A Vision for Low Ceremony CQRS with Event Sourcing

Let me just put this stake into the ground. The combination of Marten and Jasper will quickly become the most productive and lowest ceremony tooling for event sourcing and CQRS architectures on the .NET stack.

And for any George Strait fans out there, this song may be relevant to the previous paragraph:)

CQRS and Event Sourcing

Just to define some terminology, the Command Query Responsibility Separation (CQRS) pattern was first described by Greg Young as an approach to software architecture where you very consciously separate “writes” that change the state of the system from “reads” against the system state. The key to CQRS is to use separate models of the system state for writing versus querying. That leads to something I think of as the “scary view of CQRS”:

The “scary” view of CQRS

The “query database” in my diagram is meant to be an optimized storage for the queries needed by the system’s clients. The “domain model storage” is some kind of persisted storage that’s optimized for writes — both capturing new data and presenting exactly the current system state that the command handlers would need in order to process or validate incoming commands.

Many folks have a visceral, negative reaction to this kind of CQRS diagram as it does appear to be more complexity than the more traditional approach where you have a single database model — almost inevitably in a relational database of some kind — and both reads and writes work off the same set of database tables. Hell, I walked out of an early presentation by Greg Young about what later became to be known as CQRS at QCon 2008 shaking my head thinking this was all nuts.

Here’s the deal though, when we’re writing systems against the one, true database model, we’re potentially spending a lot of time mapping incoming messages to the stored model, and probably even more time transforming the raw database model into a shape that’s appropriate for our system’s clients in a way that also creates some decoupling between clients and the ugly, raw details of our underlying database. The “scary” CQRS architecture arguably just brings that sometimes hidden work and complexity into the light of day.

Now let’s move on to Event Sourcing. Event sourcing is a style of system persistence where each state change is captured and stored as an explicit event object. There’s all sorts of value from keeping the raw change events around for later, but you of course do need to compile the events into derived, or “projected” models that represent the current system state. When combining event sourcing into a CQRS architecture, some projected models from the raw events can serve as both the “write model” inside of incoming commands, while others can be built as the “query model” that clients will use to query our system.

At the end of this section, I want to be clear that event sourcing and CQRS can be used independently of each other, but to paraphrase Forrest Gump, event sourcing and CQRS go together like peas and carrots.

Now, the event sourcing side of this especially might sound a little scary if you’re not familiar with some of the existing tooling, so let’s move on first to Marten…

Marten has a mature feature set for event sourcing already, and we’re grown quite a bit in capability toward our support for projections (read-only views of the raw events). If you’ll peek back at the “scary” view of CQRS above, Marten’s projection support solves the problem of keeping the raw events synchronized to both any kind of “write model” for incoming commands and the richer “query model” views for system clients within a single database. In my opinion, Marten removes the “scary” out of event sourcing and we’re on our way to being the best possible “event sourcing in a box” solution for .NET.

As that support has gotten more capable and robust though, our users have frequently been asking about how to subscribe to events being captured in Marten and how to relay those events reliably to some other kind of infrastructure — which could be a completely separate database, outgoing message queues, or some other kind of event streaming scenario.

Oskar Dudycz and I have been discussing how to solve this in Marten, and we’ve come up with these three main mechanisms for supporting subscriptions to event data:

  1. Some or all of the events being captured by Marten should be forwarded automatically at the time of event capture (IDocumentSession.SaveChangesAsync())
  2. When strict ordering is required, we’ll need some kind of integration into Marten’s async daemon to relay events to external infrastructure
  3. For massive amounts of data with ambitious performance targets, use something like Debezium to directly stream Marten events from Postgresql to Kafka/Pulsar/etc.

Especially for the first item in that list above, we need some kind of outbox integration with Marten sessions to reliably relay events from Marten to outgoing transports while keeping the system in a consistent state (i.e., don’t publish the events if the transaction fails).

Fortunately, there’s a functional outbox implementation for Marten in…

To be clear, Jasper as a project was pretty well mothballed by the combination of COVID and the massive slog toward Marten 4.0 (and then a smaller slog to 5.0). However, I’ve been able to get back to Jasper and yesterday kicked out a new Jasper 2.0.0-alpha-2 release and (Re) Introducing Jasper as a Command Bus.

For this post though, I want to explore the potential of the Jasper + Marten combination for CQRS architectures. To that end, a couple weeks I published Marten just got better for CQRS architectures, which showed some new APIs in Marten to simplify repetitive code around using Marten event sourcing within CQRS architectures. Part of the sample code in that post was this MVC Core controller that used some newer Marten functionality to handle an incoming command:

public async Task CompleteCharting(
    [FromBody] CompleteCharting charting, 
    [FromServices] IDocumentSession session)
    var stream = await session
    // Validation on the ProviderShift aggregate
    if (stream.Aggregate.Status != ProviderStatus.Charting)
        throw new Exception("The shift is not currently charting");
    // We "decided" to emit one new event
    stream.AppendOne(new ChartingFinished(stream.Aggregate.AppointmentId.Value, stream.Aggregate.BoardId));
    await session.SaveChangesAsync();

To review, that controller method:

  1. Takes in a command message of type CompleteCharting
  2. Loads the current state of the aggregate ProviderShift model referred to by the incoming command, and does so in a way that takes care of concurrency for us by waiting to get an exclusive lock on the particular ProviderShift
  3. Assuming that the validation against the ProviderShift succeeds, emits a new ChartingFinished event
  4. Saves the pending work with a database transaction

In that post, I pointed out that there were some potential flaws or missing functionality with this approach:

  1. We probably want some error handling to retry the operation if we hit concurrency exceptions or timeout trying to get the exclusive lock. In other words, we have to plan for concurrency exceptions
  2. It’d be good to be able to automatically publish the new ChartingFinished event to a queue to take further action within our system (or an external service if we were using messaging here)
  3. Lastly, I’d argue there’s some repetitive code up there that could be simplified

To address these points, I’m going to introduce Jasper and its integration with Marten (Jasper.Persistence.Marten) to the telehealth portal sample from my previous blog post.

