This example shows another variation of the Dog aggregate class used in the tutorial and module docs.
Similar to the previous example, the model is expressed in a functional style. In contrast to the previous example, this example uses Pydantic to define immutable aggregate and event classes, rather than defining them as Python frozen data classes. This has implications for the persistence layer.
The application class in this example uses its own persistence classes PydanticMapper and OrjsonTranscoder. Pydantic is responsible for converting domain model objects to object types that orjson can serialise, and for reconstructing model objects from JSON objects that have been deserialised by orjson.
One advantage of using Pydantic here is that any custom value objects will be automatically reconstructed without needing to define the transcoding classes that would be needed when using the library's default JSONTranscoder. This is demonstrated in the example below with the Trick class, which is used in both aggregate events and aggregate state, and which is reconstructed from serialised string values, representing only the name of the trick, from both recorded aggregate events and from recorded snapshots.
../../../eventsourcing/examples/aggregate7/domainmodel.py
../../../eventsourcing/examples/aggregate7/application.py
../../../eventsourcing/examples/aggregate7/persistence.py
../../../eventsourcing/examples/aggregate7/test_application.py