FastAPI Backend Development Bootcamp · 课时

使用发布/订阅后端扩展 WebSockets

学习如何使用 Redis 发布/订阅后端，在多个 FastAPI 实例之间广播 WebSocket 消息。

第 4 / 4 课13 个步骤

使用发布/订阅后端扩展 WebSockets 是 CoddyKit 上的免费 FastAPI Backend Development Bootcamp 课时。这是第 4 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 FastAPI Backend Development Bootcamp 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The Multi-Instance Problem

A single FastAPI process can hold WebSocket connections in memory. But in production you run multiple instances behind a load balancer.

A message arriving on instance A cannot reach a client connected to instance B unless the instances share state.

What is a Backplane?

A backplane is a shared messaging channel that every instance subscribes to. When one instance receives an event, it publishes to the backplane and all instances relay it to their local clients.

Redis Pub/Sub

Redis pub/sub is a popular backplane: a PUBLISH to a channel is delivered to every SUBSCRIBEr in real time.

PUBLISH chat:room1 "hello"
SUBSCRIBE chat:room1

Local Connection Manager

Each instance still tracks its own connected clients in memory.

class ConnectionManager:
    def __init__(self):
        self.active = []
    async def broadcast_local(self, msg):
        for ws in self.active:
            await ws.send_text(msg)

Publishing to Redis

Instead of broadcasting locally, publish incoming messages to a Redis channel.

import redis.asyncio as redis

r = redis.Redis()
async def publish(channel, message):
    await r.publish(channel, message)

Subscribing on Startup

Each instance opens a Redis subscription and listens in a background task.

async def listen():
    pubsub = r.pubsub()
    await pubsub.subscribe("chat:room1")
    async for msg in pubsub.listen():
        if msg["type"] == "message":
            await manager.broadcast_local(msg["data"])

Wiring It with Lifespan

Start the listener when the app boots using FastAPI lifespan events.

from contextlib import asynccontextmanager

@asynccontextmanager
async def lifespan(app):
    task = asyncio.create_task(listen())
    yield
    task.cancel()

The Full Message Flow

Client to instance A then A publishes to Redis then Redis fans out to A and B then each relays to its local clients.

Now any client receives any message regardless of which instance it connected to.

Per-Room Channels

Use one Redis channel per chat room so instances only receive messages relevant to their connected clients.

channel = "chat:" + room_id
await r.publish(channel, payload)

Avoiding Echo Loops

Because the publisher also receives its own message via the subscription, broadcast only from the listener, not directly, to avoid sending twice.

Sticky Sessions vs Stateless

With a backplane, instances become effectively stateless for messaging, so you no longer need sticky sessions on the load balancer for delivery to work.

Quick Check

Test your scaling knowledge.

在交互式课程中回答

Recap

You learned to scale real-time apps horizontally:

In-memory managers only reach local clients
A Redis pub/sub backplane fans messages out across instances
Use per-room channels and avoid echo loops

This pattern lets WebSocket apps scale to many instances behind a load balancer.

免费开始

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常见问题解答

「使用发布/订阅后端扩展 WebSockets」课时是免费的吗？

是的 — 「使用发布/订阅后端扩展 WebSockets」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 FastAPI Backend Development Bootcamp 课程的其余内容，请升级到 CoddyKit PRO。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

「使用发布/订阅后端扩展 WebSockets」这节课中我会学到什么？

学习如何使用 Redis 发布/订阅后端，在多个 FastAPI 实例之间广播 WebSocket 消息。你通过在浏览器中直接运行的动手代码来练习 FastAPI Backend Development Bootcamp，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 FastAPI Backend Development Bootcamp 需要有经验吗？

无需任何先前经验。CoddyKit 上的 FastAPI Backend Development Bootcamp 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 4 节课，共 4 节。

「使用发布/订阅后端扩展 WebSockets」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 FastAPI Backend Development Bootcamp 课中编写并运行代码吗？

能。每节 FastAPI Backend Development Bootcamp 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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