Category: Event Sourcing

Separating data and behaviors can have a pretty profound impact on your design. An Event Sourced Aggregate allows you to focus on business logic by having capabilities to produce data (Events). Event Sourcing does exactly this by limiting the amount of state you require only to that needed for business logic within an Aggregate.

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Shipment

Before jumping in code, the example I’m going to use is of a shipment. The simple setup is you have a shipment that has two stops. The first stop is the Pickup and the last stop is the Delivery. You could think of this as a food delivery service where the shipment is of your food. A delivery driver goes to the Pickup Stop, which is the restaurant that you’re ordering from, and the delivery stop is your house.

Each stop will go through a transition in its status. At first, a stop will be “In Transit” and then will progress to “Arrived” once the delivery driver has reached the stop. Once the driver leaves the stop and heads to the next stop, the status will change to “Departed”. Once each stop goes through its transition, the shipment is completed.

Current State

Here’s an example using only the current state without event sourcing. The state is represented by properties on the ShipmentAggregateRoot and the list of Stops.

The primary reason we have this aggregate is to control the invariants and create a consistency boundary. Check out my post Using Invariants as a Guide when designing aggregates. In this case, we have a few invariants but the one I want to point out is in the Arrive() method. We need to check that all other previous stops have Departed and gone through their full progression before we can call Arrive on the next stop.

Event Sourced Aggregate

The first step in moving our aggregate towards event sourcing is actually creating events.

The next step is defining the state that we need in our aggregate. This is the key point of this post. The state we need is only for our invariants (business logic) in the aggregate. We do not need any other data in our state other than what we use for business logic.

Finally, here is the new Aggregate that is using the defined Events and the ShipmentState.

If you compare the Event Sourced version with the previous version, you will notice that we’re sorely focused on the behavior methods and the business logic. The projection/state we’re recording is much slimmer as it’s now recorded separately in the events.

Our tests are incredibly simple as we don’t really need to build up a large data model for our aggregate.

Source Code

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Related Posts

• Event Sourcing Example & Explained in plain English
• Aggregate Design: Using Invariants as a Guide
• Projections in Event Sourcing: Build ANY model you want!
• Testing your Domain when Event Sourcing
• Snapshots in Event Sourcing for Rehydrating Aggregates

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How do you test your domain when Event Sourcing? I find testing aggregates with Event Sourcing to be simpler than testing if you’re storing the current state. The inputs to your aggregate are events and the output of your aggregate are events.

Given a stream of events
When a valid command is performed
Then new event(s) occurs

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Event Sourcing

I’ve covered Event Sourcing in my Event Sourcing Example & Explained in plain English post. So check that out if you need a more detailed primer.

Generally, I use Aggregates as a way to model my domain. The aggregate is what exposes commands and if those commands are called/invoked will result in creating new events. This general concept is how testing in event sourcing becomes simple.

In order to use an aggregate, you first need to pull all the existing events from the Event Store, replay them within the Aggregate to get to the current state, then return the Aggregate to the caller. To do this, I generally use a Repository that will do this work and build the Aggregate.

To illustrate this, we have client code that is using the repository to get the Aggregate. The repository calls the Event Store to get the existing Events (if any) for this specific aggregate.

At this point the Repository will create an empty aggregate and replay all the events it received from the Event Store.

Once it has rebuilt the aggregate, it then returns it back to the Client. The client at this point will likely call various methods/commands on the aggregate.

This was the first stage of the process. I call this the rehydrating stage. You’re rehydrating your aggregate back to its current state with all the past events. Remember this first stage as the “Given” as we start testing in event sourcing.

Creating Events

The rest of the example is using a Warehouse Product, which is the concept of a product that is in a warehouse.

Now that the client code has an aggregate, it will likely perform one or more commands on the aggregate.

If the client calls ShipProduct() command on the aggregate, and the aggregate is in a valid state to do so and passes all invariants, then it will create a new event that it will keep internally within it.

If the client code then called another command, another event would be appended to the internal list.

This is the second stage of the process where we’ve created new events which are the result of calling commands on our aggregate. Remember this second stage as the “When” stage of testing with event sourcing.

Saving Events

The last step is taking the newly created events that are in the aggregate and persisting those to the event store.

This means the client code will call back to our Repository passing it the Aggreagate.

