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5. Query Optimization

I focus on optimizing backend performance by reducing unnecessary work in queries, data loading, and processing.

🧠 Backend Performance Optimization

I focus on optimizing backend performance by reducing unnecessary work in queries, data loading, and processing.


🎯 How I Approach Optimization

Most performance issues I’ve seen come from:

repeated or duplicated queries
inefficient data fetching
unnecessary model loading

These issues don’t always show early, but they impact:

response time
database load
system scalability

🧩 Key Areas I Optimize

Query Indexing

I add indexes based on actual usage patterns:

WHERE conditions
join columns
sorting (ORDER BY)

At the same time, I avoid:

over-indexing
unused or duplicate indexes

👉 I treat indexing as a tradeoff, not a default


Reducing Model Usage

I avoid loading full models when they are not needed.

Instead, I:

select only required columns
use lightweight queries
fetch IDs or minimal data when possible

👉 Less model hydration improves performance and memory usage


Handling N+1 Queries

I watch for N+1 patterns, where:

one query loads a list
additional queries run per item

To fix this, I use:

eager loading
batching related queries

👉 This reduces multiple database round trips


Avoiding Duplicate Queries

In some flows, the same data gets queried multiple times across methods.

To handle this, I:

reuse already fetched data
pass data through the flow
use request-level caching when needed

👉 Reduces unnecessary database load and keeps data consistent


⚖️ Tradeoff Awareness

Optimization can introduce:

added complexity
tighter coupling

So I focus only on:

hot paths
high-impact queries

📌 Practical Approach

I don’t optimize blindly.

I first identify:

repeated queries
bottlenecks
unnecessary data loading

Then apply targeted improvements.


💬 Summary

My optimization approach is:

minimize unnecessary database work
reduce data loading overhead
fix inefficient query patterns

This helps maintain systems that are both fast and scalable without over-engineering.