12. Database Indexing and Query Design | Engineering Notes

🧠 Database Indexing & Query Design

I focus on using indexing and query design to improve performance and scalability, especially in read-heavy systems.

Indexes help the database avoid full table scans by creating a fast lookup structure.

I typically use indexes for:

frequently filtered columns (WHERE)

join keys

sorting (ORDER BY)

enforcing uniqueness

For more complex queries, I use composite indexes, where:

column order is important

the database can only efficiently use the leftmost part

I treat indexes as a performance tradeoff, not a free optimization.

Adding too many indexes can:

slow down inserts and updates

increase storage usage

add maintenance overhead

So for write-heavy systems, I’m more selective with indexing.

I don’t rely on indexes alone — I also optimize query structure.

Key practices I follow:

select only required columns (avoid SELECT *)

paginate large datasets

filter early to reduce scanned data

avoid N+1 query patterns

When a query is slow, I:

check the execution plan first

identify bottlenecks before making changes

Common issues I look for:

missing or unused indexes

low selectivity (poor cardinality)

inefficient joins

I design indexes based on real access patterns, not just schema structure.

> I optimize where the system is actually used, not where it “might” be used.

My approach to database performance is:

combine proper indexing with efficient query design

understand tradeoffs between read and write performance

rely on execution plans instead of assumptions

This helps keep systems both fast and scalable as data grows.