Optimization of the online collaboration platform: an event queue management algorithm based on debounce and throttle techniques
Tianbao Xue, Quanxiang Lan · 2025
Against the backdrop of accelerating digital transformation, online collaboration platforms have become indispensable tools for remote work and learning. However, with the increase in the number of users, the problem of high concurrency event processing has become increasingly prominent. Therefore, this paper proposes an event queue management algorithm (EQA-DT) based on debouncing and throttling techniques to optimize the efficiency of event processing in high concurrency scenarios. The EQA-DT algorithm uses a debounce technique to control the frequency of event capture, ensuring that only the last event triggered by the user is recorded within a short period of time. It also uses a throttling technique to periodically push events in the queue to the server, limiting the frequency of events being pushed to the server. We compared the EQA-DT algorithm with traditional event processing methods in an experimental setting. The results showed that in high concurrency scenarios, the EQA-DT algorithm is superior to traditional event processing methods in terms of reducing server pressure, reducing the length of the event queue, and improving system response speed. This algorithm provides an effective solution for improving the performance of online collaboration platforms and lays the foundation for future research in this area.