List<List<Integer>> 取有交集的元素的集合

Java集合处理技巧
本文介绍了一种使用Java集合处理技巧,具体为如何从一个包含多个List的List中筛选出不被其他List完全包含的元素,通过示例代码展示了具体的实现过程。

如题。list里面也是list。怎么能够将有交集的list取出来。例如 [1,2,3,6],[2,3,4][3,6],[4,5]
这样子,最后取出来的是[1,2,3,6],[2,3,4],[4,5],而[3,6] 是被包含在[1,2,3,6]中的,就排出。

package number3;

import java.util.Arrays;
import java.util.LinkedList;
import java.util.List;

public class Tree1 {
	//这是一个测试类
	public static void main(String[] args) {
		test();
	}
	
	//任务类
	public static void test(){
        List<List<Integer>> result = new LinkedList<>();

        result.add(new LinkedList<>(Arrays.asList(1, 2, 3, 6)));
        result.add(new LinkedList<>(Arrays.asList(2, 3, 4)));
        result.add(new LinkedList<>(Arrays.asList(3, 6)));
        result.add(new LinkedList<>(Arrays.asList(4, 5)));

        int sub[] = new int[result.size()];//一开始元素全部是0,用来标记
        for (int i = 0; i < result.size(); i++) {
            List<Integer> item = result.get(i);//有点类似那种二维数组,就是二维数组的元素是一个一维数组元素
            for (int k = i + 1; k < result.size(); k++) {
                List<Integer> OtherItem = result.get(k);
                if (sub[k]!=1 && item.containsAll(OtherItem)) {//boolean contains(Object o) 如果此列表包含指定元素,则返回 true。 
                    sub[k] = 1;
                } else if (sub[i]!=1 && OtherItem.containsAll(item)) {
                    sub[i] = 1;
                }
            }
        }
            // 移除标记的元素
        for (int i = sub.length - 1; i > 0; i--) {
            if (sub[i] == 1) {
                result.remove(i);
            }
        }

        for (int i = 0; i < result.size(); i++) {
            System.out.println(Arrays.toString(result.get(i).toArray()));
        }
    }
	

