几个常用存储引擎的特点
下面我们重点介绍几种常用的存储引擎并对比各个存储引擎之间的区别和推荐使用方式。
特点 | Myisam | BDB | Memory | InnoDB | Archive |
---|---|---|---|---|---|
存储限制 | 没有 | 没有 | 有 | 64TB | 没有 |
事务安全 | 支持 | 支持 | |||
锁机制 | 表锁 | 页锁 | 表锁 | 行锁 | 行锁 |
B树索引 | 支持 | 支持 | 支持 | 支持 | |
哈希索引 | 支持 | 支持 | |||
全文索引 | 支持 | ||||
集群索引 | 支持 | ||||
数据缓存 | 支持 | 支持 | |||
索引缓存 | 支持 | 支持 | 支持 | ||
数据可压缩 | 支持 | 支持 | |||
空间使用 | 低 | 低 | N/A | 高 | 非常低 |
内存使用 | 低 | 低 | 中等 | 高 | 低 |
批量插入的速度 | 高 | 高 | 高 | 低 | 非常高 |
支持外键 | 支持 |
最常使用的2种存储引擎:
•Myisam是Mysql的默认存储引擎。当create创建新表时,未指定新表的存储引擎时,默认使用Myisam。每个MyISAM在磁盘上存储成三个文件。文件名都和表名相同,扩展名分别是.frm(存储表定义)、.MYD (MYData,存储数据)、.MYI (MYIndex,存储索引)。数据文件和索引文件可以放置在不同的目录,平均分布io,获得更快的速度。
•InnoDB存储引擎提供了具有提交、回滚和崩溃恢复能力的事务安全。但是对比Myisam的存储引擎,InnoDB写的处理效率差一些并且会占用更多的磁盘空间以保留数据和索引。
如何选择合适的存储引擎
选择标准:根据应用特点选择合适的存储引擎,对于复杂的应用系统可以根据实际情况选择多种存储引擎进行组合。
下面是常用存储引擎的适用环境:
1.MyISAM:默认的MySQL插件式存储引擎,它是在Web、数据仓储和其他应用环境下最常使用的存储引擎之一
2.InnoDB:用于事务处理应用程序,具有众多特性,包括ACID事务支持。
3.Memory:将所有数据保存在RAM中,在需要快速查找引用和其他类似数据的环境下,可提供极快的访问。
4.Merge:允许MySQL DBA或开发人员将一系列等同的MyISAM表以逻辑方式组合在一起,并作为1个对象引用它们。对于诸如数据仓储等VLDB环境十分适合。

Stored procedures are precompiled SQL statements in MySQL for improving performance and simplifying complex operations. 1. Improve performance: After the first compilation, subsequent calls do not need to be recompiled. 2. Improve security: Restrict data table access through permission control. 3. Simplify complex operations: combine multiple SQL statements to simplify application layer logic.

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

The reasons why MySQL is widely used in various projects include: 1. High performance and scalability, supporting multiple storage engines; 2. Easy to use and maintain, simple configuration and rich tools; 3. Rich ecosystem, attracting a large number of community and third-party tool support; 4. Cross-platform support, suitable for multiple operating systems.

The steps for upgrading MySQL database include: 1. Backup the database, 2. Stop the current MySQL service, 3. Install the new version of MySQL, 4. Start the new version of MySQL service, 5. Recover the database. Compatibility issues are required during the upgrade process, and advanced tools such as PerconaToolkit can be used for testing and optimization.

MySQL backup policies include logical backup, physical backup, incremental backup, replication-based backup, and cloud backup. 1. Logical backup uses mysqldump to export database structure and data, which is suitable for small databases and version migrations. 2. Physical backups are fast and comprehensive by copying data files, but require database consistency. 3. Incremental backup uses binary logging to record changes, which is suitable for large databases. 4. Replication-based backup reduces the impact on the production system by backing up from the server. 5. Cloud backups such as AmazonRDS provide automation solutions, but costs and control need to be considered. When selecting a policy, database size, downtime tolerance, recovery time, and recovery point goals should be considered.

MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

Optimizing database schema design in MySQL can improve performance through the following steps: 1. Index optimization: Create indexes on common query columns, balancing the overhead of query and inserting updates. 2. Table structure optimization: Reduce data redundancy through normalization or anti-normalization and improve access efficiency. 3. Data type selection: Use appropriate data types, such as INT instead of VARCHAR, to reduce storage space. 4. Partitioning and sub-table: For large data volumes, use partitioning and sub-table to disperse data to improve query and maintenance efficiency.

TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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