1. 背景
最近碰到个需求，源数据存在posgtreSQL中，且为JSON格式。那如果在SQLServer中则 无法直接使用，需要先解析成表格行列结构化存储，再复用。
样例数据如下
‘[{“key”:“2019-01-01”,“value”:“4500.0”},{“key”:“2019-01-02”,“value”:“4500.0”},{“key”:“2019-01-03”,“value”:“4500.0”},{“key”:“2019-01-04”,“value”:“4500.0”},{“key”:“2019-01-05”,“value”:“4500.0”},{“key”:“2019-01-06”,“value”:“4500.0”},{“key”:“2019-01-07”,“value”:“4500.0”},{“key”:“2019-01-08”,“value”:“4500.0”},{“key”:“2019-01-09”,“value”:“4500.0”},{“key”:“2019-01-10”,“value”:“4500.0”},{“key”:“2019-01-11”,“value”:“4500.0”},{“key”:“2019-01-12”,“value”:“4500.0”},{“key”:“2019-01-13”,“value”:“4500.0”},{“key”:“2019-01-14”,“value”:“4500.0”},{“key”:“2019-01-15”,“value”:“4500.0”},{“key”:“2019-01-16”,“value”:“4500.0”},{“key”:“2019-01-17”,“value”:“4500.0”},{“key”:“2019-01-18”,“value”:“4500.0”},{“key”:“2019-01-19”,“value”:“4500.0”},{“key”:“2019-01-20”,“value”:“4500.0”},{“key”:“2019-01-21”,“value”:“4500.0”},{“key”:“2019-01-22”,“value”:“4500.0”},{“key”:“2019-01-23”,“value”:“4500.0”},{“key”:“2019-01-24”,“value”:“4500.0”},{“key”:“2019-01-25”,“value”:“4500.0”},{“key”:“2019-01-26”,“value”:“4500.0”},{“key”:“2019-01-27”,“value”:“4500.0”},{“key”:“2019-01-28”,“value”:“4500.0”},{“key”:“2019-01-29”,“value”:“4500.0”},{“key”:“2019-01-30”,“value”:“4500.0”},{“key”:“2019-01-31”,“value”:“4500.0”}]’
研究了下方法，可以先将 JSON串 拆成独立的 key-value对，再来对key-value子串做截取，获取两列数据值。
2. 拆串-拆分JSON串至key-value子串
这里主要利用行号和分隔符来组合完成拆分的功能。
参考如下样例。
主要利用连续数值作为索引(起始值为1)，从源字符串每个位置截取长度为1(分隔符的长度)的字符，如果为分隔符，则为有效的、待处理的记录。有点类似于生物DNA检测中的鸟枪法，先广撒网，再根据标记识别、追踪。
1 2 3 4 5 6 7 8 9 10 11 12 13 | /* * Date : 2020-07-01 * Author : 飞虹 * Sample : 拆分 指定分割符的字符串为单列多值 * Input : 字符串'jun,cong,haha' * Output : 列，值为 'jun', 'cong', 'haha' */declare @s nvarchar(500) = 'jun,cong,haha' ,@sep nvarchar(5) = ',';with cte_Num as ( select 1 as n union all select n+1 n from cte_Num where n |
3. 取值-创建函数截取key-value串的值
基于第2步的结果，可以将JSON长串拆分为 key-value字符串，如 “2020-01-01”:“98.99”。到这一步，就好办了。既可以自己写表值函数来返回结果，也可以直接通过substring来截取。这里开发一个表值函数，来进行封装。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | /* ******************************************************************************* * Date : 2020-07-01 * Author : 飞虹 * Note : 利用patindex正则匹配字符,在while中对字符进行逐个匹配、替换为空。 * Function : getDateAmt * Input : key-value字符串，如 "2020-01-01":"98.99" * Output : Table类型(日期列,数值列)。值为 2020-01-01, 98.99 ********************************************************************************/CREATE FUNCTION dbo.getDateAmt(@S VARCHAR(100))RETURNS @tb_rs table(dt date, amt decimal(28,14)) ASBEGIN WHILE PATINDEX('%[^0-9,-.]%',@S) > 0 BEGIN -- 匹配：去除非数字 、顿号、横线 的字符 set @s=stuff(@s,patindex('%[^0-9,-.]%',@s),1,'') END insert into @tb_rs select SUBSTRING(@s,1,charindex(',',@s)-1) , substring(@s,charindex(',',@s)+1, len(@s) ) return ENDGO--测试select * from DBO.getDateAmt('{"key":"2019-01-01","value":"4500.0"') |
