/************************************************************** SQL Server 2012 新增的函数 ***************************************************************/ -- CONCAT ( string_value1, string_value2 [, string_valueN ] ) #字符串相连 SELECT CONCAT('A','BB','CCC','DDDD') --结果：ABBCCCDDDD -- PARSE ( string_value AS data_type [ USING culture ] ) #转换为所请求的数据类型的表达式的结果 SELECT PARSE('Monday, 13 December 2010' AS datetime2 USING 'en-US') AS Result; SELECT PARSE('€345,98' AS money USING 'de-DE') AS Result; SET LANGUAGE 'English'; SELECT PARSE('12/16/2010' AS datetime2) AS Result; /*结果： 2010-12-13 00:00:00.0000000 345.98 2010-12-16 00:00:00.0000000 */ -- TRY_CAST 、TRY_CONVERT、TRY_PARSE (TRY_PARSE 仅用于从字符串转换为日期/时间和数字类型) SELECT TRY_CAST('test' AS float),TRY_CAST(5 AS VARCHAR) SELECT TRY_CONVERT(float,'test'),TRY_CONVERT(VARCHAR,5) SELECT TRY_PARSE('test' AS float),TRY_PARSE('01/01/2011' AS datetime2) /*结果： NULL 5 NULL 5 NULL 2011-01-01 00:00:00.0000000 */ -- CHOOSE ( index, val_1, val_2 [, val_n ] ) #返回指定索引处的项 (即返回第几个值) SELECT CHOOSE ( 3, 'Manager', 'Director', 'Developer', 'Tester' ) AS Result; --结果：Developer -- IIF ( boolean_expression, true_value, false_value ) SELECT IIF ( 10 > 5, 'TRUE', 'FALSE' ) AS Result; SELECT (CASE WHEN 10 > 5 THEN 'TRUE' ELSE 'FALSE' END) AS Result; --结果：TRUE -- 排名函数！ SELECT * ,ROW_NUMBER ( ) OVER (PARTITION BY MyName ORDER BY Num) AS 'ROW_NUMBER' --按顺序排名 ,DENSE_RANK ( ) OVER (PARTITION BY MyName ORDER BY Num) AS 'DENSE_RANK' --同排名的后面排名连续 ,RANK ( ) OVER (PARTITION BY MyName ORDER BY Num) AS 'RANK' --同排名的后面排名不连续 ,NTILE (2) OVER (PARTITION BY MyName ORDER BY Num) AS 'NTILE' --按总数分两组，顺序排名 FROM (VALUES('AA',55),('AA',30.5),('BB',55),('BB',99),('BB',0),('BB',55))AS T(MyName,Num) ORDER BY MyName,Num /* MyName Num ROW_NUMBER DENSE_RANK RANK NTILE ------ ----- ---------- ---------- ------ ----- AA 30.5 1 1 1 1 AA 55.0 2 2 2 2 BB 0.0 1 1 1 1 BB 55.0 2 2 2 1 BB 55.0 3 2 2 2 BB 99.0 4 3 4 2 */ -- 分析函数！ SELECT * ,CUME_DIST( )OVER (PARTITION BY MyName ORDER BY Num) AS 'CUME_DIST' --相对（最大值）位置 ,PERCENT_RANK( )OVER (PARTITION BY MyName ORDER BY Num) AS 'PERCENT_RANK' --相对排名,排名分数参考 CUME_DIST ,FIRST_VALUE (MyName)OVER ( ORDER BY Num ASC) AS 'FIRST_VALUE' --Num 最低的是哪个MyName ,LAST_VALUE (MyName)OVER ( ORDER BY Num ASC) AS 'LAST_VALUE' --Num 排序选底部的那个MyName ,LAG (Num,1,0)OVER (ORDER BY Num ASC) AS 'LAG' --上/下一行(或多行)的值移到下/上一行(或多行)，方便对比 ,LEAD (Num,1,0)OVER (ORDER BY Num ASC) AS 'LEAD' --与LAG一样，排序相反 ,PERCENTILE_CONT(0.5)WITHIN GROUP (ORDER BY Num) OVER (PARTITION BY MyName) AS 