MassTransit–Avoid losing messages

At one of my clients we had a situation where messages got lost after sending them to RabbitMQ. This is quite bad as the whole point of having a message based solution was to improve the reliability of our solutions(even when of the involved systems is offline or unavailable). In this post I want to explain what got wrong and how we introduced a solution to prevent this from happening in the future.

To understand the problem I first have to explain the concept of an exchange. In RabbitMQ, exchanges are message routing agents that are responsible for routing messages to different queues with the help of header attributes, bindings, and routing keys. A producer never sends a message directly to a queue. Instead, it uses an exchange as a routing mediator. Therefore, the exchange decides if the message goes to one queue, to multiple queues, or is simply discarded.

Let me emphasize one sentence here:

In RabbitMQ , a producer never sends a message directly to a queue

Only queues provide persistence of messages. This means that when a message is send to an exchange and there is no queue linked to that exchange the message is disposed. And that was what was happening in our system, due to a misconfiguration the exchange was not correctly linked to a queue and the messages send to this exchange got lost.

MassTransit follows the default conventions of RabbitMQ. So when you Send or Publish a message and no queue is bound to the exchange, the message is discarded without a warning or error.

If you don’t want this, you can use an overload and specify that a message should mandatory be routed to a destination:

Now you get an exception when no queue is bound to the exchange:

MassTransit.MessageReturnedException : exchange:UnknownMessage => The message was returned by RabbitMQ: 312-NO_ROUTE

Happy coding!

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...

The art of simplicity

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