1 LogMiner 常用场景 1.1 用 LogMiner 跟踪特定用户所做的更改 本节例子,查出用户joedevo在特定时间范围内对数据库所做的所有操作,步骤如下: 1.创建LogMiner字典 使用LogMiner分析joedevo相关的数据,必须在joedevo对它的所有表做任何的更改操作前创建Lo
1 LogMiner常用场景
1.1 用LogMiner跟踪特定用户所做的更改
本节例子,查出用户joedevo在特定时间范围内对数据库所做的所有操作,步骤如下:
1.创建LogMiner字典
使用LogMiner分析joedevo相关的数据,必须在joedevo对它的所有表做任何的更改操作前创建LogMiner字典或者直接使用在线字典,本例使用已提取到重做日志文件中的LogMiner字典。
2.添加重做日志文件
在joedevo对数据库做了一些更改操作之后,可以指定对应的重做日志文件来分析:
SQL> EXECUTE DBMS_LOGMNR.ADD_LOGFILE( - LOGFILENAME => 'log1orc1.ora', - OPTIONS => DBMS_LOGMNR.NEW);
根据需要添加其他的重做日志文件:
SQL> EXECUTE DBMS_LOGMNR.ADD_LOGFILE( - LOGFILENAME => 'log2orc1.ora', - OPTIONS => DBMS_LOGMNR.ADDFILE);
3.开启LogMiner并限制查询范围
SQL> EXECUTE DBMS_LOGMNR.START_LOGMNR( - DICTFILENAME => 'orcldict.ora', - STARTTIME => TO_DATE('01-Jan-1998 08:30:00','DD-MON-YYYY HH:MI:SS'), - ENDTIME => TO_DATE('01-Jan-1998 08:45:00', 'DD-MON-YYYY HH:MI:SS'));
4.查询V$LOGMNR_CONTENTS
SQL> SELECT SQL_REDO, SQL_UNDO FROM V$LOGMNR_CONTENTS WHERE USERNAME = 'joedevo' AND SEG_NAME = 'salary';
5.停止LogMiner
SQL> EXECUTE DBMS_LOGMNR.END_LOGMNR();
1.2 用LogMiner做表的访问统计
本节案例中,假设你管理者一个直营数据库,想确定1月份2周内客户带来的生产效益,假定你已经创建了LogMiner字典并添加了相关的重做日志文件,接下来的步骤如下:
1.开始LogMiner并限制查询范围
SQL> EXECUTE DBMS_LOGMNR.START_LOGMNR( - STARTTIME => TO_DATE('07-Jan-2003 08:30:00','DD-MON-YYYY HH:MI:SS'), - ENDTIME => TO_DATE('21-Jan-2003 08:45:00','DD-MON-YYYY HH:MI:SS'), - DICTFILENAME => '/usr/local/dict.ora');
2.查询V$LOGMNR_CONTENTS
查询V$LOGMNR_CONTENTS确定指定时间范围内哪些表发生了变化:
SQL> SELECT SEG_OWNER, SEG_NAME, COUNT(*) AS Hits FROM V$LOGMNR_CONTENTS WHERE SEG_NAME NOT LIKE '%$' GROUP BY SEG_OWNER, SEG_NAME ORDER BY Hits DESC;
3.停止LogMiner
SQL> EXECUTE DBMS_LOGMNR.END_LOGMNR();

InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Key metrics for EXPLAIN commands include type, key, rows, and Extra. 1) The type reflects the access type of the query. The higher the value, the higher the efficiency, such as const is better than ALL. 2) The key displays the index used, and NULL indicates no index. 3) rows estimates the number of scanned rows, affecting query performance. 4) Extra provides additional information, such as Usingfilesort prompts that it needs to be optimized.

Usingtemporary indicates that the need to create temporary tables in MySQL queries, which are commonly found in ORDERBY using DISTINCT, GROUPBY, or non-indexed columns. You can avoid the occurrence of indexes and rewrite queries and improve query performance. Specifically, when Usingtemporary appears in EXPLAIN output, it means that MySQL needs to create temporary tables to handle queries. This usually occurs when: 1) deduplication or grouping when using DISTINCT or GROUPBY; 2) sort when ORDERBY contains non-index columns; 3) use complex subquery or join operations. Optimization methods include: 1) ORDERBY and GROUPB

MySQL/InnoDB supports four transaction isolation levels: ReadUncommitted, ReadCommitted, RepeatableRead and Serializable. 1.ReadUncommitted allows reading of uncommitted data, which may cause dirty reading. 2. ReadCommitted avoids dirty reading, but non-repeatable reading may occur. 3.RepeatableRead is the default level, avoiding dirty reading and non-repeatable reading, but phantom reading may occur. 4. Serializable avoids all concurrency problems but reduces concurrency. Choosing the appropriate isolation level requires balancing data consistency and performance requirements.

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

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.


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