SQLServer高效解析JSON格式数据的实例过程

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。

/*
 * 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<100
)
select d.s, a.n
		  ,n-len(replace(left(s, n), @sep, '')) + 1 as pos,
		  CHARINDEX(@sep, s+@sep, n),
          substring(s, n, CHARINDEX(@sep, s+@sep, n)-n) as element
from (select @s as s) as d
 join cte_Num a
 on
	 n<=len(s) and
 substring(@sep+s, n, 1) = @sep

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。

 /*
  *******************************************************************************
  *     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))
 AS
 BEGIN
	 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
  END
 GO

 --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。

;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 < 1000
	)
	, cte_splitJson as (
    			SELECT  a.name
 							  ,replace(replace(a.jsonStr,'[',''),']','') as jsonStr
 	 						  ,substring(replace(replace(a.jsonStr,'[',''),']','')
											, b1.rn
											, charindex('},', replace(replace(a.jsonStr,'[',''),']','')+'},', b1.rn)-b1.rn ) as value_json
 	   			from cte_t1 a
 					cross join cte_rn b1
 				where  substring('},'+replace(replace(a.jsonStr,'[',''),']',''), rn, 2) = '},'
 	)
	select *
  	from cte_splitJson a
		cross apply dbo.getDateAmt(a.value_json) as t1
	-- 注意这里生成行号时， 需要设置默认递归次数
	option(maxrecursion 0)

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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