A key metric for measuring the ability to handle concurrent requests is QPS, or queries per second. QPS is a very intuitive performance metric, representing the number of requests a server or application system can handle per unit time. QPS protection, as the name suggests, is a protection mechanism against request frequency. Its core goal is to ensure stable system operation and a normal user experience even under high concurrency or malicious traffic attacks.

　　To understand QPS protection, you must first understand the characteristics of QPS itself. A service's QPS capacity is not unlimited; it is limited by multiple factors, such as CPU, memory, network bandwidth, and database performance. If the number of requests exceeds the system's capacity, response latency will inevitably increase, requests will be dropped, or even the system will crash. Normally, QPS growth gradually increases with business development, and the operations team can address this through capacity expansion and optimization. However, in reality, systems may experience sudden traffic spikes, such as during major e-commerce promotions or popular live streaming events, or be subject to malicious attacks such as CC attacks or DDoS attacks. Without effective QPS protection, the system could be instantly overwhelmed.

　　The core principles of QPS protection can be summarized into three levels:

　　First, identify abnormal traffic. The system must be able to distinguish between legitimate user requests and malicious attack traffic, determining which requests require interception through behavioral analysis, request characteristics, and access patterns. Second, implement rate limiting and filtering. When the request volume exceeds a preset threshold, the system automatically discards excess requests or blocks frequently accessing IP addresses based on policy, thereby preventing resource abuse. Third, implement load balancing and cache offload. Load balancers can distribute requests to different servers, or the cache layer can directly handle some requests, alleviating pressure on the origin server.

　　Common QPS protection methods include:

　　First, threshold-based rate limiting. When the number of requests to a specific interface exceeds a set limit, the system rejects subsequent requests. Second, token bucket or leaky bucket algorithms control the request rate to smooth out bursts and ensure system operation within a controllable range. Third, frequency control. For example, if the same IP makes too many requests within a short period of time, the system will identify it as suspicious and throttle it. Fourth, introduce high-defense services, such as CDN high-defense nodes or cloud protection platforms, to absorb and clean traffic, distributing normal traffic to the origin server. Fifth, implement caching mechanisms, storing static content and hotspot data in the cache. This eliminates the need for user requests to access the database every time, effectively reducing QPS pressure.

　　QPS protection is widely used in multiple industries:

　　Take e-commerce websites, for example. During major holiday sales, traffic increases exponentially, and the number of concurrent requests can exceed dozens of times the normal level. Without QPS protection, databases and application servers can easily become overloaded, causing users to experience page failures or payment failures. Through rate limiting, caching, and load balancing, e-commerce platforms can control traffic within reasonable limits, ensuring both user experience and backend system stability. Online gaming is another typical scenario, where game servers must respond to the actions of thousands of players in real time. A flood of malicious requests can cause game delays, lags, or even disconnections. QPS protection not only protects against attacks but also ensures real-time and fair play. Financial trading systems place even higher demands on QPS protection, as any delay or downtime can result in significant financial losses. Therefore, financial institutions often employ multi-layered protection measures, including hardware firewalls, distributed caching, and intelligent risk control systems, to ensure smooth and secure transactions even under high concurrency.

　　In addition, many open API platforms also impose strict QPS limits. When developers call the API, each account or IP address has a fixed QPS limit. Exceeding the limit results in an error or throttling. This approach not only protects platform resources but also prevents abuse by certain users from impacting others. In scenarios like mobile apps, content distribution, online education, and social platforms, QPS protection has become standard, indispensable for nearly all high-traffic businesses.

　　Practical QPS protection also presents some common problems:

　　First, the difficulty of setting thresholds. If set too low, legitimate users may be accidentally affected, resulting in a poor user experience; if set too high, the protection is ineffective. Therefore, thresholds need to be dynamically adjusted based on business characteristics and traffic models. Second, there are the issues of false positives and missed detections. Certain normal bursts of traffic, such as e-commerce flash sales, may be misidentified by the system as malicious attacks, resulting in some users being blocked. Conversely, if an attacker simulates normal user behavior, the system may not be able to accurately identify the attacker, creating a protection vulnerability. Furthermore, there's the issue of performance overhead. Complex protection mechanisms inherently consume system resources. If poorly designed, they can backfire and increase the system's burden. Finally, there's the issue of user experience. Overly stringent protection can lead to users frequently encountering verification codes and human-machine authentication, raising the barrier to entry and impacting conversion rates.

　　Addressing these issues requires a combination of technical means and policy optimization. On the one hand, intelligent protection systems can incorporate big data analysis and machine learning to identify normal and abnormal traffic based on historical access behavior, reducing false positives. On the other hand, a layered approach to QPS protection can be employed, such as implementing high-volume traffic cleaning at the ingress layer and fine-grained traffic limiting at the application layer. This approach effectively intercepts attacks while ensuring a positive user experience. Furthermore, policies should be flexibly adjusted to suit specific business scenarios, such as relaxing thresholds during promotional periods and resuming normal limits after the event.

　　QPS protection isn't just about defending against attacks; it's more importantly about ensuring system stability and sustainable operations. The core competitiveness of internet businesses is often reflected in service quality. Any downtime caused by traffic surges can lead to user loss and brand damage. Scientific and rational QPS protection not only enhances risk mitigation but also provides solid technical support for business expansion.