Case Study
Data-Heavy Laravel Applications
Making Laravel apps faster when the data gets big: queries, indexes, Eloquent, and aggregation.
- MySQL
- PostgreSQL
- Eloquent
- Performance
Problem
Apps got slower as datasets grew and queries got more complex.
Context
Laravel apps on MySQL and PostgreSQL with Eloquent, Query Builder, aggregations, and reporting-style workloads.
Challenge
The slowdown usually isn't one bug. It's the mix of app logic, schema, queries, indexes, and volume.
Approach
I profiled the slow paths, looked at query plans and indexes, cut unnecessary data loading, and reworked queries for large collections and aggregates.
Result
More practical performance on data-heavy Laravel workloads by treating the database as part of the architecture.
Lessons
A lot of 'Laravel is slow' tickets are really database tickets wearing application clothes.
Architecture
- MySQL / PostgreSQL
- Eloquent and Query Builder
- Indexing strategies
- Aggregation and reporting queries
Technical details
- - Query optimization and indexing
- - Complex SQL and aggregation patterns such as GROUP_CONCAT where they fit
- - Keeping heavy work in the database instead of in PHP collections
- - Troubleshooting slow queries under real data volume