Case Study: Stabilizing a Mobile App Through Pragmatic GraphQL Debugging

TL;DR

This project turned my first encounter with GraphQL into a lesson in pragmatism, using the technology to serve the product rather than letting the stack dictate its pace. By focusing on stability and clarity, the app transitioned from fragile to dependable.

The Situation: Fixing a Mid-Development Bottleneck

I joined a small team mid‑development, on a gamified wellness app designed to reward users for reducing their screen time.
The app was built on React Native (Expo) with a GraphQL API (Node/Express + Apollo) backend. It aimed high technically, but the architecture was over‑engineered for an MVP: a highly structured GraphQL schema introduced friction and rigidity in a codebase that still needed fast experimentation.

My role was to stabilize the app, learn GraphQL quickly, and make the user experience reliable enough to ship.

The Diagnosis: Stale Data and Friction Everywhere

Within days I pinpointed the core failure: the data layer couldn’t stay in sync.

The UI displayed stale data (like outdated reward balances) until a user restarted the app.
Apollo’s default cache-first policy caused outdated information; switching to network-only fixed the issue but made every screen reload painfully slow (up to 5 seconds).
Different components used different query patterns, compounding inconsistent states.

From an architectural standpoint, the core issue was a mismatch between the stack and the product stage. GraphQL’s rigid schemas and deep typing help once a product stabilizes, but for an evolving pre-launch app, it slowed iteration to a crawl.

The Solution: Balancing Performance and Consistency

I focused on two goals: fix immediate user-facing bugs and make the developer workflow smoother.

Part 1: Smarter Caching

The stale data problem was a caching issue, not a GraphQL one, so I rewrote the caching policies to treat data differently based on context:

Critical user data (points, balance): Switched to cache-and-network so cached data rendered instantly while background refresh guaranteed accuracy.
Non-critical data (partner offers, stats): Fetched on demand, reducing unnecessary network calls.
Startup hydration: Pre-fetched essential data on app load to make interactions feel instant.

These changes turned the UI from “buggy and laggy” to reliable and fast.

Part 2: Structuring for Maintainability

While debugging, I also improved code structure:

Adopted simple organizational rules inspired by the Tao of React: separation of shared vs. page-specific components.
Documented the new data flow and caching policies for the team.
Simplified overly general abstractions to make future changes less fragile.

These steps lowered confusion and made onboarding new developers significantly more straightforward.

The Impact: Reliability Restored

Although still in beta during my tenure, the project shifted dramatically:

Stable Data Layer: Eliminated the main cause of user visible bugs (incorrect balances, broken updates).
Better UX: Caching strategy yielded a smooth, responsive feel while keeping information current.
Developer Confidence: The project went from brittle to consistent. Debugging became easier, and new features could be built without fear of hidden state issues.

Reflections & Key Strengths

This project reinforced another of my key engineering beliefs: reliability and user clarity matter more than chasing the most powerful or popular tool.

Here’s some of the key strengths I demonstrated:

Rapid Learning: Learned GraphQL under pressure to debug and refactor key queries.
Pragmatic Problem-Solving: Used the tool realistically — balancing schema power with performance.
User-First Thinking: Prioritized fast, accurate data over architectural perfection.
Architectural Discipline: Improved file structure and internal documentation for ongoing stability.