flatMap实战

需求分析

同一公司，同一产品可能有多条广告计划，每条广告计划的消耗不同，财务统计页面需要按天统计不同产品的消耗总和。

示例说明

处理前：

[{"consumption":50,"date":"2022-09-21","deptName":"字节","productName":"飞书"},
{"consumption":100,"date":"2022-09-21","deptName":"字节","productName":"抖音"},
{"consumption":100,"date":"2022-09-21","deptName":"字节","productName":"飞书"},
{"consumption":75,"date":"2022-09-20","deptName":"字节","productName":"飞书"},
{"consumption":60,"date":"2022-09-21","deptName":"字节","productName":"抖音"}]

合并后：

[{"consumption":150,"date":"2022-09-21","deptName":"字节","productName":"飞书"},
{"consumption":160,"date":"2022-09-21","deptName":"字节","productName":"抖音"},
{"consumption":75,"date":"2022-09-20","deptName":"字节","productName":"飞书"}]

说明： 对相同日期和相同产品的数据进行合并，消耗相加。好比处理前的第一条和第三条是同一个时间(2022-09-21)，同一个产品(飞书)产生的消耗，最终合并为合并后的第一条。

代码示例

public class MapFlatMapTest {
    public static void main(String[] args) {

        /**
         * 需求分析：同一公司，同一产品可能有多条广告计划，每条广告计划的消耗不同，财务统计页面需要按天统计不同产品的消耗总和。
         */
        LinkedList<FinanceStatisticsDto> processList = new LinkedList<>();
        FinanceStatisticsDto dto1 = new FinanceStatisticsDto().setDate("2022-09-21").setDeptName("字节").setProductName("飞书").setConsumption(new BigDecimal(50));
        FinanceStatisticsDto dto2 = new FinanceStatisticsDto().setDate("2022-09-21").setDeptName("字节").setProductName("抖音").setConsumption(new BigDecimal(100));
        FinanceStatisticsDto dto3 = new FinanceStatisticsDto().setDate("2022-09-21").setDeptName("字节").setProductName("飞书").setConsumption(new BigDecimal(100));
        FinanceStatisticsDto dto4 = new FinanceStatisticsDto().setDate("2022-09-20").setDeptName("字节").setProductName("飞书").setConsumption(new BigDecimal(75));
        FinanceStatisticsDto dto5 = new FinanceStatisticsDto().setDate("2022-09-21").setDeptName("字节").setProductName("抖音").setConsumption(new BigDecimal(60));

        processList.add(dto1);
        processList.add(dto2);
        processList.add(dto3);
        processList.add(dto4);
        processList.add(dto5);

        System.out.println("处理后前数据：" + JSON.toJSONString(processList));

        /**
         * 先对产品+时间合并分组后，合并每组数据为一条(消耗相加)，然后返回一个list(分组后size就是最终的返回size)
         */
        List<FinanceStatisticsDto> finalResult = processList.stream().collect(Collectors.groupingBy(x -> x.getProductName() + x.getDate())).values()
                .stream().flatMap(list -> Stream.of(list.stream().reduce((data1, data2) -> {
                    data1.setConsumption(data1.getConsumption().add(data2.getConsumption()));
                    return data1;
                }).orElse(new FinanceStatisticsDto()))).collect(Collectors.toList());

        System.out.println("处理后数据：" + JSON.toJSONString(finalResult));
    }
}

运行结果