Double.parseDouble(strings[2]); 
      } 
     }).sum(); 

     // let's find our most popular product 
     // first we map the data to records of (product, 1) using a PairFunction 
     // and the Tuple2 class. 
     // then we call a reduceByKey operation with a Function2, which is essentially the sum function 
     List<Tuple2<String, Integer>> pairs = data.map(new PairFunction<String[], String, Integer>() { 
      @Override 
      public Tuple2<String, Integer> call(String[] strings) throws Exception { 
       return new Tuple2(strings[1], 1); 
      } 
     }).reduceByKey(new Function2<Integer, Integer, Integer>() { 
      @Override 
      public Integer call(Integer integer, Integer integer2) throws Exception { 
       return integer + integer2; 
      } 
     }).collect(); 

     // finally we sort the result. Note we need to create a Comparator function, 
     // that reverses the sort order. 
     Collections.sort(pairs, new Comparator<Tuple2<String, Integer>>() { 
      @Override 
      public int compare(Tuple2<String, Integer> o1, Tuple2<String, Integer> o2) { 
       return -(o1._2() - o2._2()); 
      } 
     }); 
     String mostPopular = pairs.get(0)._1(); 
     int purchases = pairs.get(0)._2(); 

     // print everything out 
     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(); 

    } 

} 

私のpom.xml

たartifactIdをorg.apache.spark

ので、私はMavenを使用してコードをコンパイルしますが、この問題は4days中に私をworryedエラーメッセージ

[INFO] 2 errors 
[INFO] ------------------------------------------------------------- 
[INFO] ------------------------------------------------------------------------ 
[INFO] BUILD FAILURE 
[INFO] ------------------------------------------------------------------------ 
[INFO] Total time: 6.785 s 
[INFO] Finished at: 2016-10-10T05:17:42+00:00 
[INFO] Final Memory: 34M/777M 
[INFO] ------------------------------------------------------------------------ 
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.1:compile (default-compile) on project java-spark-app: Compilation failure: Compilation failure: 
[ERROR] /home/ubuntu/workspace/practice/8519OS_Code/Chapter_01/java-spark-app/src/main/java/JavaApp.java:[40,35] method map in class org.apache.spark.api.java.AbstractJavaRDDLike<T,This> cannot be applied to given types; 
[ERROR] required: org.apache.spark.api.java.function.Function<java.lang.String[],R> 
[ERROR] found: <anonymous org.apache.spark.api.java.function.DoubleFunction<java.lang.String[]>> 
[ERROR] reason: cannot infer type-variable(s) R 
[ERROR] (argument mismatch; <anonymous org.apache.spark.api.java.function.DoubleFunction<java.lang.String[]>> cannot be converted to org.apache.spark.api.java.function.Function<java.lang.String[],R>) 
[ERROR] /home/ubuntu/workspace/practice/8519OS_Code/Chapter_01/java-spark-app/src/main/java/JavaApp.java:[53,51] method map in class org.apache.spark.api.java.AbstractJavaRDDLike<T,This> cannot be applied to given types; 
[ERROR] required: org.apache.spark.api.java.function.Function<java.lang.String[],R> 
[ERROR] found: <anonymous org.apache.spark.api.java.function.PairFunction<java.lang.String[],java.lang.String,java.lang.Integer>> 
[ERROR] reason: cannot infer type-variable(s) R 
[ERROR] (argument mismatch; <anonymous org.apache.spark.api.java.function.PairFunction<java.lang.String[],java.lang.String,java.lang.Integer>> cannot be converted to org.apache.spark.api.java.function.Function<java.lang.String[],R>) 
[ERROR] -> [Help 1] 
[ERROR] 
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e switch. 
[ERROR] Re-run Maven using the -X switch to enable full debug logging. 
[ERROR] 
[ERROR] For more information about the errors and possible solutions, please read the following articles: 
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoFailureException 

の下に解決することはできません。 私に助けてください：

Answer 1

あなたが参照しているコードサンプルは、Sparkの旧バージョンを使用している可能性があります。

import java.util.ArrayList; 
import java.util.Collections; 
import java.util.Comparator; 
import java.util.List; 

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; 

public class JavaApp { 
public static void main(String[] args) { 
     JavaSparkContext sc = new JavaSparkContext("local[2]", "First Spark App"); 
     // we take the raw data in CSV format and convert it into a set of records of the form (user, product, price) 
     JavaRDD<String[]> data = sc.textFile("data/UserPurchaseHistory.csv") 
       .map(new Function<String, String[]>() { 
        @Override 
        public String[] call(String s) throws Exception { 
         return s.split(","); 
        } 
       }); 

     // let's count the number of purchases 
     long numPurchases = data.count(); 

     // let's count how many unique users made purchases 
     long uniqueUsers = data.map(new Function<String[], String>() { 
      @Override 
      public String call(String[] strings) throws Exception { 
       return strings[0]; 
      } 
     }).distinct().count(); 

     // let's sum up our total revenue 
     double totalRevenue = data.mapToDouble(new DoubleFunction<String[]>() { 
      @Override 
      public double call(String[] strings) throws Exception { 
       //double ret=Double.parseDouble(strings[2]); 
       //return ret; 
       return Double.parseDouble(strings[2]); 
      } 
     }).sum(); 

     // let's find our most popular product 
     // first we map the data to records of (product, 1) using a PairFunction 
     // and the Tuple2 class. 
     // then we call a reduceByKey operation with a Function2, which is essentially the sum function 
     List<Tuple2<String, Integer>> pairs = data.mapToPair(new PairFunction<String[], String, Integer>() { 
      @Override 
      public Tuple2<String, Integer> call(String[] strings) throws Exception { 
       return new Tuple2(strings[1], 1); 
      } 
     }).reduceByKey(new Function2<Integer, Integer, Integer>() { 
      @Override 
      public Integer call(Integer integer, Integer integer2) throws Exception { 
       return integer + integer2; 
      } 
     }).collect(); 

     // finally we sort the result. Note we need to create a Comparator function, 
     // that reverses the sort order. 
     Collections.sort(new ArrayList(pairs), new Comparator<Tuple2<String, Integer>>() { 
      @Override 
      public int compare(Tuple2<String, Integer> o1, Tuple2<String, Integer> o2) { 
       return -(o1._2() - o2._2()); 
      } 
     }); 
     String mostPopular = pairs.get(0)._1(); 
     int purchases = pairs.get(0)._2(); 

     // print everything out 
     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(); 

    } 
}