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SQLServer高效解析JSON格式數據的實例過程

【字号：大中小】作者：豬豬瀏覽：137日期：2023-03-06 14:25:40

1. 背景

最近碰到個需求，源數據存在posgtreSQL中，且為JSON格式。那如果在SQLServer中則無法直接使用，需要先解析成表格行列結構化存儲，再復用。

樣例數據如下

‘[{“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，可以先將 JSON串拆成獨立的 key-value對，再來對key-value子串做截取，獲取兩列數據值。

2. 拆串-拆分JSON串至key-value子串

這里主要利用行號和分隔符來組合完成拆分的功能。
參考如下樣例。
主要利用連續數值作為索引(起始值為1)，從源字符串每個位置截取長度為1(分隔符的長度)的字符，如果為分隔符，則為有效的、待處理的記錄。有點類似于生物DNA檢測中的鳥槍法，先廣撒網，再根據標記識別、追蹤。

/*
 * Date   : 2020-07-01
 * Author : 飛虹
 * Sample : 拆分 指定分割符的字符串為單列多值
 * Input  : 字符串"jun,cong,haha"
 * Output : 列，值為 "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-創建函數截取key-value串的值

基于第2步的結果，可以將JSON長串拆分為 key-value字符串，如 “2020-01-01”:“98.99”。到這一步，就好辦了。既可以自己寫表值函數來返回結果，也可以直接通過substring來截取。這里開發一個表值函數，來進行封裝。

 /*
  *******************************************************************************
  *     Date : 2020-07-01
  *   Author : 飛虹
  *     Note : 利用patindex正則匹配字符,在while中對字符進行逐個匹配、替換為空。
  * Function : getDateAmt
  *   Input  : key-value字符串，如 "2020-01-01":"98.99"
  *   Output : Table類型(日期列,數值列)。值為 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：去除非數字 、頓號、橫線 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
 
 --測試
 select  * from DBO.getDateAmt("{"key":"2019-01-01","value":"4500.0"")

4. 完整樣例

附上完整腳本樣例，全程CTE，直接查詢，預覽效果。

;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 
	-- 注意這里生成行號時， 需要設置默認遞歸次數
	option(maxrecursion 0)

5. 問題

經過在個人普通配置PC實測，性能有點堪憂，耗時：數據量約為15mins:50W ，不太能接受。有興趣或者經歷過的伙伴，出手來協助, 怎么提高效率，或者來個新方案？

到此這篇關于SQLServer高效解析JSON格式數據的文章就介紹到這了,更多相關SQLServer解析JSON數據內容請搜索以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持！

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