Here's how you can harness creativity to optimize database performance.

Indexing is a commonly applied technique in my optimization projects. However, here are some of my insights: 1. Indexing does not always slow down DML statements. Indexes can significantly speed up operations like Update and Delete when there is a Where clause. 2. Consider using composite indexes on multiple columns instead of single-column indexes. 3. Develop a strategy to periodically evaluate and remove ineffective indexes.

- Indexing the result of a function applied to one or more columns. This is useful for queries that involve computed values. - Create indexes on expressions or combinations of columns that frequently appear in queries. - Index multiple columns that are frequently used together in queries. - Use bitmap indexes for columns with a limited number of distinct values. - Index text columns to support full-text search capabilities. - Use Bloom filters to quickly test whether a value might be in a set. - Use query profiling tools to identify which queries could benefit most from creative indexing.

- Remove unnecessary joins and ensure that each join is necessary for the query result. - Create indexes that match the common query patterns, including composite indexes if multiple columns are frequently used together. - CTEs can make complex queries more readable and sometimes more efficient. - Use application-level caching for data that is read often but updated infrequently. - Periodically rebuild or reorganize fragmented indexes to maintain performance.

- Minimizes duplicate data, saving storage space and reducing the risk of inconsistencies. - Simplifies and speeds up queries by organizing data into logical structures. - Easier to update, insert, and delete data without affecting other parts of the database. - Table must be in 4NF, and every join dependency must be a consequence of the candidate keys.

Data normalization is like decluttering your home, organizing data into streamlined tables to reduce redundancy. However, excessive normalization can lead to slow queries due to too many JOIN operations. Balance this by denormalizing where performance is critical, combining tables or introducing controlled redundancy to enhance read operations. Normalization should be a tool for efficiency, not a goal in itself.

- Stores data in the system’s RAM for quick access. This is the fastest caching method but is limited by the available memory. - Caches the results of database queries to avoid repeated execution of the same query. - Stores the HTML output of entire pages or views. This reduces server load by serving cached pages directly. - Caches objects or data structures, such as query results or complex calculations, to avoid recomputation. - A caching layer placed between the client and the server, often used to cache responses from web servers.

Caching improves database performance like quick memory recall. Use in-memory caches for expensive computations, and manage cache invalidation to prevent stale data. Combining different cache levels can enhance speed.

Asynchronous processing improves database responsiveness by deferring non-critical tasks like logging. Use message queues or background jobs to handle these tasks, allowing your database to focus on essential operations and enhancing overall performance.

- Perform input/output operations in a non-blocking manner, allowing other tasks to run while waiting for I/O operations to complete. - Offload long-running tasks to background workers or processes, freeing up the main application to handle other requests. - Use events and callbacks to handle asynchronous tasks based on specific triggers or conditions. - Use queues to manage and distribute tasks to workers, allowing for decoupled and scalable processing.