'PERCENTILE_CONT' --连续分布计算百分位数 ,PERCENTILE_DISC(0.5)WITHIN GROUP (ORDER BY Num) OVER (PARTITION BY MyName) AS 'PERCENTILE_DISC' --离散分布计算百分位数 FROM (VALUES('AA',55),('AA',30.5),('BB',55),('BB',99),('BB',0),('BB',55))AS T(MyName,Num) ORDER BY Num ASC /* MyName Num CUME_DIST PERCENT_RANK FIRST_VALUE LAST_VALUE LAG LEAD PERCENTILE_CONT PERCENTILE_DISC ------ ----- --------- ------------ ----------- ---------- ----- ----- --------------- --------------- BB 0.0 0.25 0 BB BB 0.0 30.5 55 55.0 AA 30.5 0.5 0 BB AA 0.0 55.0 42.75 30.5 AA 55.0 1 1 BB BB 30.5 55.0 42.75 30.5 BB 55.0 0.75 0.33333 BB BB 55.0 55.0 55 55.0 BB 55.0 0.75 0.33333 BB BB 55.0 99.0 55 55.0 BB 99.0 1 1 BB BB 55.0 0.0 55 55.0 */ /************************************************************** SQL Server 2014 新增的函数 ***************************************************************/ --貌似没有什么 /************************************************************** SQL Server 2016 新增的函数 ***************************************************************/ -- STRING_SPLIT ( string , separator ) #字符分割 SELECT value FROM STRING_SPLIT('A,B,C',',') /*结果： value ----- A B C */ -- STRING_ESCAPE( text , type ) #特殊字符转成带有转义字符的文本（type只支持json） SELECT STRING_ESCAPE(' / \ " ', 'json') AS escapedText; --结果：\ / \\ " -- DATEDIFF_BIG ( datepart , startdate , enddate ) #日期之间的计数 SELECT DATEDIFF(day, '2005-12-12', '2017-10-10'); --以前版本 SELECT DATEDIFF_BIG(day, '2005-12-12', '2017-10-10'); SELECT DATEDIFF_BIG(millisecond, '2005-12-31 23:59:59.9999999', '2006-01-01 00:00:00.0000000'); /*结果： 4320 4320 1 */ -- inputdate AT TIME ZONE timezone #时区时间 SELECT * FROM sys.time_zone_info -- 时区及名称参考 SELECT CONVERT(DATETIME,'2017-10-10') AT TIME ZONE 'Pacific Standard Time' SELECT CONVERT(DATETIME,'2017-10-10') AT TIME ZONE 'China Standard Time' SELECT CONVERT(datetime2(0), '2017-10-10T01:01:00', 126) AT TIME ZONE 'Pacific Standard Time'; SELECT CONVERT(datetime2(0), '2017-10-10T01:01:00', 126) AT TIME ZONE 'China Standard Time'; /*结果： 2017-10-10 00:00:00.000 -07:00 2017-10-10 00:00:00.000 +08:00 2017-10-10 01:01:00 -07:00 2017-10-10 01:01:00 +08:00 */ -- COMPRESS ( expression ) # GZIP算法压缩为varbinary(max) DECLARE @COM varbinary(max) SELECT @COM = COMPRESS(N'{"sport":"Tennis","age": 28,"rank":1,"points":15258, turn":17}') SELECT @COM --结果：0x1F8B08000000000004002DCC410A80300C44D17F94D2B51B85A2780E2FE042A414AAD4BA12EFEE……（略） -- DECOMPRESS ( expression )#解压缩 SELECT CAST(DECOMPRESS(@COM) AS NVARCHAR(MAX)) --结果：{"sport":"Tennis","age": 