java语言开发实现spark版（提交单机/集群两个运行版本）：词计数

矫情吗；* 2022-04-24 13:00 108阅读 0赞

单机版本：

package com.itheima.java_wordcount;
    
    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 scala.Tuple2;
    
    import java.util.Arrays;
    import java.util.Iterator;
    import java.util.List;
    
    /**
     * Date:2019/4/24
     * Author:Lynn.cn.Li
     * Desc:
     */
    public class WordCountJava {
        public static void main(String[] args) {
            // 1.创建sparkConf对象。设置appName和master地址
            SparkConf sparkConf = new SparkConf().setAppName("LocalJavaWordCount").setMaster("local[2]");
    
            // 2.创建sparkContext对象
            JavaSparkContext jsc = new JavaSparkContext(sparkConf);
    
            // 3.读取数据文件
            JavaRDD<String> textFileRDD = jsc.textFile("g://input/1.txt");
    
            // 4.切分每一行,得到每一个单词
            JavaRDD<String> flatMapRDD = textFileRDD.flatMap(new FlatMapFunction<String, String>() {
                public Iterator<String> call(String s) throws Exception {
    
                    // 按照空格切分单词
                    String[] arr = s.split(" ");
                    return Arrays.asList(arr).iterator();
                }
            });
    
            // 5.每个单词计数为1
            JavaPairRDD<String, Integer> javaPairRDD = flatMapRDD.mapToPair(new PairFunction<String, String, Integer>() {
                public Tuple2<String, Integer> call(String s) throws Exception {
    
                    return new Tuple2<String, Integer>(s, 1);
                }
            });
    
    
            // 6.相同单词出现的次数累加
            JavaPairRDD<String, Integer> resultRDD = javaPairRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
                public Integer call(Integer v1, Integer v2) throws Exception {
    
                    return v1 + v2;
                }
            });
    
    
            /**
             * 细节：实现排序
             *  1.先将（单词，次数）进行位置对调成（次数，单词），进行排序
             *  2.排序后再将（次数，单词）进行位置对调成（单词，次数）
             */
    
    
            JavaPairRDD<Integer, String> sortRDD = resultRDD.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
                public Tuple2<Integer, String> call(Tuple2<String, Integer> tuple2) throws Exception {
    
                    return new Tuple2<Integer, String>(tuple2._2, tuple2._1);
                }
            });
            // 排序
            JavaPairRDD<Integer, String> sortDescRDD = sortRDD.sortByKey(false);
    
            // 再对调位置
            JavaPairRDD<String, Integer> finalResultRDD = sortDescRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
                public Tuple2<String, Integer> call(Tuple2<Integer, String> tuple2) throws Exception {
    
    
                    return new Tuple2<String, Integer>(tuple2._2, tuple2._1);
                }
            });
    
            // 7.收集结果数据
            List<Tuple2<String, Integer>> wordData = finalResultRDD.collect();
    
            // 8.循环打印结果数据
            for (Tuple2<String, Integer> tuple2 : wordData) {
                System.out.println("单词："+tuple2._1+"出现了"+tuple2._2+"次");
            }
            // 9.关闭SparkCount
            jsc.stop();
    
        }
    }

提交集群版本：

spark执行脚本：

spark-submit --class com.itheima.java_wordcount.WordCountJavaOnline \
    --master spark://node01:7077,node02:7077 \
    --executor-memory 512m \
    --total-executor-cores 2 \
    /export/servers/sparkTestData/wordcount_java.jar \
    /spark/wordcount/input2/1.txt \
    /spark/wordcount/output4