Therefore, a delay occurs until the data is provided within the system tables. Profiling data is only kept for 24 hours, after that, old query profiles are deleted from the system tables. This profiling data is part of the statistical system tables which are committed Queries and is collected in the system tables EXA_USER_PROFILE_LAST_DAY and EXA_DBA_PROFILE_LAST_DAY. After that, the corresponding profiling data is gathered during the execution of You can enable the general profiling feature through the statements ALTER SESSION or ALTER SYSTEMīy setting the option profile='on'. System tables EXA_DBA_PROFILE_RUNNING and EXA_USER_PROFILE_RUNNING. Enable Profilingīy default, the profiling is only activated for the running queries and provides the current status through the Further details are described in the following sections. Afterward, the corresponding information is gatheredĪnd provided to the customer through system tables. In Exasol, you can switch on the profiling feature on demand. Further, this kind of information canīe provided to Exasol to improve the query optimizer continuously. Long running queries can then be analyzed and maybe re-written. However, there are situations where you want to know how much time the certain execution parts of a query Tuning actions are executed fully automatically. Queries in Exasol are analyzed by the query optimizer and corresponding Index types, calculation of table statistics and so on. It's an ongoing process, and you should continuously monitor and fine-tune your queries as your application and data grow and evolve.Exasol deliberately omits complex tuning mechanisms for customers, for example, execution hints, creation of different Remember that query optimization is not a one-time task. Hardware and Server Configuration: Consider optimizing your server's hardware and MySQL configuration parameters, such as buffer sizes, thread settings, and query cache size, based on your specific workload. Upgrade MySQL: Ensure you are using the latest version of MySQL, as newer versions often come with performance improvements and bug fixes. Use Connection Pooling: Utilize connection pooling to reduce the overhead of opening and closing database connections for each query. Profiling: Use MySQL's built-in profiling tools like SHOW PROFILES and SHOW PROFILE to analyze query performance and identify bottlenecks.Ĭaching: Implement application-level caching to reduce the load on the database server for frequently accessed data. Regularly Analyze and Optimize Tables: Periodically run the ANALYZE TABLE and OPTIMIZE TABLE commands to update table statistics and defragment tables, respectively. Use the InnoDB Storage Engine: InnoDB is the default storage engine in MySQL and is well-suited for most applications due to its support for transactions, foreign keys, and row-level locking. In many cases, using proper indexing and optimizing queries is more effective than relying on the query cache. Query Cache: MySQL has a query cache feature, but it may not always be beneficial. This can speed up queries by allowing MySQL to scan only relevant partitions. Partitioning: For large tables, consider using table partitioning to split data into smaller, more manageable pieces. ![]() Normalization: Design your database schema following normalization principles to reduce redundancy and improve query performance. Try to avoid functions like CONVERT, DATE, and YEAR on indexed columns. Use INNER JOIN when possible, as it usually performs better than LEFT JOIN or RIGHT JOIN.Īvoid Functions in WHERE Clauses: Applying functions to columns in the WHERE clause can prevent MySQL from using indexes. Optimize JOINs: Ensure that JOIN conditions are properly indexed. ![]() Try to rewrite subqueries as JOINs where possible. Avoid Subqueries: Subqueries can be less efficient than JOINs in some cases.
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