Exploring fasthttp: Unraveling Its Performance Superiority
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Chapter 1: Understanding fasthttp's Performance
fasthttp, an HTTP package crafted in Go, prioritizes exceptional speed. Its design optimizes the "hot path" within the HTTP request-response cycle, aiming for zero memory allocation and surpassing the performance of the standard net/http library by a factor of ten. This assertion from the official GitHub repository reflects the author’s strong belief in the project's capabilities.
While this article does not focus on how to implement fasthttp, it will analyze the underlying principles that contribute to its remarkable performance.
Benchmarking Performance
To verify fasthttp's claims of superiority over net/http, I conducted a series of tests on my laptop. Note that results may vary based on your system specifications.
Test Environment:
- Mac M1 Pro
- Go Version: 1.21.1
- fasthttp Version: bbc7bd04e2cb3747dad23b1c0a9e6ff22df5d449
- GOMAXPROCS: 4
The initial benchmarking was done using net/http:
GOMAXPROCS=1 go test -bench=NetHTTPServerGet -benchmem -benchtime=10s
Results:
- BenchmarkNetHTTPServerGet1ReqPerConn: 1,663,719 ops, 7108 ns/op, 3146 B/op, 36 allocs/op
- BenchmarkNetHTTPServerGet2ReqPerConn: 2,102,826 ops, 6249 ns/op, 2745 B/op, 28 allocs/op
- BenchmarkNetHTTPServerGet10KReqPerConn: 2,847,196 ops, 4155 ns/op, 2353 B/op, 21 allocs/op
Subsequently, I ran benchmarks using the fasthttp server:
GOMAXPROCS=4 go test -bench=kServerGet -benchmem -benchtime=10s
Results:
- BenchmarkServerGet1ReqPerConn-4: 10,046,025 ops, 1106 ns/op
- BenchmarkServerGet10KReqPerConn-4: 21,422,018 ops, 529.6 ns/op
- BenchmarkServerGet100ReqPerConn10KClients-4: 20,419,492 ops, 522.2 ns/op
The results indicate that fasthttp significantly outperforms the net/http standard library, showcasing optimized memory management.
Core Optimization Aspects
Object Reuse
The workerPool manages connections, allowing for efficient processing without starting a new goroutine for each request. This design helps maintain CPU cache efficiency.
type workerPool struct {
ready []*workerChan
workerChanPool sync.Pool
}
Request and Response Management
Fasthttp employs object pooling for requests and responses, which conserves memory and improves speed:
var requestPool sync.Pool
Cookie Management
Fasthttp also features a pool for Cookie objects, ensuring efficient reuse:
var cookiePool = &sync.Pool{
New: func() interface{} {
return &Cookie{}},
}
Avoiding Reflection
Fasthttp avoids reflection in deep copy implementations, opting for manual copying of fields for efficiency.
Challenges with fasthttp
Despite its high performance, fasthttp presents certain drawbacks:
- Reduced Code Clarity: Understanding fasthttp's code can be challenging without familiarity with its design.
- Increased Development Difficulty: Developers may face a steeper learning curve due to manual object management.
- Greater Cognitive Load: Those used to net/http may struggle to adapt, increasing the risk of bugs.
Performance Optimization Techniques
- Use reuseport for better performance on multi-core systems.
- Consider running separate server instances for each CPU core.
- Balance interrupts from multi-queue network cards across CPU cores.
Best Practices for Using fasthttp
- Maximize object and buffer reuse.
- Conduct thorough performance analyses in production.
- Write tests for critical code paths.
- Avoid unnecessary type conversions between []byte and string.
Conclusion
Fasthttp is optimized for high-performance scenarios, making it a sensible choice for applications requiring high QPS and low latency. However, if the complexity and cognitive demands outweigh the performance benefits, sticking with net/http may be wiser for many developers, especially in low-traffic environments.
References
How to Download Any File Faster on Windows 10 - YouTube: A guide on enhancing file download speeds on Windows 10, providing tips and tricks for better performance.
Build .NET MAUI Apps Faster with App Accelerator - YouTube: An overview of utilizing the App Accelerator to streamline .NET MAUI app development, focusing on enhancing productivity.