对于关系型数据库而言,针对表的检索,一般来说,建立合适的索引就可以达到很好的检索效果。(这里不包含表设计的合理与否) t_girlcreate table rank_status (id integer not null, i_status varchar(3) not null); t_girlcreate table rank_status_extend (
对于关系型数据库而言,针对表的检索,一般来说,建立合适的索引就可以达到很好的检索效果。(这里不包含表设计的合理与否)t_girl>create table rank_status (id integer not null, i_status varchar(3) not null);t_girl>create table rank_status_extend (i_status varchar(3) not null, ids text);
t_girl> insert into rank_status values (222222,'yes'); Time: 4.397 ms t_girl>update rank_status_extend set ids = ids ||','||'222222' where i_status = 'yes'; Time: 43.725 ms
t_girl>delete from rank_status where i_status = 'yes' and id = 1; Time: 47.339 ms t_girl>update rank_status_extend set ids = replace(ids,',1,',',') where i_status = 'yes'; Time: 45.046 ms
t_girl>update rank_status set id = 1000 where i_status = 'yes' and id = 20; Time: 65.834 ms t_girl>update rank_status_extend set ids = replace(ids,',20,',',1000,') where i_status = 'yes'; Time: 85.974 ms
t_girl>select count(*) as total from rank_status where i_status = 'yes'; total ------- 99600 (1 row) Time: 86.563 ms t_girl>select length(ids) - length(replace(ids,',','')) + 1 as total from rank_status_extend where i_status = 'yes'; total ------- 99600 (1 row) Time: 35.762 ms t_girl>select string_agg(id::text,','),i_status from rank_status group by i_status; Time: 113.393 ms t_girl>select ids from rank_status_extend where i_status = 'yes'; Time: 2.447 ms
t_girl>create table rank_status_yes (id int not null); 3552 kB t_girl>create table rank_status_no(id int not null); 3584 kB
t_girl>create materialized view mv_rank_status_yes as select * from rank_status where i_status = 'yes';这种其实和第二种表很类似。只不过不同的是第二种表的维护需要人工来做,而这个视图系统可以维护。

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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