key techniques is using proper indexing. For example, if a table has millions of rows and we frequently filter data based on CustomerID, adding an index on that column can speed up lookups drastically. But I’m also careful not to over-index because too many indexes can slow down write operations.
I also make sure to **avoid SELECT *** and instead specify only the required columns. This reduces the data volume being fetched. Similarly, I replace subqueries with JOINs or CTEs when possible, since correlated subqueries can execute multiple times and impact performance.
Another important area is filtering early — using WHERE conditions and JOIN conditions correctly to minimize the dataset being processed. For example, in a reporting query, applying filters before aggregation rather than after can make a big difference.
In one of my previous projects, I was working on a sales reporting system where a query that joined 5 large tables was taking more than 40 seconds. After analyzing the execution plan, I noticed it was doing multiple full table scans. I added indexes on SalesDate and RegionID, replaced a correlated subquery with a CTE, and reduced unnecessary columns. The query execution time dropped to under 5 seconds.
The main challenge I often face is balancing readability and performance — sometimes heavily optimized queries become difficult to maintain. So, I usually document complex logic or break it into smaller, manageable CTEs.
A limitation of query optimization is that it depends on database statistics and data distribution — what works well today might degrade over time as data grows. As an alternative, I sometimes use materialized views or indexed views for very large datasets to pre-aggregate data and reduce runtime load.