4. 完整样例
附上完整脚本样例，全程CTE，直接查询，预览效果。
1 2 3 4 5 6 7 8 9 | ;with cte_t1 as ( select * from ( values('jun','[{"key":"2019-01-01","value":"4500.0"},{"key":"2019-01-02","value":"4500.0"},{"key":"2019-01-03","value":"4500.0"},{"key":"2019-01-04","value":"4500.0"},{"key":"2019-01-05","value":"4500.0"},{"key":"2019-01-06","value":"4500.0"},{"key":"2019-01-07","value":"4500.0"},{"key":"2019-01-08","value":"4500.0"},{"key":"2019-01-09","value":"4500.0"},{"key":"2019-01-10","value":"4500.0"},{"key":"2019-01-11","value":"4500.0"},{"key":"2019-01-12","value":"4500.0"},{"key":"2019-01-13","value":"4500.0"},{"key":"2019-01-14","value":"4500.0"},{"key":"2019-01-15","value":"4500.0"},{"key":"2019-01-16","value":"4500.0"},{"key":"2019-01-17","value":"4500.0"},{"key":"2019-01-18","value":"4500.0"},{"key":"2019-01-19","value":"4500.0"},{"key":"2019-01-20","value":"4500.0"},{"key":"2019-01-21","value":"4500.0"},{"key":"2019-01-22","value":"4500.0"},{"key":"2019-01-23","value":"4500.0"},{"key":"2019-01-24","value":"4500.0"},{"key":"2019-01-25","value":"4500.0"},{"key":"2019-01-26","value":"4500.0"},{"key":"2019-01-27","value":"4500.0"},{"key":"2019-01-28","value":"4500.0"},{"key":"2019-01-29","value":"4500.0"},{"key":"2019-01-30","value":"4500.0"},{"key":"2019-01-31","value":"4500.0"}]') ,('congc','[{"key":"2019-01-01","value":"347.82608695652175"},{"key":"2019-01-02","value":"347.82608695652175"},{"key":"2019-01-03","value":"347.82608695652175"},{"key":"2019-01-04","value":"347.82608695652175"},{"key":"2019-01-07","value":"347.82608695652175"},{"key":"2019-01-08","value":"347.82608695652175"},{"key":"2019-01-09","value":"347.82608695652175"},{"key":"2019-01-10","value":"347.82608695652175"},{"key":"2019-01-11","value":"347.82608695652175"},{"key":"2019-01-14","value":"347.82608695652175"},{"key":"2019-01-15","value":"347.82608695652175"},{"key":"2019-01-16","value":"347.82608695652175"},{"key":"2019-01-17","value":"347.82608695652175"},{"key":"2019-01-18","value":"347.82608695652175"},{"key":"2019-01-21","value":"347.82608695652175"},{"key":"2019-01-22","value":"347.82608695652175"},{"key":"2019-01-23","value":"347.82608695652175"},{"key":"2019-01-24","value":"347.82608695652175"},{"key":"2019-01-25","value":"347.82608695652175"},{"key":"2019-01-28","value":"347.82608695652175"},{"key":"2019-01-29","value":"347.82608695652175"},{"key":"2019-01-30","value":"347.82608695652175"},{"key":"2019-01-31","value":"347.82608695652175"}]') ) as t(name, jsonStr)) , cte_rn as ( select 1 as rn union all select rn+1 from cte_rn where rn |
5. 问题
经过在个人普通配置PC实测，性能有点堪忧，耗时：数据量 约为15mins:50W ，不太能接受。有兴趣或者经历过的伙伴，出手来协助, 怎么提高效率，或者来个新方案？
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