Designing Scalable Distributed Architecture for QQGame: Lessons in High‑Performance Backend

QQGame supports an enormous number of simultaneous players, organizing them into many game rooms that each have a maximum capacity (typically 400). When a player selects a room, the client must first ensure the room is not full, otherwise the entry request fails.

Because players choose rooms on the client side, the server cannot directly control the selection; instead, the server sends a copy of all room occupancy data to the client, which the client uses to display available rooms.

This client‑driven behavior follows the principle that any functionality initiated by the user should reside on the client and maintain a synchronized data replica of the server source.

A major deficiency is that the client does not refresh room counts after a player leaves a room, so stale data often leads to failed entry attempts, sometimes requiring several retries.

Fetching real‑time room data for millions of users would overwhelm the system; a single server could not handle the resulting tens of millions of requests per second.

To cope with such load, thousands of servers must be clustered, raising challenges of data consistency and integrity across the cluster.

The solution applies a "divide and conquer" (sharding) strategy and scale‑out architecture: requests are partitioned by region (e.g., Shenzhen, Sichuan, North America) and assigned to dedicated servers, avoiding circular dependencies on shared data.

All requests for the same room are routed to a dedicated "room management server" so that processing is autonomous and does not interfere with other rooms.

For real‑time updates of room counts within a region, a "region management server" aggregates changes from its room servers; multiple region servers operate with a master‑slave setup to share data without bottlenecks.

Horizontal partitioning of database tables by geographic region further eliminates cyclic dependencies, ensuring each database instance contains a self‑contained data subset.

Overall, the proposed distributed architecture meets the stringent performance requirements of QQGame, though issues such as network latency and inter‑server performance bottlenecks remain for future investigation.