Version events allow you to evolve your data schema without causing downtime or errors for downstream consumers.
Let’s see this in action. Imagine a UserCreated event that initially only contains user_id and email.
{
"event_type": "UserCreated",
"payload": {
"user_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
"email": "jane.doe@example.com"
},
"version": 1,
"timestamp": "2023-10-27T10:00:00Z"
}
Later, we want to add a display_name field. If we just added it to the existing event structure, older consumers not expecting this new field might break.
This is where versioning comes in. We can introduce a new version of the event:
{
"event_type": "UserCreated",
"payload": {
"user_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
"email": "jane.doe@example.com",
"display_name": "Jane Doe"
},
"version": 2,
"timestamp": "2023-10-27T10:05:00Z"
}
Consumers can now inspect the version field. If they receive version 1, they know to only expect user_id and email. If they receive version 2, they know display_name is also available. This allows them to gracefully handle both old and new event formats.
The core problem this solves is schema evolution in an event-driven architecture. As your application grows and requirements change, the data your events carry needs to evolve. Without a strategy like versioning, introducing new fields or changing existing ones can lead to runtime errors for consumers who haven’t updated their code to match the new schema. Consumers might encounter null values where they expect data, or worse, undefined properties leading to crashes.
Internally, this works by treating each distinct schema for an event type as a new version. The version field acts as a discriminator. When a producer emits an event, it includes the current schema version. Consumers, upon receiving an event, check this version. Based on the version, they can dynamically adjust their parsing logic. For example, a consumer might have a switch statement or a series of if/else if blocks checking event.version.
def process_user_created_event(event):
if event["version"] == 1:
user_id = event["payload"]["user_id"]
email = event["payload"]["email"]
# Process user with only ID and email
print(f"Processing V1 User: {user_id}, Email: {email}")
elif event["version"] == 2:
user_id = event["payload"]["user_id"]
email = event["payload"]["email"]
display_name = event["payload"]["display_name"] # Safely access new field
# Process user with ID, email, and display name
print(f"Processing V2 User: {user_id}, Email: {email}, Display Name: {display_name}")
else:
print(f"Unknown event version: {event['version']}")
The exact levers you control are the event_type and the version number. You decide when to increment the version and what fields to add or modify in the payload for that new version. It’s crucial to establish a clear convention for versioning – typically, incrementing the version by one for each backward-incompatible change. Backward compatibility means that older consumers can still process newer events without crashing, even if they don’t understand the new fields.
One common pattern that often gets overlooked in practice is how to handle removing or changing the meaning of fields. While adding fields is straightforward with versioning, if you need to remove a field that older consumers rely on, you can’t simply delete it from a new version. Instead, you’d typically introduce a new event type entirely, or deprecate the old event type and signal to consumers that they should migrate to the new one over time. For example, if email was to be replaced by primary_contact_method with a type: email object, you’d likely create a UserContactUpdated event with version 1 and have consumers migrate to listening to that new event, while the UserCreated event (version 1 or 2) might still include the old email field for a transitional period.
The next concept to explore is how to manage schema evolution that involves breaking changes, such as renaming fields or changing data types, and the strategies for migrating consumers gracefully.