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IFNULL(expr1,expr2)
如果expr1不是NULL,IFNULL()返回expr1,否则它返回expr2。IFNULL()返回一个数字或字符串值,取决于它被使用的上下文环境。
SELECT IFNULL(1,0); // 1 SELECT IFNULL(0,10); // 0 SELECT IFNULL(1/0,10); // 10 SELECT IFNULL(1/0,yes); // yesIF(expr1,expr2,expr3)
如果expr1是TRUE(expr10且expr1NULL),那么IF()返回expr2,否则它返回expr3。IF()返回一个数字或字符串值,取决于它被使用的上下文。
SELECT IF(1>2,2,3); // 3 SELECT IF(1<2,yes,no); // yes SELECT IF(strcmp(test,test1),yes,no); // no
expr1作为整数值被计算,它意味着如果你正在测试浮点或字符串值,你应该使用一个比较操作来做。
SELECT IF(0.1,1,0); // 0 SELECT IF(0.1<>0,1,0); // 1在上面的第一种情况中,IF(0.1)返回0,因为0.1被变换到整数值, 导致测试IF(0)。这可能不是你期望的。在第二种情况中,比较测试原来的浮点值看它是否是非零,比较的结果被用作一个整数。
CASE value WHEN [compare-value] THEN result [WHEN [compare-value] THEN result ...] [ELSE result] END
CASE WHEN [condition] THEN result [WHEN [condition] THEN result ...] [ELSE result] END
第一个版本返回result,其中value=compare-value。第二个版本中如果第一个条件为真,返回result。如果没有匹配的result值,那么结果在ELSE后的result被返回。如果没有ELSE部分,那么NULL被返回。
SELECT CASE 1 WHEN 1 THEN "one" WHEN 2 THEN "two" ELSE "more" END; // "one" SELECT CASE WHEN 1>0 THEN "true" ELSE "false" END; // "true" SELECT CASE BINARY "B" WHEN "a" THEN 1 WHEN "b" THEN 2 END; // NULLbitsCN.com

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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