Optimizing SQL Performance

Here are the main theoretical strategies to optimize SQL queries for performance:

Understand the Query Execution Plan

Every database (SQL Server, MySQL, PostgreSQL, Oracle) has a query optimizer that decides how to execute your query.
By analyzing the execution plan, you can see if your query is using indexes, table scans, joins, or sorting efficiently.
Performance tuning always starts with understanding how the database is executing the query.

Indexes work like the index of a book → they help the database find rows faster.
But: too many indexes slow down writes (INSERT/UPDATE/DELETE).
Use indexes wisely:
Clustered index (usually on primary key) → defines physical order of data.
Non-clustered indexes → speed up searches on frequently used columns.
Composite indexes → when filtering by multiple columns together.
Missing or wrong indexes are the #1 reason for slow queries.

a query scans the entire table to find results, it’s costly.
Instead, use:
WHERE filters with indexed columns.
LIMIT / TOP when you don’t need all rows.
Avoid using functions on indexed columns in WHERE (like WHERE YEAR(date) = 2024) → this breaks index usage.

Joins are often the heaviest operations in SQL.
Ensure:
Both sides of the join use indexed columns.
Use the right join type (INNER, LEFT, RIGHT, FULL) → don’t fetch more data than needed.
Minimize joining huge intermediate result sets.

Only select the data you actually need:
Avoid SELECT * → fetch only required columns.
Use proper pagination when displaying results in apps (e.g., LIMIT/OFFSET).
The less data moved from DB → App, the faster.
Use Aggregations Smartly
Aggregations (COUNT, SUM, AVG, GROUP BY) can be expensive.
Strategies:
Index columns used in GROUP BY.
Use pre-aggregated summary tables for reporting instead of recalculating huge datasets each time.
Use window functions if supported—they can be more efficient than multiple grouped queries.

Normalization → removes redundancy, good for reducing storage and anomalies.
Denormalization → sometimes improves performance (fewer joins), especially in reporting queries.
Choose based on workload type (transactional system vs analytical system).