medium![⚡_Latency_Optimization_Practical_Guide[20260108043442]](/_next/image?url=https%3A%2F%2Fres.cloudinary.com%2Fds3fvsnxb%2Fimage%2Fupload%2Fc_limit%2Cf_auto%2Cq_auto%3Abest%2Cw_800%2Cdpr_auto%2Cfl_progressive%2Cfl_strip_profile%2Fv1767881617%2Fblogwiz%2Farticles%2Fnxsrj0mcs9qjpeibzd6g.webp&w=3840&q=75)
webdev•Jan 8, 2026
⚡_Latency_Optimization_Practical_Guide[20260108043442]
In this article, I want to share practical latency optimization experience based on real project experience with a financial trading system that requires 99.9% of request latency to be below 10ms.
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Original source
Key summary
As an engineer focused on system performance optimization, the author shares practical latency optimization experience based on real project experience with a financial trading system that requires 99.9% of request latency to be below 10ms. The author highlights key characteristics of latency-sensitive applications and shares insights on how to optimize latency using various web frameworks.
TL;DR
- Latency-sensitive applications require monitoring processing time in real-time to identify performance bottlenecks.
- Memory allocation is a key factor affecting latency, and object pools can greatly reduce memory allocation time.
- Asynchronous processing and zero-copy design can reduce latency.
- Event-driven architecture and connection management can also reduce latency.
- Node.js has obvious latency problems when handling high-concurrency requests, while Go and Rust have natural advantages in latency control.
- Using DPDK and GPU for data processing can bypass the kernel network stack and directly operate on network cards, reducing latency.
- Service mesh can achieve finer-grained latency control.
This content is automatically summarized using AI. For full context, always refer to the original article.
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