Discussion on performance optimization technology of gateway current limiting function

With the popularity of microservice architecture and cloud computing, API gateway has become an indispensable part of modern distributed system architecture. API gateway is responsible for processing all requests from clients and providing functions such as routing, authentication, current limiting, and circuit breaking. Among them, the current limiting function is particularly important, as it can protect backend services from the impact of high concurrent requests. However, the implementation of the current limiting function is often accompanied by performance loss. This article will explore how to optimize the performance of the gateway current limiting function through a series of technical means.

1. Current Limiting Algorithm Selection

The choice of current limiting algorithm directly determines the performance and effect of the current limiting function. Common current limiting algorithms include leaky bucket algorithm, token bucket algorithm, sliding window algorithm, etc.

1. Leaky Bucket Algorithm

The leaky bucket algorithm treats requests as water flow, and the flow limiter is the leaky bucket. The speed of water flowing in may vary, but the speed of flowing out (i.e. processing requests) is constant. The leaky bucket algorithm can smooth burst traffic, but it may cause resource waste because when the bucket is full, excess requests will be discarded.

2. Token Bucket Algorithm

In the token bucket algorithm, tokens are put into the bucket at a certain rate, and each request consumes a token. If there is a token in the bucket, the request is processed; if there is no token in the bucket, the request is rejected. The token bucket algorithm can handle burst traffic, but it may cause response delays.

3. Sliding Window Algorithm

The sliding window algorithm divides time into multiple windows and counts the number of requests in each window. When the number of requests in a window exceeds the limit, subsequent requests are rejected. The sliding window algorithm can accurately control the number of requests in each time period, but its implementation is relatively complex.

When choosing a current limiting algorithm, you need to weigh it based on the business scenario and performance requirements. For example, for scenarios with high real-time requirements, you can choose the token bucket algorithm; for scenarios that need to smooth burst traffic, you can choose the leaky bucket algorithm.

2. Cache Optimization

Caching is one of the important means to optimize gateway performance. In the current limiting function, you can reduce the number of database or remote service accesses by caching user information, current limiting rules and other data, thereby reducing latency and improving throughput.

1. Local Cache

Local cache stores data in the gateway's memory, which has fast access speed but limited capacity. You can use local cache frameworks such as Guava Cache and Caffeine to implement this. For data that is frequently accessed and does not change frequently, such as user information, current limiting rules, etc., you can use local cache to improve performance.

2. Distributed Cache

Distributed cache stores data on multiple nodes, which can achieve high concurrent access and horizontal expansion. Redis, Memcached, etc. are common distributed cache systems. For scenarios where data needs to be shared or capacity requirements are large, distributed cache can be used to improve performance.

3. Asynchronous processing

Asynchronous processing can execute time-consuming operations in background threads to avoid blocking the main thread, thereby improving system throughput. In the current limiting function, performance can be optimized by asynchronously loading current limiting rules and asynchronously recording logs.

1. Asynchronous loading current limiting rules

The current limiting rules may be adjusted dynamically according to business needs. If the current limiting rules are loaded from the database or remote service in real time for each request, it will cause unnecessary performance loss. Therefore, the current limiting rules can be loaded into the local cache and the cache can be updated asynchronously when the rules change.

2. Asynchronous logging

Logging is an important means of system monitoring and troubleshooting. However, if logging is performed in real time for each request, it will have a certain impact on system performance. Therefore, the logging operation can be performed asynchronously in a background thread to avoid blocking the main thread.

4. Optimize network transmission

Network transmission is one of the bottlenecks of gateway performance. Optimizing network transmission can reduce latency and improve throughput.

1. Use HTTP/2 protocol

The HTTP/2 protocol has better performance and higher efficiency than HTTP/1.1. It supports multiplexing, header compression and other features, which can reduce network transmission overhead and latency.

2. Use a connection pool

The connection pool can reuse established TCP connections to avoid frequent establishment and closing of connections, thereby reducing network transmission overhead and latency.

V. Conclusion

Optimizing the performance of the gateway current limiting function is a complex and important task. By selecting appropriate current limiting algorithms, optimizing cache, asynchronous processing, optimizing network transmission, etc., the performance of the gateway current limiting function can be significantly improved, thereby protecting the backend service from the impact of high concurrent requests. In actual applications, it is necessary to weigh and select according to business scenarios and performance requirements to achieve the best results.