Here's how you can enhance database query optimization with creativity.

When creating indexes, ensure they are frequently used in the WHERE clause. You can create a functional index like TO_CHAR(NOW()::DATE, 'Mon dd, yyyy'), which will be useful when searching using a format like "Sep 27, 2021". The indexes may vary depending on your system design and UI/UX requirements.

Effective indexing boosts query performance but requires balance. Avoid over-indexing to prevent slow writes and under-indexing for slow reads. Use composite indexes with the most selective column first for optimal efficiency.

In my experience, instead of using SELECT *, you should only select the columns you need. When a table has thousands of columns, using SELECT * will retrieve data for every single column, which can be extremely inefficient and resource-intensive. Additionally, when selecting rows, remember to implement paging.

In the case of joins, the position of the joining matters because the query runs starting from the table, then the WHERE clause, and finally the SELECT clause. Placing the join after the WHERE clause could limit the rows you need to join, whereas placing it before might result in joining the entire orders and users tables and then filtering out orders_qty > 5. By filtering the orders first and then joining the users, you can optimize the query performance.

One time at work, I learned about caching SQL servers. I read an article on wecommit about writing and reusing caches of previously written SQL statements. Since then, I have optimized many commands based on the cached article on wecommit.

I think that catching partial results through caching is too awesome and game changing because it helps systems handle higher loads by decreasing the need to recompute frequently requested data.

Understanding query execution plans is crucial for optimizing database performance. By analyzing these plans, you gain insight into how the database engine processes your queries, revealing inefficiencies like full table scans or poorly designed joins. This knowledge allows you to make informed adjustments to your queries or schema, improving speed and efficiency. In essence, mastering execution plans helps you ensure that your database operates at its best.

Joins are key in relational databases but can be resource-intensive. To optimize them, choose the right join type (INNER, LEFT, RIGHT), index foreign keys, and consider table sizes and join order. Also, avoid joins on columns with different data types to prevent slow implicit type conversions.

In my experience, parallelizing queries can significantly speed up operations such as rebuilding indexes on large tables or inserting data from other large tables. However, it is crucial to reset the parallel processing settings to their default values once these tasks are completed. Overusing parallel processing can lead to resource contention and negatively impact overall system performance.