SQL Query Optimization

Neural Tech

March 27, 2025

SQL

Ever written an SQL query that takes forever to run? Most slow queries can be optimized with just a few smart tweaks. Let’s break it down.

Stop Using (SELECT *), Only Fetch What You Need:
- When you use (SELECT *), you are retrieving every column, even the ones you don’t need. This increases data transfer time and slows things down.
Add Indexes, Make Searching Lightning Fast:
- Indexes act like a table of contents in a book. Instead of scanning every row, the database jumps straight to the relevant data.
- Without Index: Your database does a full table scan, which is slow.
- With Index: It quickly finds rows using a B-tree structure.
Avoid Unnecessary JOINs, They Slow Everything Down:
- JOINs are powerful but can be expensive in terms of performance. If your query is slow, check if you really need all those JOINs.

Instead of multiple JOINs, consider:

Denormalizing data, Store frequently accessed information in one table instead of spreading it across many.
Using Indexed JOINs, Ensure JOIN keys have indexes for faster lookups.

Use EXISTS Instead of IN for Subqueries:
- EXISTS is often faster than IN, especially in large datasets. Why?
- IN retrieves all matching values first, then filters.
- EXISTS stops searching as soon as it finds a match.
Partition Large Tables, Speed Up Query Processing:
- If your table has millions of rows, scanning everything is slow. Partitioning splits data into smaller, manageable chunks, making queries much faster.
Use WHERE Instead of HAVING (When Possible):
- The HAVING clause filters data AFTER aggregation, while WHERE filters before aggregation.
- Reduces the number of rows before grouping, making queries much faster.