Get how many rows there are in the table
Tips:
select count(*) from table_name;
Get the columns with cat_id=4 and cat_id=11
Use or select * from goods where cat_id=4 or cat_id=11 ;
Do not use or select * from goods where cat_id in(4,11);
Get the price>=100 and
select * from goods where shop_price >= 100 and shop_price <= 500; select * from goods where shop_price between 100 and 500;
Get the price< ;=100 and>=500
select * from goods where shop_price <=100 and shop_price >= 500; select * from goods where shop_price not between 100 and 500;
in is the set of scatter points, between and is the interval
cat_id is not a column of 3 or 11
select * from goods where cat_id!=3 and cat_id!=11; select * from goods where cat_id not in(3,11);
Calculate the discount value than the market price
select goods_id,(market_price-shop_price) as chajia ,goods_name from goods ;
Find the local price that is more than 200 cheaper than the market price
select goods_id,(market_price-shop_price) as chajia ,goods_name from goods where (market_price - shop_price) > 200;
(The chajia column is generated after where has been used)
Doubtful points note: where works on the data in the real table, and having can filter the where results
select goods_id,(market_price-shop_price) as chajia ,goods_name from goods where chajia > 200;(错误的)
The same effect
select goods_id,(market_price-shop_price) as chajia ,goods_name from goods having chajia>200;
Change [20,29] in the num column in the main table to 20 [30,39] to 30
update mian set num = floor(num/10)*10 where num between 20 and 39;
likeFuzzy query
Intercept the content after Nokia
select goods_id ,goods_name,substring(goods_name,4) from goods where goods_name like '诺基亚%';
Find the content starting with Nokia and replace it with htc (no change to the real table content)
select goods_id ,goods_name,concat('htc',substring(goods_name,4)) from goods where goods_name like '诺基亚%';
Replace Nokia with htc (change the real table content)
update goods set goods_name = concat('htc',substring(goods_name,4)) where goods_name like '诺基亚%' and cat_id=4;
The above is the content of mysql query. For more related content, please pay attention to the PHP Chinese website (www.php.cn)!

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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