以start with ename =
在Oracle 10g下，来到scott用户下，分别以层次 1,2,3,4上的节点做实验：
当start with是根节点(level=1)，要查其子节点，connect by pump和emp都是被扫描4次(总的层次)。
当start with是根节点(level=2)，要查其子节点，，connect by pump和emp被扫描3次。
当start with是根节点(level=3)，要查其子节点，connect by pump和emp被扫描2次。
当start with是根节点(level=4)，要查其子节点，connect by pump和emp被扫描1次。
注意的是：leve=2,level=3不是叶子节点，如果是叶子节点，那connect by pump和emp只扫描一次。
Operation Name Starts
FILTER
TABLE ACCESS FULL EMP 1
HASH JOIN
CONNECT BY PUMP 4
TABLE ACCESS FULL EMP 4
我来解读上面的执行计划，以start with ename = 'KING'为例，显示对EMP通过"ENAME"='KING'过滤找到节点作为根节点（集合A），通过集合A到下一级所有满足条件的节点(集合B)，通过集合B再到下一级所有满足条件的节点(集合C)，树有几级就CONNECT BY PUMP几次。
Oracle 函数中游标及递归的应用
Oracle递归函数
Oracle 递归查询
Oracle递归START WITH...CONNECT BY PRIOR子句用法
Oracle 使用递归的性能提示
Oracle递归查询(start with)
SQL> set pagesize 100
SQL> --根节点 level=1
SQL> select e.empno, e.ename, e.mgr, e.deptno,level
from emp e
start with ename = 'KING'
connect by prior empno = mgr;
EMPNO ENAME MGR DEPTNO LEVEL
---------- ---------- ---------- ---------- ----------
7839 KING 10 1
7566 JONES 7839 20 2
7788 SCOTT 7566 20 3
7876 ADAMS 7788 20 4
7902 FORD 7566 20 3
7369 SMITH 7902 20 4
7698 BLAKE 7839 30 2
7499 ALLEN 7698 30 3
7521 WARD 7698 30 3
7654 MARTIN 7698 30 3
7844 TURNER 7698 30 3
7900 JAMES 7698 30 3
7782 CLARK 7839 10 2
7934 MILLER 7782 10 3
已选择14行。
SQL> select * from table(dbms_xplan.display_cursor(null,null,'allstats last'));
PLAN_TABLE_OUTPUT
-----------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------
SQL_ID 6as71p9t5arg3, child number 0
-------------------------------------
select e.empno, e.ename, e.mgr, e.deptno,level from emp e start with ename = 'KING' connect by prior empno
= mgr
Plan hash value: 3364448299
-----------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------
|* 1 | CONNECT BY WITH FILTERING| | 1 | | 14 |00:00:00.01 | 35 | 9216 | 9216 | 8192 (0)|
|* 2 | FILTER | | 1 | | 1 |00:00:00.01 | 7 | | | |
| 3 | TABLE ACCESS FULL | EMP | 1 | 14 | 14 |00:00:00.01 | 7 | | | |
|* 4 | HASH JOIN | | 4 | | 13 |00:00:00.01 | 28 | 1036K| 1036K| 776K (0)|
| 5 | CONNECT BY PUMP | | 4 | | 14 |00:00:00.01 | 0 | | | |
| 6 | TABLE ACCESS FULL | EMP | 4 | 14 | 56 |00:00:00.01 | 28 | | | |
| 7 | TABLE ACCESS FULL | EMP | 0 | 14 | 0 |00:00:00.01 | 0 | | | |
-----------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("ENAME"='KING')
2 - filter("ENAME"='KING')
4 - access("MGR"=NULL)
SQL> --level=2
SQL> select e.empno, e.ename, e.mgr, e.deptno,level
from emp e
start with ename = 'JONES'
connect by prior empno = mgr;
EMPNO ENAME MGR DEPTNO LEVEL
---------- ---------- ---------- ---------- ----------
7566 JONES 7839 20 1
7788 SCOTT 7566 20 2
7876 ADAMS 7788 20 3
7902 FORD 7566 20 2
7369 SMITH 7902 20 3
SQL> select * from table(dbms_xplan.display_cursor(null,null,'allstats last'));
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------
SQL_ID 2bcjwvmbyg7a5, child number 1
-------------------------------------
select e.empno, e.ename, e.mgr, e.deptno,level from emp e start with ename = 'JONES' connect by prior empno
= mgr
Plan hash value: 3364448299
-----------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------
|* 1 | CONNECT BY WITH FILTERING| | 1 | | 5 |00:00:00.01 | 28 | 9216 | 9216 | 8192 (0)|
|* 2 | FILTER | | 1 | | 1 |00:00:00.01 | 7 | | | |
| 3 | TABLE ACCESS FULL | EMP | 1 | 14 | 14 |00:00:00.01 | 7 | | | |
|* 4 | HASH JOIN | | 3 | | 4 |00:00:00.01 | 21 | 1036K| 1036K| 404K (0)|
| 5 | CONNECT BY PUMP | | 3 | | 5 |00:00:00.01 | 0 | | | |
| 6 | TABLE ACCESS FULL | EMP | 3 | 14 | 42 |00:00:00.01 | 21 | | | |
| 7 | TABLE ACCESS FULL | EMP | 0 | 14 | 0 |00:00:00.01 | 0 | | | |
-----------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("ENAME"='JONES')
2 - filter("ENAME"='JONES')
4 - access("MGR"=NULL)
SQL> --level=3
SQL> select e.empno, e.ename, e.mgr, e.deptno,level
from emp e
start with ename = 'SCOTT'
connect by prior empno = mgr;
EMPNO ENAME MGR DEPTNO LEVEL
---------- ---------- ---------- ---------- ----------
7788 SCOTT 7566 20 1
7876 ADAMS 7788 20 2
SQL> select * from table(dbms_xplan.display_cursor(null,null,'allstats last'));
PLAN_TABLE_OUTPUT
-----------------------------------------------------------------------------------------------------------------------
SQL_ID fqf7r75c9atqv, child number 0
-------------------------------------
