Oracle Shared Pool优化思路
shared pool主要由保存数据字典的data_dictionary和保存SQL和PL/SQL代码和执行计划的library cache组成 。还包括其它供系统不同特
shared pool主要由保存数据字典的data_dictionary和保存SQL和PL/SQL代码和执行计划的library cache组成 。还包括其它供系统不同特性和技术使用的若干缓冲区,如为shared server模式提供的UGA等。
优化shared pool的思路:
1)根据设置经验,例如,可设置shared_pool_size=sga_target*(10%~15%)。
2)重点关注保存SQL和PL/SQL代码和执行计划的library cache相关指标。查看AWR报告Load Profile部分,分析Hard Parses/s等指标。分析Instance Efficiency Percentages (Target 100%)中Library Hit %、Execute to Parse %、Soft Parse %等
需要关注的等待事件:
Latch:library cache
Latch:shared pool
3)查看Time Model Statistics中与shared pool相关指标(parse time elapsed与hard parse elapsed time)。
如果hard parse elapsed time所占比例较高,说明应用的语句共享性存在严重问题。
优化方法:
1)评估语句共享性
Execute to Parse %=(execute次数-Parse次数)/Execute次数*100%
如果Execute to Parse %太低,说明解析次数非常高,系统整体共享性差。一般该指标达到70%以上,就说明语句共享性不错。
AWR报告中Library Hit %、Soft Parse %和Hard Parses/s。Parse包含Hard Parse与Soft Parse次数,但我们应关注Hard Parses。
查询非共享的sql语句(执行次数为1):
select sql_text from v$sqlarea where executions=1 order by upper(sql_text);
2)通过shared pool advisory设置合理的shared_pool_size。也可以通过设置shared_pool_reserved_size参数,使一些比较大的PL/SQL对象常驻内存中,减少shared pool出现碎片的可能性。
3)合理设置large_pool_size参数
large pool缓冲区用于备份恢复操作、并行处理、ASM、共享连接模式、模拟异步I/O操作等场景,应合理设置large_pool_size以避免使用shared pool缓冲区,加剧shared pool缓冲区空间的紧张和产生碎片的可能性。
注意:并不是所有的sql都需要共享,对于统计报表类sql因其单笔事务的资源消耗大,并发量不高的特点,应该保证其执行计划的最优,这时候不用绑定变量。

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

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











The main role of MySQL in web applications is to store and manage data. 1.MySQL efficiently processes user information, product catalogs, transaction records and other data. 2. Through SQL query, developers can extract information from the database to generate dynamic content. 3.MySQL works based on the client-server model to ensure acceptable query speed.

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.

Compared with other programming languages, MySQL is mainly used to store and manage data, while other languages such as Python, Java, and C are used for logical processing and application development. MySQL is known for its high performance, scalability and cross-platform support, suitable for data management needs, while other languages have advantages in their respective fields such as data analytics, enterprise applications, and system programming.

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 basic operations of MySQL include creating databases, tables, and using SQL to perform CRUD operations on data. 1. Create a database: CREATEDATABASEmy_first_db; 2. Create a table: CREATETABLEbooks(idINTAUTO_INCREMENTPRIMARYKEY, titleVARCHAR(100)NOTNULL, authorVARCHAR(100)NOTNULL, published_yearINT); 3. Insert data: INSERTINTObooks(title, author, published_year)VA

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.

InnoDBBufferPool reduces disk I/O by caching data and indexing pages, improving database performance. Its working principle includes: 1. Data reading: Read data from BufferPool; 2. Data writing: After modifying the data, write to BufferPool and refresh it to disk regularly; 3. Cache management: Use the LRU algorithm to manage cache pages; 4. Reading mechanism: Load adjacent data pages in advance. By sizing the BufferPool and using multiple instances, database performance can be optimized.

MySQL efficiently manages structured data through table structure and SQL query, and implements inter-table relationships through foreign keys. 1. Define the data format and type when creating a table. 2. Use foreign keys to establish relationships between tables. 3. Improve performance through indexing and query optimization. 4. Regularly backup and monitor databases to ensure data security and performance optimization.
