通过案例学调优之--和SHAREDPOOL相关的主要Latch3.1、和SHAREDPOOL相关的主要Latch有:Latch:sharedpoolLatch:librarycache我们知道Oracle通过SHAREDPOOL来实现SQ
的相关信息, 如:
, 也是通过将不同的
的相 关
Library Cache LatchLibrary Cache SHRAE POOL 。 接下来就开始进行硬解析过程,将执行解析后的执行计划等信息记录到 Library Cache ,一 直到硬解析结束。(硬解析)
的话,根据上面的逻辑那说明数 据库中存在大量的硬解析,这个时候就要查找那些
10:56:01 SCOTT@ prod >show parameter cursor NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ cursor_sharing string similar cursor_space_for_time boolean FALSE open_cursors integer 300 session_cached_cursors integer 50 1、session1:以scott建立测试表 11:44:26 SYS@ prod >conn scott/tiger Connected. 11:01:41 SCOTT@ prod >select * from v$mystat where rownum=1; SID STATISTIC# VALUE ---------- ---------- ---------- 1 0 0 10:56:09 SCOTT@ prod >create table test as select rownum as col1 ,rownum col2 from user_objects 10:58:38 2 ; Table created. 2、建立测试表直方图 10:58:51 SCOTT@ prod >exec dbms_stats.gather_table_stats(user,'TEST',method_opt=>'for columns col1 size 3'); PL/SQL procedure successfully completed. 10:59:36 SCOTT@ prod >select column_name,num_buckets,histogram from user_tab_col_statistics 11:00:43 2 where table_name='TEST'; COLUMN_NAME NUM_BUCKETS HISTOGRAM ------------------------------ ----------- --------------- COL1 3 HEIGHT BALANCED 11:01:35 sys@ prod >ALTER SYSTem flush shared_pool; System altered. 3、session 2:以scott建立另一个会话 11:03:44 SCOTT@ prod >select * from v$mystat where rownum=1; SID STATISTIC# VALUE ---------- ---------- ---------- 44 0 0 11:04:01 SCOTT@ prod >create table test1 as select rownum as col1 ,rownum col2 from user_objects; Table created. 11:04:36 SCOTT@ prod >exec dbms_stats.gather_table_stats(user,'TEST1',method_opt=>'for columns col1 size 3'); PL/SQL procedure successfully completed. 11:05:04 SCOTT@ prod >select column_name,num_buckets,histogram from user_tab_col_statistics 11:05:19 2 where table_name='TEST1'; COLUMN_NAME NUM_BUCKETS HISTOGRAM ------------------------------ ----------- --------------- COL1 3 HEIGHT BALANCED 11:05:30 sys@ prod >ALTER SYSTem flush shared_pool; System altered. 4、在session 1执行以下操作 11:02:42 SCOTT@ prod >begin 11:06:28 2 for i in 1..50000 loop 11:06:40 3 execute immediate 'select * from test where col1='||i; 11:07:08 4 end loop; 11:07:11 5 end; 11:07:13 6 / 在session 2执行同样地操作 11:07:57 SCOTT@ prod >begin 11:08:01 2 for i in 1..50000 loop 11:08:01 3 execute immediate 'select * from test1 where col1='||i; 11:08:01 4 end loop; 11:08:01 5 end; 11:08:02 6 / 5、查看session event 11:11:36 sys@ prod > select sid,event,p1,p1text,p2,p2text from v$session where sid in (1,44) SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch: shared pool 537557404 address 293 number 44 latch: shared pool 537557404 address 293 number Elapsed: 00:00:00.00 11:11:38 sys@ prod >/ SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch: shared pool 537557404 address 293 number 44 latch: row cache objects 828539960 address 270 number Elapsed: 00:00:00.00 11:11:39 sys@ prod >/ SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch: shared pool 537557404 address 293 number 44 latch: shared pool 537557404 address 293 number Elapsed: 00:00:00.00 11:11:41 sys@ prod >/ SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch: shared pool 537557404 address 293 number 44 latch: row cache objects 828007508 address 270 number Elapsed: 00:00:00.00 11:11:42 sys@ prod >/ SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch: shared pool 537557404 address 293 number 44 latch: shared pool 537557404 address 293 number 11:12:32 sys@ prod >/ SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch free 821793596 address 274 number 44 latch: shared pool 537557404 address 293 number sys@ prod >select sid,event,p1,p1text,p2,p2text from v$session where sid in (1,44) SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 latch: shared pool 537557404 address 293 number 44 library cache: mutex X 1307903034 idn 65536 value 11:14:58 sys@ prod >select sid,event,p1,p1text,p2,p2text from v$session where sid in (1,44) SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 library cache: mutex X 3413592168 idn 2883584 value 44 latch: row cache objects 828539960 address 270 number 11:15:18 sys@ prod >select sid,event,p1,p1text,p2,p2text from v$session where sid in (1,44) SID EVENT P1 P1TEXT P2 P2TEXT ---------- ------------------------------ ---------- ------------------------------ ---------- ------------------------------ 1 SQL*Net message from client 1650815232 driver id 1 #bytes 44 SQL*Net message from client 1650815232 driver id 1 #bytes 从上面的过程可以看到,大量的硬解析将导致严重的 library cache latch(mutex) 和 shared pool latch竞争。 6、查看Library cache中sql情况 sys@ prod >select * 2 from (select sql_id,child_number,child_latch,executions,sql_text 3 from v$sql 4 where sql_text like '%select * from test1 where col1%' 5 and sql_text not like '%v$sql%' 6 and sql_text not like '%begin%' 7 order by child_number desc) 8* where rownum select sql_id,hash_value,address,version_count from v$sqlarea where sql_id='6tsrjxza4gvur'; SQL_ID HASH_VALUE ADDRESS VERSION_COUNT ------------- ---------- -------- ------------- 6tsrjxza4gvur 3561484119 2E8CF368 3885
MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.

