The document discusses various techniques for evolving event sourced systems to handle changes over time, as immutable event streams must be adapted to new requirements. Upcasters can modify event representations during loading to rename fields, add data, or change event types, while maintaining immutable event storage. Migrating to a new "version 2.0" event stream allows restructuring events while rebuilding systems. Editing stored events is discouraged except under rare circumstances like data regulations, as the preferred approach is adding corrective events or redesigning read models, events, and aggregates over time through incremental changes.

1. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Jan-Hendrik Kuperus

2. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
State of the universe
• age: 7.252.754.400.000.000.000 minutes
• width: 554.000.000.000 lightyears
• stars:
10.000.000.000.000.000.000.000.000
• birthRate: 190.000 stars / minute
Photo by Guillermo Ferla on Unsplash

3. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
State of the universe
• age: 7.252.754.400.000.000.001 minutes
• width: 554.000.000.006 lightyears
• stars:
10.000.000.000.000.000.000.000.000
• birthRate: 190.000 stars / minute
• deathRate: 190.000 stars / minute
Photo by Guillermo Ferla on Unsplash

4. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
What just happened?
Photo by Guillermo Ferla on Unsplash
state
t[0]
state
t[1]
time passes

5. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
What just happened?
Photo by Guillermo Ferla on Unsplash
state
t[0]
state
t[1]
time passes

6. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Image credit: Particle Data Group at Lawrence Berkeley National
[910] StarBirthEvent
...
[867] PlanetLostLifeEvent
...
[1] ParticleFormedEvent
[0] BigBangEvent
...
[593] PlanetSparkedLifeEvent
[911] PlanetSparkedLifeEvent
...
[???] JFall2021Event

7. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Traditional State Sourcing (CRUD)
• Stores current state of system
• Constant read-time complexity
• Impossible to determine reason for change
• Impossible to analyze origin of data
Event Sourcing
• Stores changes / events
• Increasing read-time complexity
• All intents of changes visible
• Analysis of data origin

8. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Traditional State Sourcing (CRUD) Event Sourcing
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1 copy of Domain-Driven Design by Eric Evans
Added Domain-Driven Design by Eric Evans
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by Vaughn Vernon
Added Domain-Driven Design Distilled by
Vaughn Vernon
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Vaughn Vernon
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Design by Vaughn Vernon

9. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Typical Architecture
• User interacts with Command Handler (CH)
• CH validates state and emits Event
• Event stored in Event Stream
• Event is read by Event Processor
• Event Processor updates Read Model
• User reads from Read Model
CQRS

10. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Common Terms in Event Sourcing
Aggregate Set of entities that share consistency rules. Emits events to
indicate changes. Is rebuilt from its stored events.
Event Unit of change in an Aggregate. Consists of a type/name,
timestamp, payload, metadata and associated Aggregate.
Command Expresses and intent to performa a change within an Aggregate.
Cause Aggregate reconstruction, validation and event emission.

11. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
The Event Stream
• The Event Stream maintains all history
– Aggregates contain subplots
• Practically Immutable
– Time Travel wreaks havoc
• You should never change past events
– But, what if … ?
Image credit: Particle Data Group at Lawrence Berkeley National

12. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Types of changes
• Read Model Changes
– New Structure
– Derived Data
• Event-based Changes
– New Data
– Renaming Fields
– Filtering Events
– Splitting Events
– Corrective Events
• Drastic Changes
– Editing Events
– Migrating Event Streams

13. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Changing a Read Model
• Read Model Changes are "easy"
– Delete & Start over
– Build & Switch
– Corrective Processor
• When ?
– A bug in a projection
– Adding derived data (scale conversion)
– Changes in Read API

14. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Rebuilding a Read Model
• Stop Event Processor
• Delete the Read Model
• Delete the state of Event Processor
• Restart Event Processor
• ... wait for it
• ... wait for it
• Profit!

15. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Build & Switch
• Build a second Event Processor
• Add second Read Model database
• Start the Event Processor
• Both projections are kept up to date
• Seamless jump from old to new
• Old is a backup-plan
• When?
– Structural Changes in Read Model

16. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Corrective Processor
• Fix the original Event Processor
• Build a second Event Processor
• Target the existing Read Model
• Start the second Event Processor
• Projection is corrected while online
• When?
– Adding data
– Fixing Projection Bugs

17. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Designing Events for Evolution
• Events live forever!
• Careful design pays off
• Start small, capture change in new events
• Event Hierarchies are a big help
A useful system must evolve continually, or it
becomes less useful over time
- Meir Lehman

18. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Event Hierarchies
• Allow grouping of events
• Allows for easy “fixing events”
• Allows for adding similar events easily
interface UserDetailsEvent {}
class AppliedUnicodePatchJIRA123Event
implements UserDetailsEvent {}
@EventHandler
public void onUserEvent(UserEvent event) {
...
}

19. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Changing Events
• Events in storage never change
• Axon offers Upcasters
– Modify event during Aggregate Loading
– Chained together
– Driven by Event Revisions
@Revision("1.0")
class User {
String userId;
}

20. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Adding Data to Events
• Natural progression of application
• SingleEventUpcaster
• Provide default values
• Mind the order of upcasters!
@Revision("1.0")
class User {
String userId;
}
@Revision("2.0")
class User {
String userId;
String name;
}

21. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
How do upcasters work?
• Axon loads events into
IntermediateEventRepresentation
• Upcasters are asked to do their thing
• Final intermediate representation is then
deserialised
document -> {
document.getRootElement()
.addElement("name")
.setText("Not entered");
return document;
}

22. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Renaming fields
• Typically fixing typo's or refactorings
document -> {
document.getRootElement()
.selectSingleNode("name")
.setName("username");
return document;
}

23. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Renaming Events
• Event name should reflect Domain action
• Can be conditional
• Use EventTypeUpcaster

24. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Filtering Events
• Effectively "deletes" events from a stream
• Useful for reducing clutter in Aggregates
– After "moving" behaviour away
• Impractical for corrections

25. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Splitting Events
• Allows to reduce the scope of an Event
• Free to split into any number of Events
• Can help reduce Event Handler size
• Uses SingleEntryMultiUpcaster
• Returns a Stream of new events
class UserDetailsUpdatedEvent {
String newName;
String newAddress;
String newPhone;
String newEmail;
String newSelfie;
}

26. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Corrective Events
• Indicate a manually corrected situation
• A bug caused Email Addresses to be null
• Extend an existing Event
• Make the name recognizable as a correction
• May need a temporary Command
• Temporary means Temporary!
class UserEmailCorrectedByJIRA1234Event
extends UserEmailUpdatedEvent {
String newEmail;
}

27. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Drastic Changes

28. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Editing Events
• Last resort, consider upcasting / filtering
• Regulations
– GDPR
– Secrets
• Shut down the application
• Backup the database
• Edit the database
• Restart the application

29. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Migrating Event Streams
• When applications change drastically
• Chance to consolidate Events / Aggregates
• Restructure Events
• Practically building your 2.0
• It will take time
– System 2.0 can run in "shadow mode"
• Useful when upcasters have stacked
• Useful for splitting applications
• Useful for merging applications

30. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Summary
• Event Streams are immutable
– But reality changes constantly
• Read Models are easy to change
• Upcasters can modify Event-representations
• Corrective Events show mea culpa
• Database editing as last resort
• Event Stream migration for next version

31. Jan-Hendrik Kuperus Evolution in Event Sourced Systems
Thank you!
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