What Surprised Us When We First Looked at the Code
When the team at Sajag Sports brought us in to audit their digital platform, they were experiencing a frustrating problem: during high-demand promotional sales, customers were completing purchases for items that were already out of stock in physical warehouses.
On paper, their system looked fine. But when we dug into the telemetry server logs, we discovered something shocking.
A single product page render was firing over 140 individual database queries. Every variant dropdown, related product widget, review badge, and inventory check was running its own separate SQL lookup without proper caching or indexing. Under heavy traffic, database connections quickly ran out, request queues backed up, and race conditions occurred during checkout.
[Legacy Bottleneck]
1 Page Request ──> 140 Un-indexed Queries ──> Connection Pool Starvation ──> Stock Desync
[Our Streamlined Approach]
1 Page Request ──> 2 Optimized Cached Queries ──> Atomic Stock Lock ──> Instant Order
The Biggest Lesson: Fixing Inventory Locks
Our primary goal was making sure two customers clicking "Buy Now" at the exact same millisecond couldn't both purchase the last remaining tennis racket in stock.
We originally considered integrating a third-party inventory management SaaS tool to handle this. But after evaluating the added network latency and monthly subscription costs, we realized PostgreSQL already had everything we needed built-in: Row-Level Locking (FOR UPDATE).
When a customer initiates a checkout, our transaction locks that specific product's inventory row for a few milliseconds while verifying stock and processing the reservation.
THE RESULT
Implementing atomic database locking completely eliminated inventory overselling across thousands of orders, giving the client total confidence during high-volume sales events.
Redesigning Search for Instant Results
Another challenge was product search. Users searching for specialized gear like "leather cricket gloves" were waiting nearly two seconds for results because the legacy search did plain string pattern matching across entire text columns.
Instead of introducing heavy external search cluster infrastructure that would add monthly maintenance overhead, we utilized PostgreSQL's native tsvector full-text search with GIN indexes.
Search queries dropped from nearly two seconds to practically instantaneous responses, right inside their existing database.
Summary: If We Built This Again
Looking back at the Sajag Sports project, our biggest takeaway was that you don't always need a complex web of microservices and third-party SaaS tools to solve performance issues. Often, taking the time to write clean, targeted database queries and using the full power of tools like PostgreSQL and Next.js is all it takes to build a world-class platform.
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Frequently Asked Questions
How did you prevent inventory overselling during peak sales traffic?
We used PostgreSQL atomic row-level locking during checkout transactions, ensuring two buyers couldn't claim the same final stock item at the exact same millisecond.
Why replace third-party e-commerce plugins?
Off-the-shelf plugins often run dozens of un-indexed database joins for simple product views. Building a custom modular queries layer gave us full control over query speed.