SQL commands in MySQL can be divided into categories such as DDL, DML, DQL, DCL, etc., and are used to create, modify, delete databases and tables, insert, update, delete data, and perform complex query operations. 1. Basic usage includes CREATETABLE creation table, INSERTINTO insert data, and SELECT query data. 2. Advanced usage involves JOIN for table joins, subqueries and GROUPBY for data aggregation. 3. Common errors such as syntax errors, data type mismatch and permission problems can be debugged through syntax checking, data type conversion and permission management. 4. Performance optimization suggestions include using indexes, avoiding full table scanning, optimizing JOIN operations and using transactions to ensure data consistency.

InnoDB achieves atomicity through undolog, consistency and isolation through locking mechanism and MVCC, and persistence through redolog. 1) Atomicity: Use undolog to record the original data to ensure that the transaction can be rolled back. 2) Consistency: Ensure the data consistency through row-level locking and MVCC. 3) Isolation: Supports multiple isolation levels, and REPEATABLEREAD is used by default. 4) Persistence: Use redolog to record modifications to ensure that data is saved for a long time.

MySQL's position in databases and programming is very important. It is an open source relational database management system that is widely used in various application scenarios. 1) MySQL provides efficient data storage, organization and retrieval functions, supporting Web, mobile and enterprise-level systems. 2) It uses a client-server architecture, supports multiple storage engines and index optimization. 3) Basic usages include creating tables and inserting data, and advanced usages involve multi-table JOINs and complex queries. 4) Frequently asked questions such as SQL syntax errors and performance issues can be debugged through the EXPLAIN command and slow query log. 5) Performance optimization methods include rational use of indexes, optimized query and use of caches. Best practices include using transactions and PreparedStatemen

MySQL is suitable for small and large enterprises. 1) Small businesses can use MySQL for basic data management, such as storing customer information. 2) Large enterprises can use MySQL to process massive data and complex business logic to optimize query performance and transaction processing.

InnoDB effectively prevents phantom reading through Next-KeyLocking mechanism. 1) Next-KeyLocking combines row lock and gap lock to lock records and their gaps to prevent new records from being inserted. 2) In practical applications, by optimizing query and adjusting isolation levels, lock competition can be reduced and concurrency performance can be improved.


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