28,"rank":1,"points":15258, turn":17} -- SESSION_CONTEXT(N'key') #获取指定的键的值 EXEC sp_set_session_context 'user_id', 4; --设置键值 SELECT SESSION_CONTEXT(N'user_id'); --结果：4 -- ISJSON ( expression ) #测试字符串是否包含有效JSON DECLARE @param1 NVARCHAR(MAX) DECLARE @param2 NVARCHAR(MAX) SET @param1 = N' "id" : 2,"info": { "name": "John", "surname": "Smith" }, "age": 25 ' SET @param2 = N'[{ "id" : 2,"info": { "name": "John", "surname": "Smith" }, "age": 25 }]' SELECT ISJSON(@param1) as P1, ISJSON(@param2) as P2 GO /*结果： P1 P2 -- -- 0 1 */ -- JSON_VALUE ( expression , path ) #从 JSON 字符串中提取值 DECLARE @param NVARCHAR(MAX) SET @param = N'{ "id" : 2,"info": { "name": "John", "surname": "Smith" }, "age": 25 }' SELECT JSON_VALUE(@param,'$.id') as P1,JSON_VALUE(@param,'$.info.name')as P2 GO /*结果： P1 P2 -- ---- 2 John */ -- JSON_QUERY ( expression [ , path ] ) #从 JSON 字符串中提取对象或数组 DECLARE @param NVARCHAR(MAX) SET @param = N'{ "id" : 2,"info": { "name": "John", "surname": "Smith" }, "age": 25 }' SELECT JSON_QUERY(@param,'$.info') GO --结果：{ "name": "John", "surname": "Smith" } -- JSON_MODIFY ( expression , path , newValue ) #更新的 JSON 字符串中属性的值并返回更新的 JSON 字符串 DECLARE @param NVARCHAR(MAX) SET @param = N'{ "id" : 2,"info": { "name": "John", "surname": "Smith" }, "age": 25 }' SELECT JSON_MODIFY(@param,'$.info.surname','newValue') GO --结果：{ "id" : 2,"info": { "name": "John", "surname": "newValue" }, "age": 25 } /************************************************************** SQL Server 2017 新增的函数 ***************************************************************/ -- CONCAT_WS ( separator, argument1, argument1 [, argumentN]… ) #按第一个分隔符连接后面的字符 SELECT CONCAT_WS( ' - ', 1, 'kk', '12dd') --结果：1 - kk - 12dd -- TRANSLATE ( inputString, characters, translations) #整体对应替换 SELECT TRANSLATE('2*[3+4]/{7-2}', '[]{}', '()()'); SELECT REPLACE(REPLACE(REPLACE(REPLACE('2*[3+4]/{7-2}','[','('), ']', ')'), '{', '('), '}', ')'); SELECT TRANSLATE('2*[3+4]/[7-2]', '[2', '61'); /*结果： 2*(3+4)/(7-2) 2*(3+4)/(7-2) 1*63+4]/67-1] */ -- TRIM ( [ characters FROM ] string ) #删除字符串左右空格字符 SELECT TRIM( ' test ') AS Result,LTRIM(RTRIM(' test ')) -- STRING_AGG ( expression, separator ) #同列字符相连成一行 SELECT STRING_AGG (MyName, CHAR(13)) FROM (VALUES('AAAA'),('BBBBB'),('CCCCCC') )AS T(MyName) SELECT STRING_AGG (MyName,',') FROM (VALUES('AAAA'),('BBBBB'),('CCCCCC') )AS T(MyName) SELECT STRING_AGG (MyName,',') WITHIN GROUP(ORDER BY id DESC ) FROM (VALUES(1,'AAAA'),(1,'BBBBB'),(2,'CCCCCC'))AS T(id,MyName) /*结果： AAAA BBBBB CCCCCC AAAA,BBBBB,CCCCCC CCCCCC,BBBBB,AAAA */