Real-time Game Environment (Full-stack)

Introduction

Game development and web development are often framed as unrelated skill sets. After building and deploying a multiplayer game into production, I discovered that many of the same architectural and operational concerns apply.

This project was deployed into production and operated at real user scale. The game accumulated over 100,000 visits, maintained a 92% approval rating, and reached a peak concurrency of ~400 simultaneous players. These conditions exposed sustained load, real economic incentives, and long-lived state.

This post explores the overlap between web systems and multiplayer game environments, the challenges I encountered, and the lessons that reshaped how I think about building and deploying web applications in production.

Modeling the Core Loop as an API

In a multiplayer game, the core loop directly dictates how your backend API is structured. Even if the API does not follow a traditional REST model—or is intentionally obfuscated—the same underlying principles still apply.

When designing the backend, I found it effective to break the core loop down into verbs: the actions a player can perform and the state transitions those actions produce. These verbs naturally map to endpoints or internal service boundaries. Modeling them early—using simple diagrams or class-level abstractions—helped surface edge cases and invalid state transitions before any code was written.

This approach mirrors good web architecture. Reducing complex systems into explicit actions and responsibilities early prevents structural mistakes that become expensive to fix later.

Never Trust the Client

“Never trust the client” is a principle commonly associated with web development, and it applies even more strictly in concurrent multiplayer game environments.

In game development, a “hacked” client is simply a client that bypasses intended controls and sends arbitrary requests to the backend. In a production game, this is not hypothetical. Any client-side logic can be modified, replayed, or automated.

This becomes critical once real value is involved. In-app purchases, progression systems, and competitive balance all depend on server-side validation. An unprotected API allows malicious actors to grant themselves paid items, manipulate game state, or degrade the experience for other players.

The web development equivalent is data exfiltration or unauthorized state changes. In both cases, trusting the client leads to compromised data, financial loss, and erosion of user trust.

Backend Architecture Under Sustained Load

Backend architecture in a multiplayer game requires more care than a typical web application due to sustained concurrency and high-frequency state updates. Instead of sporadic user requests, the backend must process continuous input streams from many clients simultaneously, amplifying the impact of memory usage, object lifetimes, and request volume.

Object Modeling: OOP vs Functional

Both object-oriented and functional approaches have valid use cases in game backends. Object-oriented modeling can make in-game entities and systems easier to reason about, particularly when representing persistent world objects.

A functional approach, however, reduces long-lived references and implicit state coupling. In Lua, this can significantly improve garbage collection behavior by avoiding accidental retention through shared tables or improperly scoped self references. Over time, this reduces memory pressure without requiring aggressive manual cleanup.

Throttling and Debouncing

Unlike most web applications, where requests are user-driven and relatively infrequent, game backends often receive continuous updates tied to simulation ticks or player movement. Without strict throttling and debouncing, even legitimate clients can overwhelm the server.

Input rate limiting becomes a core stability mechanism, not an optimization.

Payload Size and Access Patterns

High request frequency makes payload size and data access patterns critical. Minimizing serialized data and avoiding repeated linear scans prevents unnecessary allocation and CPU overhead.

Using lookup tables and caching derived state early reduced both bandwidth usage and server-side computation in my implementation, especially in hot paths executed every tick.

Designing a Luau Frontend with React-Lua

One advantage of working with Luau is its React-based UI framework, React-Lua. While not a one-to-one implementation of React, it follows the same mental model: declarative components, hooks for state management, and memoization for performance-sensitive updates.

From a design perspective, the primary challenge is not tooling but restraint. In-game UI competes directly with gameplay, and overcrowded interfaces degrade the player experience quickly. Most of my UI work involved designing reusable, configurable components that could be themed or extended without reworking layouts across projects.

A practical limitation of React-Lua is ecosystem maturity. Unlike the JavaScript React ecosystem, there are few established component libraries or abstractions to accelerate development. This shifts more responsibility onto the developer to build and maintain UI primitives, but it also encourages a deeper understanding of layout, state boundaries, and performance costs.

Post-Production Issues and Resolutions

I don’t write perfect code, and production has a way of exposing assumptions that seemed reasonable during development. A large part of engineering maturity is identifying those failures early, correcting them safely, and ensuring they don’t recur.

In this project, the most significant post-production issues stemmed from data integrity and schema design.

Pricing and Purchase History

In the initial implementation, in-game store pricing was stored per player as a mutable currentCost value, updated after each purchase. This approach worked until pricing logic needed to change. At that point, there was no reliable way to recompute prices for existing players.

The fix required a schema change with backward compatibility. Instead of storing derived state (currentCost), I began storing the number of times an item had been purchased and recalculating cost deterministically. To support existing players, I derived purchase counts by dividing their stored currentCost by the updated item price. This preserved existing data while restoring flexibility.

Spatial Data and Identifier Assumptions

Another issue surfaced in how resource deposits were stored. Each plot contained multiple deposit locations, and the original schema encoded deposits using plot-relative numeric ID ranges. This silently assumed a fixed spawn location.

When players spawned on a different plot, their stored data no longer aligned with the expected ID range, effectively orphaning their resources. Resolving this required decoupling storage from positional offsets and restructuring the schema to associate deposits explicitly with plot identifiers.

These issues were not subtle bugs; they were design assumptions that did not survive real usage. Correcting them reinforced the importance of modeling invariant data explicitly and avoiding derived state in persistent storage.

Conclusion

Building and deploying a multiplayer game challenged many assumptions I previously held about system design, state management, and operational reliability. While the domain differs from traditional web applications, the underlying problems—concurrency, trust boundaries, data integrity, and performance under load—are fundamentally the same.

What game development made more explicit was the cost of getting these details wrong. High-frequency input, persistent state, and real economic value leave little room for loose abstractions or deferred decisions. Architectural choices, data models, and validation strategies must hold up not just in development, but under sustained real-world usage.

Although this project began as an exploration of game development, it ultimately reshaped how I think about building and operating software systems in general.