Stream、MapReduce

table of Contents

• Stream

• MapReduce

Stream

Stream data flow calculation, the calculation speed is very fast.

Parallel data streams is calculated as: public default Stream paralleStream (); (JDK 1.8)

Data flow is calculated as: public default Stream Stream (); (JDK 1.8)

Common methods:

method	Explanation
public long count()	Calculation of the number of data
public Stream limit(long maxSize)	Set the number of set up can be placed
public Stream skip(long n)	Set the number of skipped

Example:

public class TestStream {
    public static void main(String[] args) {
        List<String> list = new ArrayList<>();
        Collections.addAll(list, "Java", "JavaScript", "C", "R", "C#");
        //将数据转成数据流
        Stream<String> stream = list.stream();

        /*
        获取集合中带有J的数据的个数
        
        int number = (int) stream.filter((element) -> element.toLowerCase().contains("j")).count();
        System.out.println(number);
        
        结果：2
        */


        /*
        获取带J的数据重新组装成一个集合
        
        List<String> subList = stream.filter((element) -> element.toLowerCase().contains("j")).collect(Collectors.toList());
        System.out.println(subList.toString());
        
        结果：[Java,JavaScript]
        
        */

        /*
          skip(跳过多少数据)
          limit(集合最多放多少数据)
         */

        //获取加了skip()和limit()的数据集合	
       
        List<String> newList = stream.filter((element) ->element.toLowerCase().contains("j")).skip(1).limit(1).collect(Collectors.toList());
        System.out.println(newList);
        stream.close();
        
         //结果：[Java]
    }
}

MapReduce

• Map processing: various kinds of data pre-processing (calculation, transformation, etc.).
• Reduce processing: calculation of statistical data, statistical operation for the processed data content.

Example:

public class TestMapReduce {
    public static void main(String[] args) {
        List<Ball> list = new ArrayList<>();
        Collections.addAll(list,
                new Ball("篮球", 199.9, 20),
                new Ball("足球", 109.9, 200),
                new Ball("排球", 59.9, 300),
                new Ball("蹴鞠", 789.9, 5));
        Stream<Ball> stream = list.stream();
        DoubleSummaryStatistics doubleSummaryStatistics = stream.filter((element) -> (element.getName())
                .contains("球")).mapToDouble((obj) -> obj.getPrice() * obj.getAmount()).summaryStatistics();
        System.out.println("购买数量：" + doubleSummaryStatistics.getCount());
        System.out.println("总花销：" + doubleSummaryStatistics.getSum());
        System.out.println("最高价格：" + doubleSummaryStatistics.getMax());
        System.out.println("最低价格：" + doubleSummaryStatistics.getMin());
        System.out.println("平均价格：" + doubleSummaryStatistics.getAverage());
    }
}

class Ball {
    private String name;
    private double price;
    private double amount;

    public Ball(String name, double price, double amount) {
        this.name = name;
        this.price = price;
        this.amount = amount;
    }

    public double getPrice() {
        return price;
    }

    public String getName() {
        return name;
    }

    public double getAmount() {
        return amount;
    }

    @Override
    public String toString() {
        return "Ball{" +
                "name='" + name + '\'' +
                ", price=" + price +
                ", amount=" + amount +
                '}';
    }
}

result:

购买数量：3
总花销：43948.0
最高价格：21980.0
最低价格：3998.0
平均价格：14649.333333333334