后台查询到前台所需,数据结构转换的通用方法(三) 地图-色斑图的对接
背景:
前台所需的数据结构和后台sql查询的数据结构一般不一致,该情况就涉及到了数据结构的转换,往往很多接口的查询都是固定两种数据结构的转化,这个时候就可以把这种转化关系抽离成一个通用的方法,避免大量的重复代码
前台数据结构:
min和max用来确定色域范围
{
year:[2010,2011,2012],
dates:[
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value}
]
}
后台数据结构:
id year column1 areaCode areaName
1 2010 value 111 北京
2 2011 value 111 北京
3 2012 value 111 北京
4 2013 value 111 北京
1 2010 value 222 上海
2 2011 value 222 上海
3 2012 value 222 上海
4 2013 value 222 上海
1 2010 value 333 广州
2 2011 value 333 广州
3 2012 value 333 广州
4 2013 value 333 广州
实现:
使用List<Map<String,Object>> 接收后台sql查询数据
sql:select year,column1,areaCode,areaName from test where parentCode= 110000 and year>=2010 and year <=2013
year和areaCode联合唯一
代码:
/**
*
* @param results 原始数据结构列
* @param valueColumn 需要取值的列名
* @return {year:[...],datas:[{data:[{name:地区名,value:数值,areaCode:地区码}....],minValue:最大值,maxValue:最小值}...]}
*/
public Map<String,Object> transfDataStructTwo(List<Map<String, Object>> results,String valueColumn){
//管理引用的数据结构 year:[{columnName:columnValue}...]
Map<Integer,List<Map<String,Object>>> managerObject = new HashMap<Integer,List<Map<String,Object>>>();
//管理引用的数据结构 year:{minValue,maxValue};
Map<Integer,Map<String,Float>> managerMinMax = new HashMap<Integer,Map<String,Float>>();
//年份数组 从小到大排列
Set<Integer> years = new TreeSet<Integer>();
Integer year;
Float value;
Float minValue;
Float maxValue;
List<Map<String, Object>> objM;
Map<String,Float> minMaxM;
for (Map<String, Object> map : results) {
year = Integer.parseInt(map.get("static_year").toString());
value = Float.parseFloat(map.get(valueColumn).toString());
years.add(year);
//替换map中valueColumn的名字,去掉年份
map.put("value", value);
map.remove(valueColumn);
map.remove("static_year");
//管理引用
objM = managerObject.get(year);
if(objM==null) {
objM = new ArrayList<Map<String, Object>>();
managerObject.put(year, objM);
}
objM.add(map);
//求对应年份内的最大最小值
minMaxM = managerMinMax.get(year);
if(minMaxM==null) {
minMaxM = new HashMap<String,Float>();
managerMinMax.put(year, minMaxM);
minMaxM.put("minValue", value);
minMaxM.put("maxValue", value);
}else {
minValue = minMaxM.get("minValue");
maxValue = minMaxM.get("maxValue");
if(minValue>value) {
minMaxM.put("minValue", value);
}else if(maxValue<value) {
minMaxM.put("maxValue", value);
}
}
}
Map<String,Object> result = new HashMap<String,Object>();
result.put("static_year", years);
List<Map<String,Object>> datas = new ArrayList<Map<String,Object>>();
result.put("datas", datas);
Map<String,Object> data;
//封装数据
for (Integer yearKey : years) {
objM = managerObject.get(yearKey);
minValue = managerMinMax.get(yearKey).get("minValue");
maxValue = managerMinMax.get(yearKey).get("maxValue");
data = new HashMap<String,Object>();
datas.add(data);
data.put("data", objM);
data.put("minValue", minValue);
data.put("maxValue", maxValue);
}
return result;
}
测试:
public static void main(String[] args) {
MainTest test = new MainTest();
List<Map<String,Object>> results = new ArrayList<Map<String,Object>>();
Map<String,Object> result1 = new HashMap<String,Object>();
results.add(result1);
result1.put("static_year", 2010);
result1.put("column1", 100);
result1.put("areaName", "北京");
result1.put("areaCode", "111");
Map<String,Object> result2 = new HashMap<String,Object>();
results.add(result2);
result2.put("static_year", 2010);
result2.put("column1", 90);
result2.put("areaName", "上海");
result2.put("areaCode", "222");
Map<String,Object> result3 = new HashMap<String,Object>();
results.add(result3);
result3.put("static_year", 2010);
result3.put("column1", 80);
result3.put("areaName", "广州");
result3.put("areaCode", "333");
Map<String,Object> result4 = new HashMap<String,Object>();
results.add(result4);
result4.put("static_year", 2011);
result4.put("column1", 105);
result4.put("areaName", "北京");
result4.put("areaCode", "111");
Map<String,Object> result5 = new HashMap<String,Object>();
results.add(result5);
result5.put("static_year", 2011);
result5.put("column1", 95);
result5.put("areaName", "上海");
result5.put("areaCode", "222");
Map<String,Object> result6 = new HashMap<String,Object>();
results.add(result6);
result6.put("static_year", 2011);
result6.put("column1", 85);
result6.put("areaName", "广州");
result6.put("areaCode", "333");
Map<String, Object> transfDataStruct = test.transfDataStructTwo(results,"column1");
System.out.println(transfDataStruct);
}
结果:
{static_year=[2010, 2011], datas=[{minValue=80.0, data=[{areaCode=111, areaName=北京, value=100.0}, {areaCode=222, areaName=上海, value=90.0}, {areaCode=333, areaName=广州, value=80.0}], maxValue=100.0}, {minValue=85.0, data=[{areaCode=111, areaName=北京, value=105.0}, {areaCode=222, areaName=上海, value=95.0}, {areaCode=333, areaName=广州, value=85.0}], maxValue=105.0}]}
后言:
static_year可以替换为排序字段,本通用型方法只能支持对单一数据列数据的数据结构转化,如果有需要可以往后扩展增加对多个数据列的数据结构转换
扩展后前台接收数据结构:
{
year:[2010,2011,2012],
column1:[
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value}
]
column2:[
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value}
]
column3:[
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value},
{data:[{areaName:地区名,areaCode:地区码,value:值},...],min:value,max:value}
]
}
转载自：https://blog.csdn.net/a13871125303/article/details/81216318
