Java,Python,Scala比较（三）wordcount

向右看齐 2022-06-05 04:59 213阅读 0赞

众所周知，wordcount在大数据中的地位相当于helloworld在各种编程语言中的地位。本文并不分析wordcount的计算方法，而是直接给出代码，目的是为了比较Spark中Java，Python，Scala的区别。  
　　显然，Java写法较为复杂，Python简单易懂，Scala是Spark的原生代码，故即为简洁。  
Java完整代码：

import java.util.Arrays;
    import java.util.Iterator;
    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.FlatMapFunction; 
    import org.apache.spark.api.java.function.Function2; 
    import org.apache.spark.api.java.function.PairFunction;
    import org.apache.spark.api.java.function.VoidFunction;
    import scala.Tuple2;
    public class wordcount {
        public static void main(String[] args) {
            SparkConf conf = new SparkConf().setMaster("local").setAppName("wc");
            JavaSparkContext sc = new JavaSparkContext(conf);
            //read a txtfile
            JavaRDD<String> text = sc.textFile("/home/vagrant/speech.txt");
            //split(" ")
            JavaRDD<String> words = text.flatMap(new FlatMapFunction<String, String>() {
                private static final long serialVersionUID = 1L;
                @Override
                public Iterator<String> call(String line) throws Exception {
                    return Arrays.asList(line.split(" ")).iterator();
                }
            });
            //word => (word,1)
            JavaPairRDD<String,Integer> counts=words.mapToPair(
                    new PairFunction<String, String, Integer>() {
                        public Tuple2<String, Integer> call(String s) throws Exception {
                            return new Tuple2(s, 1);
                        }
                    }
            );
            //reduceByKey
            JavaPairRDD <String,Integer> results=counts.reduceByKey(
                    new Function2<Integer, Integer, Integer>() {
                        public Integer call(Integer v1, Integer v2) throws Exception {
                            return v1 + v2;
                        }
                    }
            ) ;
            //print
            results.foreach(new VoidFunction<Tuple2<String, Integer>>(){
                  @Override
                  public void call(Tuple2<String, Integer> t) throws Exception { 
                                  System.out.println("("+t._1()+":"+t._2()+")");
                  }
                  }); 
        }
    }

Pyspark完整代码：

# Imports the PySpark libraries
    from pyspark import SparkConf, SparkContext  
    # Configure the Spark context to give a name to the application
    sparkConf = SparkConf().setAppName("MyWordCounts")
    sc = SparkContext(conf = sparkConf)
    # The text file containing the words to count (this is the Spark README file)
    textFile = sc.textFile('/home/vagrant/speech.txt')
    # The code for counting the words (note that the execution mode is lazy)
    # Uses the same paradigm Map and Reduce of Hadoop, but fully in memory
    wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
    # Executes the DAG (Directed Acyclic Graph) for counting and collecting the result
    for wc in wordCounts.collect():
        print(wc)

Scala完整代码：

import org.apache.spark.{SparkContext,SparkConf}
    
    object test{
      def main(args:Array[String]){
        val sparkConf = new SparkConf().setMaster("local").setAppName("MyWordCounts")
        val sc = new SparkContext(sparkConf)
        sc.textFile("/home/vagrant/speech.txt").flatMap(_.split(' ')).map((_,1)).reduceByKey(_+_).foreach(println)
      }
    }

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