Events Driven Architecture


Principle #1: Design Events for the Right Level of Granularity

When designing your events, think about the burden you are placing on subscribers. If you design very fine-grained events — such as an event for every changed field within an API request — subscribers will be required to aggregate and process multiple events to get their desired results.

One recommendation is to start with a single event per action (e.g. created, updated, deleted, submitted, approved), per business event (e.g. savings account opened), or per external system event (e.g. alert received). This will allow your subscriber to easily determine what events are of interest and what events should be ignored.

During the initial design of an event, it may seem appropriate to flatten out the schema for your event payload, e.g.:


Over time, events may require additional details to be included, resulting in a very large structure that may confuse or overwhelm subscribers. In our example above, what if we need to add a mailing address alongside the billing address?

Instead of adding new fields with a mailingAddress prefix (e.g. mailingAddressLine1, mailingAddressLIne2,…), consider grouping related data together at the initial design phase to offer more evolvability. For example:


We can now add shippingAddress as a new grouping within our event payload, while allowing subscribers to easily process the address(es) they are interested in. As a bonus, the subscriber can optimize their code by defining an address data structure or object that can be used to process billing and mailing addresses, along with any additional addresses necessary in the future.

Principle #3: Don’t Break Your Event Subscribers

Like APIs, events are contracts between an event producer and its subscribers. The needs of your subscribers will likely change over time, requiring modifications to your event payload schema. Apply the following rules to help ensure you don’t break your event subscribers:

  1. It is acceptable to add new fields to a payload without breaking subscribers. Use the Principle #2 (above) to group related data.
  2. Do not rename existing field names. Instead, add the new field alongside the previous one to correct any naming errors to provide better clarity to subscribers.
  3. Do not delete fields, as your subscribers may be dependent on their existence.

If you cannot apply the rules above, then you likely need to define a new event on a new topic to address the need. Over time, you can attempt to guide subscribers to move to the new topic and remove the deprecated one. This, however, can be a big challenge so be prepared to support both for some time.

For this to work we have to add two rules;

  • we are not allowed to rename anything, and
  • we may not change the semantic meaning of a property.

Principle #4: Manage Your Event Dependencies

It is important to understand the kind of dependencies you are creating between event producers and subscribers. Evolvable event streams require producers to be unaware of their subscribers.

Events should emit information that any subscriber may need in a format that is understandable without intimate knowledge of the producing system. When events emit information for a specific, known subscriber, it results in tight coupling that produces fragile software.

This principle not only extends to the producer’s awareness of its downstream subscribers, but also to the event payload. Poor event payload design requires event producers to understand and translate internal data values, that may be in an internal format, into a more useful value that the subscriber can easily process.

For example, an event payload that has a field named customerStatus may internally store the values of N,A,I. While your team may know that these values stand for new, active, and inactive, not everyone will be aware of their meaning. Additionally, downstream subscribers shouldn’t be required to lookup those codes in some documentation. Instead, the customerStatus field should contain one of new, active, and inactive to clearly describe the status. While this requires a little extra development effort, subscribers are able to easily understand and process the event without extra heavy lifting or intimate knowledge of how your internal data is stored.

Principle #5: Differentiate Between Private and Shared Event Streams

Not all events should be considered public and available to other teams. Some event streams, particularly those that support microservice communication and coordination, should remain private.

For example, background job progress events might be used within your system to keep other services updated on the internal progress of a background job. However, subscribers are only interested when a job has been completed or exited as a result of an error.

When designing event streams to support your APIs and microservices, share only the events necessary for downstream subscribers. This will allow your internal microservices to evolve independently from your subscribers. In some cases, it is often best to start with all private event streams, then migrate some of your private events to public events as you better understand stakeholder needs.

Versioning of Events in Event Sourced Systems

A challenge with event sourced systems is that events put in the event store years ago must be readable today, even though the software has gone through numerous changes. There are downstream consumers like projections and other systems that must be able to handle old as well as new versions of events.

If a system can be taken down, updated and brought back up, versioning of events is relatively simple. The the real challenge comes when a system can’t be taken down and you have two versions of the software running at the same time.

A general rule when versioning events is that adding things does not cause a versioning conflict. Adding a new version of an event is therefore not a problem, as long as we don’t break the definition of a new version event; it must be convertible from an old version of the same event. If this is not possible, then it’s a new event. Following these rules, when old versions are read from the store, they can first be converted, or upcasted, to the latest version before handled. This means that an event handler only needs to know how to deal with the latest version, an important benefit if many new versions have been created. To be able to convert an old version of an event when a property has been added to the new version, we use the same principle as when we add a new not nullable column to a database; we use a default value.

With events based on a type system, we are using a strong schema. With that we will not get any support from the serializer; it can only read events that exactly match the type or schema. Young notes that this approach only works if you can take the system down and upgrade it, otherwise consumers may read new versions of events that they don’t know about and are unable to deserialize them. He therefore generally recommends against this approach.

Using JSON or XML as a format for describing an event we have a weak schema, and instead of deserializing we map from JSON data to an event object. If a property has the same name in the two we copy the value into the event. If something in the JSON data is missing in the event we just leave the corresponding value. If a property in the event is not found in the JSON data, it will get a default value. For this to work we have to add two rules; we are not allowed to rename anything, and we may not change the semantic meaning of a property. One advantage of this technique is that we don’t need to create new versions of events; we only need the latest versions. For Young this technique is much preferable since now an old version of the software is able to read a new event.

Another option, related to a weak schema, is a hybrid schema where things an event needs to make sense, like the identity of the order for an event related to that order, are required and the rest are optional.

Other more complex kinds of problems include events that shouldn’t have been published and situations when you find out that the aggregate boundaries are wrong. Updating an existing event can cause large problems and Young strongly argues against this. Instead, he prefers using streams for manipulation, one example being transform streams. During the release process, you can make any transformation that is needed by reading from one stream, make a transformation and write to a new stream. The old stream can afterwards be deleted. Other examples include joining and splitting streams.

References / Acknowledgements