The repository will get the new events and append those to the Event Store for that specific aggregates event stream.

Remember this stage as the “Then” in our tests when Event Sourcing.

Given, When, Then

If you take that basic 3 steps of loading an aggregate, calling commands, saving the new events, you can boil that down to:

Given a stream of events
When a valid command is performed
Then new event(s) occurs

You can use this as the testing strategy for testing your Aggregates.

The WarehouseProduct above has 3 commands: ShipProduct, ReceiveProduct, and AdjustInventory. All of which result in creating their respective events if they passed any invariants.

To illustrate this Given, When, Then for the ShipProduct command, which should create a ProductShipped Event.

While that does satisfy our goal, it’s a bit cumbersome to have to write this to verify the event. To simply and make this a bit more natural to read, I created a base class to use within our tests.

Now using this abstract class, here are all the tests for the WarehouseProduct.

Source Code

Developer-level members of my CodeOpinion YouTube channel get access to the full source for any working demo application that I post on my blog or YouTube. Check out the membership for more info.

Related Posts

• Event Sourcing Example & Explained in plain English
• Projections in Event Sourcing: Build ANY model you want!
• Snapshots in Event Sourcing for Rehydrating Aggregates

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Once you understand how Event Sourcing works, the most common thought is: “What happens when you have a lot of Events? Won’t it be inefficient to fetch every event from the event stream and replay all of them to get to the current state?”. It might be. But to combat this, you can use snapshots in event sourcing to rehydrate aggregates. Snapshots give you a representation of your aggregates state at a point in time. You can then use this as a checkpoint and then only replay the events since the snapshot.

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Check out my YouTube channel where I post all kinds of content that accompanies my posts including this video showing everything that is in this post on using Snapshots in Event Sourcing.

https://www.youtube.com/watch?v=eAIkomEid1Y

Large Event Streams

I’d guess in most situations, you won’t have a stream with a lot of events because you’ll have a lifecycle to the stream. Meaning an event stream usually has a life where it being and ends. For example, an Order as a lifecycle from when from the OrderPlaced to maybe an OrderShipped. After the order has been shipped, there likely are no more events for that specific order that will occur. However, you could have a stream that is opened ended or possibly has a really long life, such as maybe a product in a warehouse. That event stream could be long-lived as long as you keep selling and having a given product in the warehouse. In these situations, you may have thousands or millions of events depending on the context.

When you have a lot of events, the challenge is you generally will fetch from the Event Store all the events from the very beginning of the stream, and then replay them in your aggregate to build up to the current state. This could take an undesirable amount of time if you have to fetch and replay a significant amount of events.

Snapshots in Event Sourcing

Snapshots are a way of solving this issue by recording the state of an aggregate at a point in time. You then store this state in a separate event stream. Along with the state, you record the version of the last event you processed that represents this state.

For illustration, you can see that the snapshot has our current state, which we’re recording the total quantity on hand. It also captures version (3) which is the last event in our event stream. For my example, I’m creating a snapshot on every 4th event. In reality, at what interval you create a snapshot is totally dependent on your situation. This could be hundreds.

Once more events are added to our event stream, we create new snapshots of the state and keep track of the version they pertain to.

Rehydrating Aggregate

Now instead of replaying all the events from the beginning of the event stream, what we do is first look at the snapshot stream and see if there are any events. But you do so reading the stream backward! So in the example above, we would get the last snapshot in the stream, which has a State with the Qty: 87 and the Version = 11. We will pass the state into our aggregate, then we will query the event stream and start at Version 12. In my example above, it would then retrieve 3 events from that point, which would be replayed in our aggregate.

Here is some example of code from a Repository that is rehydrating an aggregate. That repository is using EventStoreDB as the EventStore.

Before you go down this road, make sure you absolutely need snapshots. They might not be required. Or perhaps you need to implement them for a specific type of event stream that is more open-ended. Keep track of metrics related to how long it takes to replay your aggregates, how many events typically in a stream, then decide if it’s worth adding.

Source Code

Developer-level members of my CodeOpinion YouTube channel get access to the full source for the working demo application available in a git repo. Check out the membership for more info.

Additional Related Posts

• Event Sourcing Example & Explained in plain English
• Projections in Event Sourcing: Build ANY model you want!

Follow @CodeOpinion on Twitter

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