Architecture Overview
🔗 Backend for Frontend (BFF) Core At the heart of the solution was a NestJS GraphQL BFF, acting as the single gateway for the mobile app. It aggregated data from multiple backend services, applied business logic, and returned only the required information --- reducing payload size and optimizing performance for mobile users.
⚡ Speed Through Caching A Redis caching layer accelerated frequent queries, cutting response times and reducing load on backend services.
🔍 Smart Search Experience Algolia powered instant, relevant search results, ensuring a smooth and responsive product discovery process.
📊 Proactive Monitoring We integrated New Relic and Amplitude for deep performance and usage monitoring, enabling quick detection and resolution of potential issues before they impacted end users.
🤝 Close Collaboration with Frontend Teams While the mobile frontend was developed separately inFlutter, we partnered closely with the client's developers to resolve integration challenges, fine-tune API responses, and ensure backend compatibility with evolving frontend needs.
Technology Stack
Backend
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NestJS (TypeScript) -- GraphQL API (BFF layer)
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Redis -- High-speed caching layer
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Kubernetes -- Container orchestration for scalable deployments
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AWS -- Hosting, storage, and infrastructure services
Integrations
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Algolia -- Instant, relevant search functionality
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Amplitude & New Relic -- Real-time monitoring and performance tracking
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Firebase -- Notifications and real-time updates
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E-Commerce Services -- Product, order, and user management built on the existing web application backend, enhanced through the BFF to optimize performance and tailor responses for the mobile experience.
Why This Stack and Architecture
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NestJS GraphQL API → Tailored, mobile-optimized data delivery with flexible query capabilities. This flexibility allows the mobile client to fetch exactly the fields it needs for each view, avoiding overfetching and improving performance across different parts of the application.
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BFF Pattern → Clear separation between mobile app needs and backend complexity, making future updates easier.
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Redis Caching → Faster API responses and reduced backend load by storing frequently accessed products, as well as relatively static data such as translations and general-purpose store information that doesn't change often.
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Algolia Search → A hosted search-as-a-service platform that delivers sub-second, highly relevant search results through advanced indexing and ranking algorithms. In our store's use case, Algolia powers instant product discovery with typo tolerance, faceted filtering, and personalized ranking, enabling customers to quickly find items even with incomplete or imprecise queries. This significantly improves the shopping experience and reduces friction during product exploration.
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New Relic + Amplitude → Together, these tools provide complete visibility into performance, usage patterns, and system health. New Relic enables real-time monitoring of backend performance, error tracking, and infrastructure health, while Amplitude offers deep insights into user behavior, feature adoption, and engagement flows. This combination allows us to quickly detect and resolve issues, optimize system performance, and refine the user experience based on clear behavioral patterns and the measured success of new features.
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AWS → AWS provides a highly available, globally distributed infrastructure with a robust suite of cloud services, security features, and monitoring tools, ensuring that our backend can scale reliably to meet demand.
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Kubernetes → adds a secure orchestration layer that automates deployment, scaling, and management of containerized services. Its built-in health checks, self-healing capabilities, and diagnostics tools enable us to maintain system stability, quickly resolve issues, and adapt resources dynamically for peak performance.
Monitoring & Stability- How we keep the BFF Rock-Solid
Real-Time Performance Tracking with New Relic We integrated New Relic to continuously monitor API performance, response times, and error rates. This visibility allowed us to detect issues before they impacted end-users and quickly isolate bottlenecks.
Data Protection at Every Layer All monitoring and logging pipelines were designed with data obfuscation to ensure no sensitive customer information or personally identifiable data was ever exposed.
Usage & Traffic Insights We tracked throughput, active user counts, and request volumes segmented by time zone, device type (iOS/Android), and region. These metrics provided critical insights into user behavior and helped optimize scaling strategies.
Incident Response & Notifications Custom alerts and Slack channel integrations ensured the right people were notified immediately during incidents, reducing response times.
Incident Playbooks & Post-Mortems A detailed incident playbook guided the team on what actions to take for each type of issue. After every major incident, we conducted post-mortems to implement improvements and strengthen the system.
Automated End-to-End Testing We ran hourly E2E tests via cron jobs to verify that both the BFF and all connected external services were functioning correctly.
Quality Gates in the Pipeline CI/CD pipelines included unit tests, integration tests, E2E tests, and SonarCloud analysis to ensure code quality and prevent regressions.
Preventing Breaking Changes in GraphQL We implemented a GraphQL schema checker in the pipeline to detect breaking changes before deployment, ensuring backward compatibility for all app versions in use.
Why the BFF Approach Was Critical
🛡️ Safeguarding Against Frontend Risks
One of the biggest operational challenges in mobile app development is the long approval process for publishing updates on the iOS App Store and Google Play Store. In this project, a key reason for adopting a Backend for Frontend (BFF) layer --- rather than exposing the same backend used by the retail website --- was to prevent mobile-specific dependencies on unstable or experimental frontend implementations from the website. Any frontend bug that slipped into the app could have caused serious downtime or user frustration due to the delay in pushing fixes through the app store approval process. The BFF acted as a safety buffer, ensuring only stable, mobile-optimized data was delivered to the app.
🔄 Backward Compatibility Without Friction
Another major advantage was version compatibility. Unlike web applications, where updates are instantly available, mobile users may continue running older versions of the app for weeks or months. Forcing them to update every sprint would have created unnecessary friction. The BFF allowed us to maintain backward compatibility with older app versions by adapting services on the fly, ensuring a smooth user experience without forcing frequent, disruptive updates.
Feature Highlights
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Optimized Mobile Data Delivery -- Backend tailored for mobile devices to minimize payloads and boost speed.
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High-Speed Search -- Algolia integration delivering instant product discovery.
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Performance-Driven Architecture -- Redis caching and Kubernetes orchestration for stability and scalability.
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Seamless E-Commerce Integration -- Connection to existing product, inventory, and order systems.
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Backend-Frontend Collaboration -- Ongoing support and bug resolution to ensure smooth integration with the Flutter app.
Challenges & Lessons Learned
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Integration Complexity -- Coordinating between backend and an external frontend team required constant communication and rapid iteration.
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Tight Deadlines -- Designing, implementing, and optimizing the BFF within a short delivery window demanded strict prioritization.
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Scalable Foundation -- Building the backend to handle global traffic while staying flexible for future features.
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Performance Monitoring Wins -- Early adoption of New Relic enabled us to catch and fix bottlenecks before they impacted users.
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