Stream的使用可极大的减少sql的复杂度和对数据库的访问压力，我们可以用sql将数据一次性全部取出来,根据我们的实际需要,去组织我们需要的数据。

分组

// 按照sn分组:  List<Map<String, Object>> dataList
Map<String, List<Map<String, Object>>> dataMap = dataList.stream().collect(Collectors.groupingBy(e -> e.get("sn") + ""));	
//按照职员部分分组: List<Employee> list
Map<String, List<Employee>> collect = list.stream().collect(Collectors.groupingBy(i -> i.getUnitName()));
//多条件分组
Map<String, Map<String,List<Employee>>> collect =list.stream().collect(Collectors.groupingBy(i -> i.getUnitName(),Collectors.groupingBy(i -> i.getWorkType())));

过滤

//根据指定sn,过滤出符合的数据: List<Map<String, Object>> deviceDataList
List<Map<String, Object>> tempDeviceDataList = deviceDataList.stream().filter(map -> map.get("sn").toString().equals(sn)).collect(Collectors.toList());
//筛选出工资大于10000的职员
List<Employee> newList = list.stream().filter(item -> {
			return item.getSalary().compareTo(new BigDecimal(10000)) > 0 && !item.getWorkType().equals("项目经理");
		}).collect(Collectors.toList());

List和Map互转

list转map

// (k1,k2)->k2 避免键重复 k1-取第一个数据；k2-取最后一条数据
//key和value,都可以根据传入的值返回不同的Map
Map<String, String> deviceMap = hecmEnergyDevicesList.stream().collect(Collectors.toMap(i -> i.getDeviceNum(), j -> j.getDeviceName(), (k1, k2) -> k1));
//
Map<String, Object> map = list.stream()
				.collect(Collectors.toMap(i -> i.getEmpName() + i.getUnitName(), j -> j, (k1, k2) -> k1));

map转list

//在.map里面构造数据 return什么数据就转成什么类型的list
List<Employee> collect = map.entrySet().stream().map(item -> {
			Employee employee = new Employee();
			employee.setId(item.getKey());
			employee.setEmpName(item.getValue());
			return employee;
		}).collect(Collectors.toList());

求和/极值

//在egyList里面求cols的和
public static BigDecimal getSumBig(List<Map<String,Object>> egyList, String cols){
        BigDecimal consuBig = egyList.stream()
                .filter((Map m)->StringUtils.isNotEmpty(m.get(cols)+"") && !"null".equals(String.valueOf(m.get(cols)))
                        && !"-".equals(String.valueOf(m.get(cols))))
                .map((Map m)->new BigDecimal(m.get(cols)+""))
                .reduce(BigDecimal.ZERO,BigDecimal::add);
        return consuBig;
}
//List<Employee> list
//Bigdecimal求和/极值: 
BigDecimal sum = list.stream().map(Employee::getSalary).reduce(BigDecimal.ZERO,BigDecimal::add);
BigDecimal max = list.stream().map(Employee::getSalary).reduce(BigDecimal.ZERO,BigDecimal::max);
//基本数据类型求和/极值:
Integer sum = list.stream().mapToInt(Employee::getId).sum();
OptionalInt optionalMax = list.stream().mapToInt(Employee::getId).max();
optionalMax.getAsInt();

求最大/最小值的对象

Optional<Employee> optional = list.stream().collect(Collectors.maxBy(Comparator.comparing(Employee::getId)));
 if (optional.isPresent()) { // 判断是否有值
 		Employee user = optional.get();
 }
return optional.orElse(new Employee());

去重

//去重之后进行拼接: List<String> deviceNodeList
Srting deviceNodeStr = deviceNodeList.stream().distinct().collect(Collectors.joining("','"));
//直接去重返回list
// List<String> deviceIdList
 List<String> deviceIdList = deviceIdList.stream().distinct().collect(Collectors.toList());

排序

//按照时间排序 1升 -1降
Collections.sort(listFast, (p1, p2) -> {
     return String.valueOf(p1.get("time")).compareTo(p2.get("time") + "");
});
// s1-s2 升序   s2-s1降序
Collections.sort(list,(s1,s2) -> s1.getSalary().compareTo(s2.getSalary()));
//多条件排序: List<Employee> list, s1-s2 升序   s2-s1降序
list.sort(Comparator.comparing(Employee::getSalary).reversed().thenComparing(Employee::getId).reversed());

拼接

//将某个字段,按照某个字符串拼接:  List<Map<String, Object>> deviceMapList 
String sns = deviceMapList.stream()
     	.map((m)->m.get("sn")+"").collect(Collectors.joining(","));
//使用场景很多,在sql里面用于组织in的值.比如:
SELECT sn,time,value FROM electric_real_time WHERE FIND_IN_SET(sn,?)
List<Map<String, Object>> dataList = JdbcUtil.getJdbcTemplate().queryForList(dataSql, sns)

统计

//统计：和、数量、最大值、最小值、平均值: List<Employee> list
IntSummaryStatistics collect = list.stream().collect(Collectors.summarizingInt(Employee::getId));
System.out.println("和：" + collect.getSum());
System.out.println("数量：" + collect.getCount());
System.out.println("最大值：" + collect.getMax());
System.out.println("最小值：" + collect.getMin());
System.out.println("平均值：" + collect.getAverage());

平均值

OptionalDouble average = list.stream().mapToInt(Employee::getId).average();
average.getAsDouble();

某个值的数量

//List<Employee> list
Map<BigDecimal, Long> collect = list.stream().collect(Collectors.groupingBy(i -> i.getSalary(),Collectors.counting()));
//List<Map<String,Object>> egyList
long count = egyList.stream()
     .filter((Map m)->StringUtils.isNotEmpty(m.get(cols)+""))
     .map((Map m)->new BigDecimal(m.get(cols)+""))
     .count();

分区

//List<Employee> list
//单层分区
Map<Boolean, List<Employee>> collect = list.stream().collect(Collectors.partitioningBy(i -> i.getId() == 1));
//多层分区
Map<Boolean, Map<Boolean,List<Employee>>> collect = list.stream().collect(Collectors.partitioningBy(i -> i.getId() == 1,Collectors.partitioningBy(i -> i.getSalary().compareTo(new BigDecimal(20000)) == 0)));