ShopEase: Optimizing Backend Performance with Redis, Hibernate & MySQL
Overview
As part of my ShopEase e-commerce project, I worked on improving backend performance by optimizing the catalog and order data pipelines. The goal was to reduce read latency, eliminate inefficient database queries, improve connection management, and make data retrieval more efficient.
Key Optimizations
1. Redis Cache-Aside
Implemented a Redis Cache-Aside layer for catalog and order pipelines, along with automated cache invalidation to keep cached data consistent with updates.
Reported result: Read latency reduced from 1.88 seconds to 110 milliseconds — a 94% reduction.
2. Hibernate N+1 Query Optimization
Resolved Hibernate N+1 query bottlenecks using JOIN FETCH, allowing related data to be retrieved more efficiently and reducing unnecessary database round trips.
3. HikariCP Connection Pool Tuning
Optimized HikariCP connection pool settings to improve database connection management and resource utilization.
4. MySQL Composite B-Tree Indexes
Introduced composite B-Tree indexes to improve query execution efficiency and reduce unnecessary row scans.
Reported result: MySQL query row-scans reduced by 95%.
Technology Stack
- Language: Java
- Backend: Spring Boot, Hibernate, Spring Data JPA
- Database: MySQL
- Caching: Redis
- Connection Pooling: HikariCP
- Query Optimization: JOIN FETCH and composite B-Tree indexes
What I Learned
This optimization work reinforced that backend performance depends on multiple layers working together. Caching reduces repeated database reads, efficient ORM queries help avoid unnecessary round trips, connection pooling improves resource management, and indexing makes data retrieval more efficient.
Conclusion
Improving ShopEase was not just about adding features; it was also about making existing functionality faster and more efficient. These optimizations strengthened my practical understanding of Java backend development, database performance tuning, caching strategies, and scalable application design.
Engineered a multi-tier performance optimization for catalog and order pipelines: implemented a Redis Cache-Aside layer cutting read latency by 94% (1.88s → 110ms) with automated cache invalidation, resolved Hibernate N+1 query bottlenecks via JOIN FETCH, tuned HikariCP connection pooling, and introduced composite B-Tree indexes reducing MySQL query row-scans by 95%.
About ShopEase
ShopEase is a full-stack e-commerce application built with Java, Spring Boot, MySQL, and Redis. It focuses on secure shopping, efficient data management, and high-performance backend architecture.
Developed by Shitanshu Jha 💻
Project: ShopEase — Full-Stack E-Commerce Application
GitHub — Backend: https://github.com/Shitanshu686/shopping-app-backend
GitHub — Frontend: https://github.com/Shitanshu686/shopping-app
Top comments (1)
The 110 ms figure becomes much easier to interpret when cold and warm reads are reported separately. I'd add one small regression scenario: clear the relevant cache key, read once, read again, update the item, then read again and assert the new value. That checks both the cache-aside benefit and the invalidation path without hiding the underlying database cost. For the latency comparison, keeping dataset size and concurrency fixed and reporting p95 alongside the average would make the improvement more reproducible.