I’m going to move the actual handling of the CompleteCharting to a Jasper handler shown below that is functionally equivalent to the controller method shown earlier (except I switched the concurrency protection to being optimistic):

// This is auto-discovered by Jasper
public class CompleteChartingHandler
    [MartenCommandWorkflow] // this opts into some Jasper middlware 
    public ChartingFinished Handle(CompleteCharting charting, ProviderShift shift)
        if (shift.Status != ProviderStatus.Charting)
            throw new Exception("The shift is not currently charting");

        return new ChartingFinished(charting.AppointmentId, shift.BoardId);

And the controller method gets simplified down to just relaying the command to Jasper:

    public Task CompleteCharting(
        [FromBody] CompleteCharting charting, 
        [FromServices] ICommandBus bus)
        // Just delegating to Jasper here
        return bus.InvokeAsync(charting, HttpContext.RequestAborted);

There’s some opportunity for some mechanisms to make the code above be a little less repetitive and efficient. Maybe by riding on Minimal APIs. That’s for a later date though:)

By using the new [MartenCommandWorkflow] attribute, we’re directing Jasper to surround the command handler with middleware that handles much of the Marten mechanics by:

  1. Loading the aggregate ProviderShift for the incoming CompleteCharting command (I’m omitting some details here for brevity, but there’s a naming convention that can be explicitly overridden to pluck the aggregate identity off the incoming command)
  2. Passing that ProviderShift aggregate into the Handle() method above
  3. Applying the returned event to the event stream for the ProviderShift
  4. Committing the outstanding changes in the active Marten session

The Handle() code above becomes an example of a Decider function. Even better yet, it’s completely decoupled from any kind of infrastructure and fully synchronous. I’m going to argue that this approach will make command handlers much easier to unit test, definitely easier to write, and easier to read later just because you’re only focused on the business logic.

So that covers the repetitive code problem, but let’s move on to automatically publishing the ChartingCompleted event and some error handling. I’m going to add Jasper through the application’s bootstrapping code as shown below:

builder.Host.UseJasper(opts =>
    // I'm choosing to process any ChartingFinished event messages
    // in a separate, local queue with persistent messages for the inbox/outbox
    // If we encounter a concurrency exception, just try it immediately 
    // up to 3 times total
    // It's an imperfect world, and sometimes transient connectivity errors
    // to the database happen
        .RetryWithCooldown(50.Milliseconds(), 100.Milliseconds(), 250.Milliseconds());

Jasper comes with some pretty strong exception handling policy capabilities you’ll need to do grown up development. In this case, I’m just setting up some global policies in the application to retry message failures on either Marten concurrency exceptions or the inevitable, transient Postgresql connectivity hiccups. In the case of the concurrency exception, you may just need to start the work over to ensure you’re starting from the most recent aggregate changes. I used globally applied policies here, but Jasper will also allow you to override that on a message type by message type basis.

Lastly, let’s add the Jasper outbox integration for Marten and opt into automatic event publishing with this bit of configuration chained to the standard AddMarten() usage:

builder.Services.AddMarten(opts =>
    // Marten configuration...
    // I added this to enroll Marten in the Jasper outbox
    // I also added this to opt into events being forward to
    // the Jasper outbox during SaveChangesAsync()

And that’s actually that. The configuration above will add the Jasper outbox tables to the Marten database for us, and let Marten’s database schema management manage those extra database objects.

Back to the command handler (mildly elided):

public class CompleteChartingHandler
    public ChartingFinished Handle(CompleteCharting charting, ProviderShift shift)
        // validation code goes here!

        return new ChartingFinished(charting.AppointmentId, shift.BoardId);

By opting into the outbox integration and event forwarding to Jasper from Marten, when this command handler is executed, the ChartingFinished events will be published — in this case just to an in-memory queue, but it could also be to an external transport — with Jasper’s outbox implementation that guarantees that the message will be delivered at least once as long as the database transaction to save the new event succeeds.

Conclusion and What’s Next?

There’s a tremendous amount of work in the rear window to get to the functionality that I demonstrated here, and a substantial amount of ambition in the future to drive this forward. I would love any possible feedback both positive and negative. Marten is a team effort, but Jasper’s been mostly my baby for the past 3-4 years, and I’d be happy for anybody who would want to get involved with that. I’m way behind in documentation for Jasper and somewhat for Marten, but that’s in flight.

My next couple posts to follow up on this are to:

  • Do a deeper dive into Jasper’s outbox and explain why it’s different and arguably more useful than the outbox implementations in other leading .NET tools
  • Introduce the usage of Rabbit MQ with Jasper for external messaging
  • Take a detour into the development and deployment time command line utilities built into Jasper & Marten through Oakton

2 thoughts on “A Vision for Low Ceremony CQRS with Event Sourcing

  1. Jasper provides an excellent productivity boost and might attract more people to try event sourcing. The built-in resiliency features look promising. Thank you for the blog post.

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