select e.empno, e.ename, e.mgr, e.deptno,level from emp e start with ename = 'SCOTT' connect by prior empno
= mgr
Plan hash value: 3364448299
-----------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------
|* 1 | CONNECT BY WITH FILTERING| | 1 | | 2 |00:00:00.01 | 21 | 9216 | 9216 | 8192 (0)|
|* 2 | FILTER | | 1 | | 1 |00:00:00.01 | 7 | | | |
| 3 | TABLE ACCESS FULL | EMP | 1 | 14 | 14 |00:00:00.01 | 7 | | | |
|* 4 | HASH JOIN | | 2 | | 1 |00:00:00.01 | 14 | 1036K| 1036K| 282K (0)|
| 5 | CONNECT BY PUMP | | 2 | | 2 |00:00:00.01 | 0 | | | |
| 6 | TABLE ACCESS FULL | EMP | 2 | 14 | 28 |00:00:00.01 | 14 | | | |
| 7 | TABLE ACCESS FULL | EMP | 0 | 14 | 0 |00:00:00.01 | 0 | | | |
-----------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("ENAME"='SCOTT')
2 - filter("ENAME"='SCOTT')
4 - access("MGR"=NULL)
MySQL: BLOB and other no-sql storage, what are the differences?May 13, 2025 am 12:14 AMMySQL'sBLOBissuitableforstoringbinarydatawithinarelationaldatabase,whileNoSQLoptionslikeMongoDB,Redis,andCassandraofferflexible,scalablesolutionsforunstructureddata.BLOBissimplerbutcanslowdownperformancewithlargedata;NoSQLprovidesbetterscalabilityand
MySQL Add User: Syntax, Options, and Security Best PracticesMay 13, 2025 am 12:12 AMToaddauserinMySQL,use:CREATEUSER'username'@'host'IDENTIFIEDBY'password';Here'showtodoitsecurely:1)Choosethehostcarefullytocontrolaccess.2)SetresourcelimitswithoptionslikeMAX_QUERIES_PER_HOUR.3)Usestrong,uniquepasswords.4)EnforceSSL/TLSconnectionswith
MySQL: How to avoid String Data Types common mistakes?May 13, 2025 am 12:09 AMToavoidcommonmistakeswithstringdatatypesinMySQL,understandstringtypenuances,choosetherighttype,andmanageencodingandcollationsettingseffectively.1)UseCHARforfixed-lengthstrings,VARCHARforvariable-length,andTEXT/BLOBforlargerdata.2)Setcorrectcharacters
MySQL: String Data Types and ENUMs?May 13, 2025 am 12:05 AMMySQloffersechar, Varchar, text, Anddenumforstringdata.usecharforfixed-Lengthstrings, VarcharerForvariable-Length, text forlarger text, AndenumforenforcingdataAntegritywithaetofvalues.
MySQL BLOB: how to optimize BLOBs requestsMay 13, 2025 am 12:03 AMOptimizing MySQLBLOB requests can be done through the following strategies: 1. Reduce the frequency of BLOB query, use independent requests or delay loading; 2. Select the appropriate BLOB type (such as TINYBLOB); 3. Separate the BLOB data into separate tables; 4. Compress the BLOB data at the application layer; 5. Index the BLOB metadata. These methods can effectively improve performance by combining monitoring, caching and data sharding in actual applications.
Adding Users to MySQL: The Complete TutorialMay 12, 2025 am 12:14 AMMastering the method of adding MySQL users is crucial for database administrators and developers because it ensures the security and access control of the database. 1) Create a new user using the CREATEUSER command, 2) Assign permissions through the GRANT command, 3) Use FLUSHPRIVILEGES to ensure permissions take effect, 4) Regularly audit and clean user accounts to maintain performance and security.
Mastering MySQL String Data Types: VARCHAR vs. TEXT vs. CHARMay 12, 2025 am 12:12 AMChooseCHARforfixed-lengthdata,VARCHARforvariable-lengthdata,andTEXTforlargetextfields.1)CHARisefficientforconsistent-lengthdatalikecodes.2)VARCHARsuitsvariable-lengthdatalikenames,balancingflexibilityandperformance.3)TEXTisidealforlargetextslikeartic
MySQL: String Data Types and Indexing: Best PracticesMay 12, 2025 am 12:11 AMBest practices for handling string data types and indexes in MySQL include: 1) Selecting the appropriate string type, such as CHAR for fixed length, VARCHAR for variable length, and TEXT for large text; 2) Be cautious in indexing, avoid over-indexing, and create indexes for common queries; 3) Use prefix indexes and full-text indexes to optimize long string searches; 4) Regularly monitor and optimize indexes to keep indexes small and efficient. Through these methods, we can balance read and write performance and improve database efficiency.
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Dreamweaver Mac version
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function
MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.
SecLists
SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.
