In this post, we will build a simple microservice using an Event Source architecture pattern. Previously, I discussed Event-Driven architecture. This post will be more elaborative on how one can build a microservice with this pattern. But before we do that, let’s look at some fundamentals.
Event Sourcing
Event sourcing is an append-only log of events. We store the events and also the context of those events. Every service will store the data as events.
Usually, the data is related to changes to the business/domain entity. Every change is captured as an event. The service stores the event in a database with all the required context. This allows rebuilding of the current state of the entity.
Auditing is one of the benefits of event sourcing. The key difference between audit logs and event sourcing is the context. In audit logs, there is no context of changes to entities. But, with event sourcing, context is part of the storage.
Event Store
Event Store is an event database. A system records each change to the domain in the database. Event store stores immutable events. Events are by nature immutable. We can rebuild the entity state using the event store.
To give an example – consider if you swipe a debit card to buy something and the money from your bank account is deducted.
In this scenario, a system will trigger an event CardSwiped. We will store the event CardSwiped with details like date, price, and merchant details. For any reason, if the transaction has to be reversed, the system will send another event instead of changing anything with the first event. Reversing of a transaction is itself an event. So, it will trigger CardTransactionReverse event.
In short, we did not change CardSwiped as an event in the database, but we changed the effect it caused.
Streams
Within the event store, the events for a domain live in an event stream. One can rebuild the state of the domain by reading all the events from a stream.
As the name goes, streams are incoming events. The sequence of events matters, especially if the state of the domain is going to change. A unique number or numeric value represents the position of the event.
Benefits of Event Sourcing
There are a number of benefits of using event sourcing. Here goes the list
- Auditing
- Asynchronous communication
- Fault tolerance
- Easier to rebuild the state
- Observability
- Service autonomy – If a service with event sourcing is down, dependent services can catch up when the service is back.
Microservice Example
In this example, we will look at when a customer orders for food delivery.
- Customer orders for food. Order service takes up the order and runs some validation before creating order.
- Order service will call Consumer service to verify consumer details.
- Order service will call Kitchen service to create food order ticket.
- Order service will call Accounts service for credit card authorization.
- If everything went successfully, order service will create an order.
For demo purposes, we won’t detail each piece of this example. I will show how an order service will create an order.
In event sourcing, each event is a domain event. To understand domain event better, you should check domain-driven design.
Domain Event
In event sourcing, we represent domain entity or aggregate with domain event. The usual approach to name an event is to use past-participle verb. Example – OrderCreated CreditCardAuthorized.
These domain events include information about the domain. It represents the state changes for the domain entity. It also includes Event id, timestamp, user information.
In our microservice example, we will be using number of domain events – OrderCreated, CreditCardAuthorized, OrderRejected, OrderShipped.
Whenever a consumer places an order to buy food, either the client will send a request for order. For managing orders, we have a microservice OrderService. OrderService can store the incoming order request as is in database. OrderService will need to inform KitchenService about the order, so it can prepare the food. In mean time, if we receive some update to original order, it will overwrite the details of initial order. We lose important state changes.
Now, comes the event sourcing.
With event sourcing, we can create domain events and these events track the state of domain. When a client sends initial request, the event OrderCreated tracks the order creation. Before order is getting ready for KitchenService , if a customer updates or cancels the order, we will have OrderUpdated OR OrderCanceled events.
We store each of these events in event store. Event store allows to create object by applying those events.
In many instances, aggregates can be tightly coupled. To avoid the tight coupling, each aggregate can publish a domain event while storing the event data in its store. This store acts as audit log as well as provides a capability to rebuild the state.
Order service will then publish the event OrderCreated through message broker. Various services like Kitchen service and Accounts Service will subscribe to the event. They will perform their work asynchronously. Kitchen service will then perform consumer verification and if successful, it will send ConsumerVerified event. Accounts Service will equally create CreditCardAtuhorized.
CQRS Pattern
When using event sourcing as architecture pattern, you will also use CQRS (command query responsibility segregation) pattern.
In traditional database application, we use CRUD operations to manage data. CQRS conceptually separates the model for update and display. Command acts for create, update and delete and Query acts for fetching the data from database.
In our example for Order Service, when a user orders for food delivery, client sends a request. We use request details to call command CreateOrder . Order repository uses this command to save order details. And then orderCreated event is emitted to event queue. Subscribed services consume this event to further processing.
Idempotency Handling
Every subscriber service has to implement idempotency for consuming the events. It is possible that publishing service publishes the event more than once. If the subscriber has already processed that event before, then subscriber should ensure to not change domain state if the event comes second time.
Usual solution is to pass a unique id in each event. Subscriber then stores the event id in database table ProcessedMessages as unique. If a subscriber consumes the event with the same id, there will be an error when storing that id in the database table.
Conclusion
In this post, I gave a detail account of event sourcing. Event sourcing is a great way to write micro services. Especially, it solves the problem for data consistency. Whenever a state of entity is changed, a new event is added to the list of events. It also helps in avoiding the object-relational impedance mismatch problem.