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I ran into this one the hard way.
I built a real-time notification feature with SignalR, tested it locally, everything worked great. Then I scaled to two instances behind a load balancer, and notifications started disappearing for some users.
The code was fine. The problem was that SignalR connections are bound to the server process that accepted them. Each instance only knows about its own connections. So when an API request lands on Server 1 but the user is connected to Server 2, the notification just... doesn't get delivered.
This is the SignalR scale-out problem, and it bites almost everyone who goes from one instance to more.
Why SignalR Breaks When You Scale Out
With a single instance, everything just works.
The server holds the full map of who's connected, so sending a message to a user, a group, or all clients works because that map is complete.
But scale out to two or more instances, and that map fractures.
Server 1 has no idea Client 3 or Client 4 even exist. So when an order status change happens on Server 1 and needs to reach Client 3, Server 1 checks its connection map, finds nothing, and the message is quietly dropped.
The Backplane Pattern
The fix is a backplane - a shared messaging layer that sits between all your server instances.
Every server publishes outgoing messages to a central channel, and every server subscribes to that same channel. When a message comes in, each server checks if any of its local connections should receive it.
When Server 1 wants to notify Client 3:
- Server 1 publishes the message to the backplane
- All servers receive the message
- Server 2 recognizes Client 3 as one of its connections and delivers the notification
From your code's perspective, it looks like every server can see every connection. Redis works really well for this because its Pub/Sub delivers messages to all subscribers in near real-time. And if you're already using Redis for distributed caching, you don't even need to spin up anything new.
Setting It Up
Let me walk through how I set this up in an order notification system. Clients connect to a SignalR hub, authenticate via JWT, and get real-time status updates when an order changes.
Install the NuGet Package
dotnet add package Microsoft.AspNetCore.SignalR.StackExchangeRedis
Register the Backplane
Chain .AddStackExchangeRedis() onto your AddSignalR() call:
builder.Services.AddSignalR()
.AddStackExchangeRedis(builder.Configuration.GetConnectionString("cache")!);
If you're running with .NET Aspire, you will have the Redis connection string registered via environment variables, so you can just pull it from configuration. Reference the same named connection:
builder.AddRedisDistributedCache("cache");
builder.Services.AddSignalR()
.AddStackExchangeRedis(builder.Configuration.GetConnectionString("cache")!);
If you have multiple SignalR apps sharing the same Redis instance, you'll want to add a channel prefix. Otherwise, messages from one app will reach subscribers in every app on that Redis server.
builder.Services.AddSignalR()
.AddStackExchangeRedis(connectionString, options =>
{
options.Configuration.ChannelPrefix = RedisChannel.Literal("OrderNotifications");
});
The nice thing is that your IHubContext<> call site doesn't change at all.
Clients.User(...) works the same whether you have one instance or ten - the backplane handles routing behind the scenes.
When I tested this with two replicas in .NET Aspire, I tagged each notification with the sending instance's ID. A client on Replica 1 received a notification stamped with Replica 2's ID, which confirmed the message was crossing instances through Redis.
The Sticky Sessions Requirement
This is something you should know before you set up the backplane: you still need sticky sessions.
The Redis backplane solves message routing, but it does not remove the need for sticky sessions.
SignalR's connection negotiation is a two-step process:
- The client sends a
POSTto/hub/negotiateto obtain a connection token - The client uses that token to establish the WebSocket connection
Both requests must land on the same server. If your load balancer routes the negotiation to Server 1 but the WebSocket upgrade to Server 2, the connection fails.
Make sure sticky sessions are enabled in your load balancer. Most load balancers support this via IP hash or cookie affinity - check the docs for whichever you're using.
What Happens When Redis Goes Down?
One thing worth knowing: SignalR does not buffer messages when Redis is unavailable.
If Redis stops responding, any messages sent during the outage are simply lost. SignalR may throw exceptions, but your existing WebSocket connections stay open - clients don't get disconnected. Once Redis comes back, SignalR reconnects automatically.
For most real-time scenarios like order updates or live dashboards, this is fine. The next state change triggers a fresh notification anyway, or the user can just reload. If you're dealing with something more critical (financial data, operational alerts), you'll want a reconciliation strategy on reconnect or a durable queue running alongside.
Redis Backplane vs. Azure SignalR Service
If you're on Azure, the managed Azure SignalR Service is worth considering. It proxies all client connections through the service, so sticky sessions aren't required and your servers only hold a small number of constant connections to the service.
The Redis backplane is the better fit when you're self-hosted, latency-sensitive, or already running Redis. For everything else, Azure SignalR Service is the cleaner option.
Summary
Honestly, the Redis backplane is almost too simple to set up.
One method call on AddSignalR() and your app goes from silently dropping messages to routing them across every instance.
You don't need to make changes to your hub code, client code, or application logic.
Just remember two things:
- You still need sticky sessions
- Messages aren't buffered if Redis goes down temporarily
Get those two right and SignalR scales out just as smoothly as the rest of your stack.
If you want to go deeper on building real-time features and APIs in .NET, check out my Pragmatic REST APIs course.
Hope this was useful. See you next week.
