惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
大猫的无限游戏
大猫的无限游戏
Google DeepMind News
Google DeepMind News
V
V2EX
T
The Blog of Author Tim Ferriss
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Hugging Face - Blog
Hugging Face - Blog
Stack Overflow Blog
Stack Overflow Blog
I
InfoQ
博客园_首页
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Last Week in AI
Last Week in AI
Recent Announcements
Recent Announcements
Vercel News
Vercel News
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
美团技术团队
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
Blog — PlanetScale
Blog — PlanetScale
GbyAI
GbyAI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
J
Java Code Geeks

ByteByteGo Newsletter

Why An LLM’s Memory Gets Expensive and How to Fix It LLM Security Basics: The Full Threat Model Hiring: Part Time Instructor, Write Production Grade Code with AI A Detailed Guide to Idempotency, Delivery Semantics, and Deduplication How ChatGPT Optimizes its Agent Loop: Harness, API, and Inference Why DoorDash, Instacart, and Uber Eats Integrated LLMs Into Search Three Different Ways How NVIDIA Builds Open Models for the Age of AI A Beginner’s Guide to Clocks, Causality, and Ordering in Distributed Systems Best Practices for Building AI Agents That Work in Production Inside Roblox’s Bet on World Models MCP vs A2A vs ACP: How AI Agents Actually Talk to Each Other A Guide to Multi-Tenancy: Benefits and Challenges AI Customer Support at Scale: The Travel Industry’s $Billion Bet How LLMs Learn to Be Helpful (RLHF vs DPO) How Microsoft Ships AI Agents at Enterprise Scale EP221: How Docker Works Under the Hood LAST CALL FOR ENROLLMENT: Become an AI Engineer - Cohort 7 Streaming vs Batch: Two Philosophies of Data Processing The Agent Loop: How AI Goes From Answering Questions to Doing Things ChatGPT vs Gemini vs Claude: How They Differ LAST CALL FOR ENROLLMENT: Become an AI Engineer - Cohort 7 Proof of Human: How to Verify a Person Is Real and Unique Multi-Region Architecture: Going Global Without Going Broke How OpenAI Delivers Low-Latency Voice AI for 900M Users Inside Thinking Machines’ Interaction Models How AI Agents Manage Memory and Avoid Forgetfulness EP220: RAG vs Graph RAG vs Agentic RAG Top Anti-Patterns to Avoid in Service Architecture Large Language Models vs Small Language Models An Ex-Meta L8’s Agentic Engineering Setup
A Guide to Async Patterns in API Design
ByteByteGo · 2026-05-21 · via ByteByteGo Newsletter

The default model for client-server communication is request-response. The client sends a request, the server returns a response, and the connection closes. This handles the majority of what most software needs to do, and it covers an enormous range of practical scenarios in modern web applications.

It doesn’t handle everything. Some work takes too long to complete inside a single request. Some events happen on the server’s schedule, not the client’s. Some interactions are continuous rather than one-shot. And some messages need to outlive the moment they were sent.

Async API patterns are the techniques engineers use for these cases, and the list has grown over the years. It now includes short polling, long polling, server-sent events, WebSockets, webhooks, async APIs with status polling, message queues, and GraphQL subscriptions. Each has its own design and preferred use case. What they share is one purpose, which is to extend what’s possible beyond a single HTTP request and response.

In this article, we will look at each of these patterns in detail, along with their advantages. We’ll start by looking at where request-response stops fitting and then walk through each pattern in turn.