import mysql.connector # 连接到数据库 db = mysql.connector.connect( host="localhost", user="yourusername", password="yourpassword", database="yourdatabase" ) cursor = db.cursor() # 假设我们要获取id在1到10之间的所有用户 for i in range(1, 11): query = "SELECT * FROM users WHERE id...
接下来,编写存储过程: DELIMITER//CREATEPROCEDUREcalculate_salary()BEGINDECLAREdoneINTDEFAULTFALSE;DECLAREemployee_idINT;DECLAREemployee_nameVARCHAR(50);DECLAREemployee_salaryDECIMAL(10,2);DECLAREemployee_departmentVARCHAR(50);DECLAREcurCURSORFORSELECTid,name,salary,departmentFROMemployee;DECLARECONTINUEHANDLERFORNOT...
mysql> delete from t1// Query OK, 0 rows affected (0.00 sec) mysql> call pro10()// Query OK, 1 row affected (0.00 sec) mysql> select * from t1// +——-+ | filed | +——-+ | 0 | | 1 | | 2 | | 3 | | 4 | +——-+ 5 rows in set (0.00 sec) 1. 2. 3. 4. ...
import mysql.connector # 连接到MySQL数据库 db = mysql.connector.connect( host="localhost", user="yourusername", password="yourpassword", database="yourdatabase" ) cursor = db.cursor() # 假设我们要查询id从1到10的用户信息 for i in range(1, 11): query = "SELECT * FROM users WHERE id...
(in_job varchardefault'AC_MGR')ISSELECTempname,salaryFROMemp where job=in_job;v_job emp.job%TYPE;BEGINv_job:='PU_CLERK';FORr_empINc_emp(v_job)LOOPDBMS_OUTPUT.PUT_LINE('DEPT '||v_job||': '||r_emp.empname||'''s salary is '||r_emp.salary);ENDLOOP;END;/QueryOK,0rows ...
GaussDB(for MySQL)解决Big IN性能问题的方法是将大IN谓词转换为IN子查询。也就是说，IN谓词的形式为 深色代码主题 复制 columnIN(const1, const2, ...)转换为等效的IN子查询：深色代码主题 复制 columnIN(SELECT...FROMtemporary_table)经过上述的变化，IN函数查询变成了一个IN子查询，并且该子查询是非相关子...
(select #2)) (cost=25.89 rows=26) -> Index lookup on lineitem using LINEITEM_FK2 (L_PARTKEY=part.P_PARTKEY) (cost=25.89 rows=26) -> Select #2 (subquery in condition; dependent) -> Aggregate: avg(lineitem.L_QUANTITY) -> Index lookup on lineitem using LINEITEM_FK2 (L_PARTKEY...
SELECT..., sw_sowing.type, ...GROUPBY..., sw_sowing.typeHAVINGtypeNOTIN('ER','PR') If so, then it would be more efficient to move the test againsttypeintoWHEREinstead ofHAVING. Only considering the query itself, Tag query blocks withQB_nas follows ...
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存储角度=> 首先,要创建索引,就要占用存储空间。我们每创建一个索引,MySQL就会搞出一个B+树,每棵B+树都要占用很多的磁盘空间啊,所以搞太多索引,也是很耗费磁盘空间的。 增删改维护角度=> 其次,你在进行增删改的时候,每次都需要维护各个索引的数据有序性,因为每个B+树都要求页内是按照值大小来排序的,页之间也...