A message bus is an extremely useful piece of technology. There are many implementations available out there: RabbitMQ, ActiveMQ, MSMQ, or Azure Service Bus, to name a few. However, their usage seems to be misunderstood by many developers.
Sometime last week, a couple of collegues and I were discussing what we envisioned for the system’s architecture in the mid-long term. Some of them brought up introducing a message bus to enable asynchronous processing. The way my teammates seemed to see it, a message bus would magically fix performance problems. I disagreed. This post aims to document my reflection on the subject.
The team I work with maintains and evolves a reservation system for a world-leading tour operator. The system is built using .NET technologies and exposes web APIs allowing various web sites to search for flights and to book seats, along with a back office allowing the company’s staff to manage flights, capacity and pricing.
The system’s architecture follows a pattern which is pretty common in old .NET projects and which I call a layered monolith. Layered, because the code is sliced in layers:
Monolith, for two very distinct reasons. First, the classes making those layers are most of the time tightly coupled together, meaning that it’s impossible to change the system’s behavior simply by recomposing its classes. The code must always be changed. Second, the boundaries between the functional domains are sometimes blurry, often unexistant. The system is a Big Ball of Mud in all of its glory.
What my collegues had in mind when they proposed a messaging bus was something in the like:
With this amended architecture, the presentation layer would send operations to the message bus. Additionally, some dispatcher process would monitor the bus and would dispatch all received operations to the proper service on the business layer.
The initial argument for this pattern is that scalability could be boosted by adding more instances of the dispatcher process, so more asynchronous operations could be processed in parallel.
Let’s see the implicit problems of this solution.
The system we work on globally follows the CQRS principles: operations are either commands or queries. Commands are used to change the system’s state, while queries simply retrieve some part of the system’s current state and are side-effect free.
While asynchronism can work for commands, it doesn’t work for queries, because they follow a request-response flow: a request querying for some data is received by the system, and it must respond with the proper data.
With the amended architecture, information can only flow down from the presentation layer. There is no way to get responses from the business layer. In order to enable such a thing, we would need to add a second bus for responses:
This means that the presentation layer, upon receiving a query, will first send a message on the bus, and will monitor the second bus, waiting for a response message.
This is completely useless. It adds a lot of complexity, and the presentation layer will still block while waiting for a response, because such queries are implicitely synchronous operations (don’t forget that here, the presentation layer is a web API).
Of course, the argument stating that it can scale more easily by adding more dispatching processes still stands. However, the same is true by simply adding instances of the web API itself, and such a solution is much simpler than this amended architecture. Additionally, it requires almost no changes in the existing architecture.
We could say that queries would bypass the whole bus layer, and would be sent synchronously by the presentation layer to the business layer, and that commands would be sent asynchronously through the bus layer:
However, not all commands are fire-and-forget. Some commands can be executed only when the system is in a specific state. Such commands must be validated before they can be executed to make sure they don’t violate any system invariant. If the validation process fails, the caller - may it be a user or another system - usually needs to be notified that the command failed and, more importantly, why it failed.
We now go back to the same problem we had with queries: information must flow both ways.
For me, a message bus really shines when they solve two types of problems:
Let me explain.
A command is typically made of:
Sometimes, a command’s side effects must be logically atomic with its pre-conditions. This means that the system’s state can’t mutate between the evaluation of the pre-conditions and the application of the side effects. For example, when the system’s state is stored using a relational database, this can be performed using a transaction.
There are however some side effects which can be eventually consistent. Such side effects can tolerate some form of delay between the execution of the rest of the command and their own execution.
To illustrate this, let’s redesign a small chunk of our monolith app, by separating flight management concerns and booking concerns in two (what out for the buzzword) micro-services.
When a user configures a new flight in the system, the booking engine, which is used by the client-facing websites to book seats, could be notified of the availability of the new flight asynchronously. From a business and domain perspective, there’s no need to update this engine’s data in the same transaction as the configuration command executed on the flight management module.
This type of scenario is perfect for a message bus:
With the new design, the user sends a command to the flight management service. The module first evaluates the command’s pre-conditions: it makes sure that the flight number provided by the user is unique, and that the departure time is in a predefined range of time (I wouldn’t buy tickets for a flight that takes off at 3 in the morning).
Next, the module updates its own state. This side-effect must be on the same transaction as the evaluation of the pre-conditions, because the pre-conditions checked for data uniqueness.
Once this is done, the service sends an event on a message bus, and can then return a positive response to the user, because the other side effect is processed asynchronously: the booking engine will eventually become consistent, by grabbing the event sent on the bus at its own speed and adding the newly configured flight to its own local state, so customers can book seats on it.
Eventually consistent side effects are not limited to data update: it can be an email or text message notification, for example.
A message bus is not a silver bullet. It adds complexity to a system, and must be used when it adds real value. In short:
https://martinfowler.com/articles/201701-event-driven.htmlWritten on February 21st , 2017 by Manuel Guilbault