Book a 15 Minute Consultations

If we can help in any way, please don't hesitate to set a time to meet or talk, or leave your details and we'll get back to you.

Looking for a job?

Apply here
Please provide details about your requirements before we schedule a call.
Talk to Support

Migrating from Monolithic Django-React to a Microservices Architecture

To modernize a legacy monolithic Django REST application (hosted on Heroku with a React frontend) by transitioning it to a microservices architecture. This migration was aimed at improving performance, scalability, maintainability, and aligning the product with evolving industry standards.

Problem Statement

Client’s Challenges
  • Delayed Transactions : System latency resulted in slow transaction processing.
  • Customer Churn : Premium users experienced data delays during high-traffic periods.
  • Compliance Risks : Audit logs were processed in hourly batches, reducing real-time traceability.
Why the Previous Architecture Failed
  • Inefficient Polling : HTTP polling increased AWS costs to over $8,000/month and introduced latency.
  • Third-Party Limitations : The reliance on external tools resulted in vendor lock-in and lacked customizable security options.

Solution

Strategic Architectural Shift

Golden Eagle IT Technologies implemented a microservices-based architecture with containerized deployments and GCP integration to address performance, cost, and reliability concerns.

Key Enhancements
  • Microservices Migration : Decoupled services to enable independent scaling and updates.
  • Modern Tooling : Shifted from requirements.txt to .toml for streamlined dependency management.
  • Containerization : Introduced Docker for service portability and faster CI/CD.
  • Cloud-Native Deployment : Used GCP Cloud Run, Cloud Build, and Memorystore for high scalability.
  • Real-Time Monitoring : Integrated Sentry for instant error notifications and improved issue resolution.

Feature List

01
Microservices Architecture

Decoupled and independently deployable services.

02
Optimized Dependency Handling

Improved build time using .toml configurations.

03
Enhanced Query Performance

Query optimization for high-volume, low-latency data handling.

04
Scalable Background Jobs

Dedicated microservices for asynchronous operations.

05
Real-Time Monitoring

Sentry-based alerts for faster issue resolution.

06
Containerized Deployment

Docker-based services deployed via Kubernetes.

Tech and Solution Stack

Backend

Django REST Framework, Python 3.10

Frontend

React (connected via REST APIs)

Containerization

Docker

Configuration Management

.toml file for Python dependency management

Database & Caching

PostgreSQL, GCP Memorystore

CI/CD

Google Cloud Build, Cloud Run, Kubernetes

Logging & Monitoring

Custom loggers, Sentry

Payment & E-Commerce

Shopify, Stripe, and other third-party gateways

Hosting

  • Cloud Provider : Google Cloud Platform (GCP)
  • Deployment Model : Cloud Run for microservices; Kubernetes for orchestration
  • CI/CD Automation : Implemented using GCP Cloud Build
  • Cost Optimization : Efficient container usage led to a reduction in operational costs

Team & Support

Project Roles & Responsibilities

  • Backend Lead : Directed microservices design and Django API restructuring.
  • DevOps Engineer : Managed Docker configurations, CI/CD pipeline, and GCP deployments.
  • Frontend Developers : Refactored frontend code to integrate with new RESTful microservices.
  • QA Engineers : Conducted performance testing and ensured the system met SLAs.

Client Collaboration & Support Process

Post-implementation, we continuously monitored performance and addressed new challenges :

  • Agile Sprints : Bi-weekly sprints for requirement gathering and validation.
  • Post-deployment support : 24/7 monitoring with automated alerts.
  • Continuous Feedback Loop : Regular check-ins to incorporate business and user feedback.

Maintenance & Evolution

Ongoing Optimization

  • Performance Tuning : Continuous query optimization and database indexing.
  • Infrastructure Scaling : Adjusting container resources based on real-time load.
  • Security Audits: Periodic updates to ensure compliance and data protection.
  • Monitoring Enhancements : Real-time logging improvements for proactive issue detection.

Future Roadmap

  • CI/CD Automation Expansion : Enhanced test coverage and deployment efficiency.
  • Feature Rollouts : Adding user-driven enhancements and integrations.
  • Global Scaling : Multi-region deployment to reduce latency for international users.

Conclusion

Golden Eagle IT Technologies successfully transformed a legacy Django-React monolithic application into a modern, scalable, and cost-effective microservices system. Leveraging Docker, GCP services, and Sentry-based monitoring, the team delivered a high-performance platform with real-time responsiveness and reduced infrastructure costs. The shift not only improved customer satisfaction but also positioned the client for sustainable growth and global expansion.