}

package mxr.spark.cases; import mxr.spark.util.SparkUtil; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function; import org.junit.Test; import scala.Tuple3; import java.io.Serializable; import java.util.List; /** * @author RenYuXin * @date 2025/4/25 8:12 * @description */ public class ScoreTest implements Serializable { public static JavaSparkContext sc = SparkUtil.getSparkContext(); /** 大数据基础课程的前5名 */ @Test public void testBigDataTop5() { //todo 1.读原始数据 JavaRDD<String> rdd = sc.textFile("datas/input/scoreSpark/result_bigdata.txt"); //todo 2.对原始数据进行切割:数组 Function<String,String[]> function = new Function<String,String[]>() { @Override public String[] call(String line) throws Exception { String[] strings = line.split("\t"); return strings; } }; JavaRDD<String[]> mapped = rdd.map(function); //todo 3.将数组转换为Tuple3 Function<String[],Tuple3<String,String,Integer>> tuple3Function = new Function<String[],Tuple3<String,String,Integer>>() { @Override public Tuple3<String,String,Integer> call(String[] strings) throws Exception { return new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2])); } }; JavaRDD<Tuple3<String,String,Integer>> tuple3 = mapped.map(tuple3Function); //todo 4.对Tuple3的第三个元素降序排序 Function<Tuple3<String,String,Integer>,Integer> t3f = new Function<Tuple3<String,String,Integer>,Integer>() { @Override public Integer call(Tuple3<String,String,Integer> tuple3) throws Exception { return tuple3._3(); } }; JavaRDD<Tuple3<String,String,Integer>> sortedBy = tuple3.sortBy(t3f,false,1); //todo 5.前5个元素 List<Tuple3<String,String,Integer>> taken = sortedBy.take(5); taken.forEach(System.out :: println); } /** 大数据基础课程的前5名 */ @Test public void testBigDataTop51() { //todo 1.读原始数据 JavaRDD<String> rdd = sc.textFile("datas/input/scoreSpark/result_bigdata.txt"); //todo 2.将原始数据转换为Tuple3 Function<String,Tuple3<String,String,Integer>> function = new Function<String,Tuple3<String,String,Integer>>() { @Override public Tuple3<String,String,Integer> call(String line) throws Exception { String[] strings = line.split("\t"); if(strings.length == 3) { return new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2])); } return null; } }; JavaRDD<Tuple3<String,String,Integer>> tuple3 = rdd.map(function); //todo 3.对Tuple3的第三个元素降序排序 Function<Tuple3<String,String,Integer>,Integer> t3f = new Function<Tuple3<String,String,Integer>,Integer>() { @Override public Integer call(Tuple3<String,String,Integer> tuple3) throws Exception { return tuple3._3(); } }; JavaRDD<Tuple3<String,String,Integer>> sortBy = tuple3.sortBy(t3f,false,1); //todo 4.获前5名 List<Tuple3<String,String,Integer>> taken = sortBy.take(5); taken.forEach(System.out :: println); } @Test public void testBigDataTop511() { //todo 1.读原始数据 JavaRDD<String> rdd = sc.textFile("datas/input/scoreSpark/result_bigdata.txt"); //todo 2.将原始数据转换为Tuple3 Function<String,Tuple3<String,String,Integer>> function = line -> { String[] strings = line.split("\t"); if(strings.length == 3) { return new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2])); } return null; }; JavaRDD<Tuple3<String,String,Integer>> tuple3 = rdd.map(function); //todo 3.对Tuple3的第三个元素降序排序 JavaRDD<Tuple3<String,String,Integer>> sortedBy = tuple3.sortBy(Tuple3 :: _3,false,1); //todo 4.前5个元素 List<Tuple3<String,String,Integer>> taken = sortedBy.take(5); taken.forEach(System.out :: println); } /** 数学基础课程的前5名 */ @Test public void testMathTop5() { //todo 1.读原始数据 JavaRDD<String> rdd = sc.textFile("datas/input/scoreSpark/result_math.txt"); //todo 2.将原始数据转换为Tuple3 Function<String,Tuple3<String,String,Integer>> function = line -> { String[] strings = line.split("\t"); if(strings.length == 3) { return new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2])); } return null; }; JavaRDD<Tuple3<String,String,Integer>> tuple3 = rdd.map(function); //todo 3.对Tuple3的第三个元素降序排序 JavaRDD<Tuple3<String,String,Integer>> sortedBy = tuple3.sortBy(Tuple3 :: _3,false,1); //todo 4.前5名 List<Tuple3<String,String,Integer>> taken = sortedBy.take(5); taken.forEach(System.out :: println); } @Test public void testMathTop5_1() { //todo 1.读原始数据 JavaRDD<String> rdd = sc.textFile("datas/input/scoreSpark/result_math.txt"); //todo 2.将原始数据转换为Tuple3 Function<String,Tuple3<String,String,Integer>> function = line -> { String[] strings = line.split("\t"); if(strings.length == 3) { return new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2])); } return null; }; JavaRDD<Tuple3<String,String,Integer>> tuple3 = rdd.map(function); //todo 3.对Tuple3的第三个元素降序排序 JavaRDD<Tuple3<String,String,Integer>> sortedBy = tuple3.sortBy(Tuple3 :: _3,false,1); //todo 4.前5名 List<Tuple3<String,String,Integer>> taken = sortedBy.take(5); taken.forEach(System.out :: println); } /** 两科成绩都是100分的学生id */ @Test public void testDouble100Id() { JavaRDD<String> bigdata = sc.textFile("datas/input/scoreSpark/result_bigdata.txt"); JavaRDD<String> math = sc.textFile("datas/input/scoreSpark/result_math.txt"); //@formatter:off JavaRDD<Object> bigdata100Id = bigdata.map(line -> line.split("\t")) .map(strings -> new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2]))) .filter(tuple3 -> tuple3._3()==100) .map(Tuple3::_1); JavaRDD<String> math100Id = math.map(line -> line.split("\t")) .map(strings -> new Tuple3<>(strings[0],strings[1],Integer.parseInt(strings[2]))) .filter(tuple3 -> tuple3._3()==100) .map(Tuple3::_1); //@formatter:on //求交集 bigdata100Id.intersection(bigdata100Id).collect().forEach(System.out :: println); } } 将上述代码加入求两科成绩都是100分的学生学号,使用HDFS HA
最新发布
06-21
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