From Ryam: p26 倒数第二行: 原文:“逻辑层与存储引擎实现层的过度解偶” - :“逻辑层与存储引擎实现层的过度解耦” p82 第9行: 原文:“这样就省略了分页程序在分以前实时计算” - :“这样就省略了分页程序在分页前实时计算” p118 第5行: 原文:“
From Ryam:
p26
倒数第二行:
原文:“逻辑层与存储引擎实现层的过度解偶”
-> :“逻辑层与存储引擎实现层的过度解耦”
p82
第9行:
原文:“这样就省略了分页程序在分以前实时计算”
-> :“这样就省略了分页程序在分页前实时计算”
p118
第5行:
原文:“如果系统须要有限保证”
-> :“如果系统须要优先保证”
p123
第1行:
原文:“并不一定完全按照系数据库的元信息”
-> :“并不一定完全按照数据库的元信息”
p85
倒数第2行:
原文:“然后再瓶装展现对象”
-> :“然后再拼装展现对象”
p139
第8行:
原文:“那么将会存在大量记录指针信息存于同一Hash值相关联”
-> :“那么将会存在大量记录的指针信息与同一Hash值相关联”
p142
倒数第6行:
原文:“当然,并不是存在更新的字段就适合创建索引”
-> :“当然,并不是存在更新的字段就不适合创建索引”
p171
第3行:
原文:“但是当遇到一些自查询或较为复杂的join时”
-> :“但是当遇到一些子查询或较为复杂的join时”
第11行:
原文:“group_message_bad是优化前的表,优化后为group_message表),如示例代码9-1所示:…”
-> :这里的示例代码中group_message更换成group_message_bad
p194
第3行
原文:“这样不仅可以让变化频繁的Table的Query浪费Query Cache的内存”
-> :“这样不仅可以避免变化频繁的Table的Query浪费Query Cache的内存”
p196
倒数第1行:
原文:“先看一下与网络连接的性能配置项及对性能的影响”
-> :“先看一下与网络连接相关的性能配置项及其对性能的影响”
p202
倒数第2行:
原文:“在数据库上进行线后的参数调整”
-> :“在数据库上线后进行的参数调整”
p208
倒数第6行:
原文:“还是将页节点也加载进来”
-> :“还是将叶节点也加载进来”
p237
第2行:
原文:“还须要提一下系统高可用及数据安这两方面”
-> :“还须要提一下系统高可用及数据安全这两方面”
p246
第13行:
原文:“只有Master和Slave的server-id参数值比不一致时”
-> :“只有Master和Slave的server-id参数值不一致时”
p249
第2行:
原文:“最大的好处就是既可以避免主Master的写操作不会受到Slave集群的复制所带来的影响”
-> :“最大的好处就是既可以避免主Master的写操作受到Slave集群的复制所带来的影响”
p261
倒数第9行:
原文:“主要是将某个访问极其平凡的表”
-> :“主要是将某个访问极其频繁的表”
p264
图14-3
原文:“hprizontal sharding”
-> :“horizontal sharding”
问题:photo albums数据库的水平切分示例中,每个切分库的切分条件都是album_id % n = 0,应该依次为album_id % n = 0, album_id % n = 1 …
p279
倒数第15行:
原文:“当Innodb在本地的Buffer Pool(…)的时候”
-> :“当Innodb在本地的Buffer Pool(…)找不到所需数据的时候”
From 云水馋心:
P153
原文:“c_rec.group_msg_id = c_rec.id{”
-> :“c_rec.group_msg_id=m_rec.id”
From mysqlkumao
P201
原文:“10.4.3 Sort Buffer、Join Buffer 和 Read Buffer”
-> : “10.4.3 Sort Buffer 和 Join Buffer”
From ochef
推荐序二
原文:“Blog:http://www.orawh.com/”
-> : “Blog:http://www.ixdba.com/”
P18
原文:“ 如:update、delte、insert、create”
-> : “ 如:update、delete、insert、create”
Latest Update:2009.10.19
From:Jasper Che
P108
原文:“TL_WRITE_ALLOW_WRITE”
->:“WRITE_ALLOW_WRITE”
原文:“P108 L7 同时Current read lock是Read_NO_INSERT的锁定类型”
->:“同时Current read lock不是Read_NO_INSERT的锁定类型”
P109
原文: “而其他几种支持事务的存储存储引擎”
->:“而其他几种支持事务的存储引擎”
P127
原文:“改行算法只须要访问一次数据 ”
->:“改进算法只须要访问一次数据”
P131
原文:“主要包含如下集中类型”
->:“主要包含如下几种类型”
P148
原文:“subject LIKE ‘weiurazs%’”
->:“AND subject LIKE ‘weiurazs%’”
Latest Update:2009.12.14
原文地址:读者朋友整理的《MySQL性能调优与架构设计》勘误信息, 感谢原作者分享。

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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