eventsourcing-eventstoredb 1.2.0

Python package for eventsourcing with EventStoreDB and KurrentDB

Please note: following the rebranding of EventStoreDB to KurrentDB, this package has been rebranded as eventsourcing-kurrentdb. Please migrate your code to use the eventsourcing-kurrentdb package when you are ready.

Event Sourcing in Python with KurrentDB

This is an extension package for the Python eventsourcing library that provides a persistence module for EventStoreDB and KurrentDB. It uses the kurrentdbclient package to communicate with EventStoreDB and KurrentDB via the gRPC interface.

Installation

Use pip to install the stable distribution from the Python Package Index.

$ pip install eventsourcing-eventstoredb

Please note, it is recommended to install Python packages into a Python virtual environment.

Getting started

Define aggregates and applications in the usual way. Please note, "streams" in EventStoreDB and KurrentDB are constrained to start from position 0, and this package expects the originator_version of the first event in an aggregate sequence to be 0, so you must set INITIAL_VERSION on your aggregate classes to 0.

from __future__ import annotations

from uuid import uuid5, NAMESPACE_URL
from typing import List, TypedDict, Tuple

from eventsourcing.application import Application
from eventsourcing.domain import Aggregate, event


class TrainingSchool(Application):
    def register(self, name: str) -> int:
        dog = Dog(name)
        recordings = self.save(dog)
        return recordings[-1].notification.id

    def add_trick(self, name: str, trick: str) -> int:
        dog = self._get_dog(name)
        dog.add_trick(trick)
        recordings = self.save(dog)
        return recordings[-1].notification.id

    def get_dog_details(self, name: str) -> DogDetails:
        dog = self._get_dog(name)
        return {'name': dog.name, 'tricks': tuple(dog.tricks)}

    def _get_dog(self, name: str) -> Dog:
        return self.repository.get(Dog.create_id(name))



class Dog(Aggregate):
    INITIAL_VERSION = 0  # for EventStoreDB and KurrentDB

    @staticmethod
    def create_id(name: str):
        return uuid5(NAMESPACE_URL, f"/dogs/{name}")

    @event('Registered')
    def __init__(self, name):
        self.name = name
        self.tricks: List[str] = []

    @event('TrickAdded')
    def add_trick(self, trick):
        self.tricks.append(trick)


class DogDetails(TypedDict):
    name: str
    tricks: Tuple[str, ...]

Configure the TrainingSchool application to use EventStoreDB or KurrentDB by setting the environment variable PERSISTENCE_MODULE to 'eventsourcing_eventstoredb'. You can do this in actual environment variables, or by passing in an env argument when constructing the application object, or by setting env on the application class.

import os

os.environ['TRAININGSCHOOL_PERSISTENCE_MODULE'] = 'eventsourcing_eventstoredb'

Also set environment variable EVENTSTOREDB_URI to an EventStoreDB or KurrentDB connection string URI. This value will be used as the uri argument when the KurrentDBClient class is constructed by this package.

os.environ['EVENTSTOREDB_URI'] = 'esdb://localhost:2113?Tls=false'

If you are connecting to a "secure" EventStoreDB or KurrentDB server, unless the root certificate of the certificate authority used to generate the server's certificate is installed locally, then also set environment variable EVENTSTOREDB_ROOT_CERTIFICATES to an SSL/TLS certificate suitable for making a secure gRPC connection to the EventStoreDB server(s). This value will be used as the root_certificates argument when the KurrentDBClient class is constructed by this package.

os.environ['EVENTSTOREDB_ROOT_CERTIFICATES'] = '<PEM encoded SSL/TLS root certificates>'

Please refer to the kurrentdbclient documentation for details about starting a "secure" or "insecure" EventStoreDB server, and the "esdb" and "esdb+discover" EventStoreDB connection string URI schemes, and how to obtain a suitable SSL/TLS certificate for use in the client when connecting to a "secure" EventStoreDB server.

Construct the application.

training_school = TrainingSchool()

Call application methods from tests and user interfaces.

training_school.register('Fido')
training_school.add_trick('Fido', 'roll over')
training_school.add_trick('Fido', 'play dead')
dog_details = training_school.get_dog_details('Fido')
assert dog_details['name'] == 'Fido'
assert dog_details['tricks'] == ('roll over', 'play dead')

To see the events have been saved, we can reconstruct the application and get Fido's details again.

training_school = TrainingSchool()

dog_details = training_school.get_dog_details('Fido')

assert dog_details['name'] == 'Fido'
assert dog_details['tricks'] == ('roll over', 'play dead')

Eventually-consistent materialised views

To project the state of an event-sourced application "write model" into a materialised view "read model", first define an interface for the materialised view using the TrackingRecorder class from the eventsourcing library.

The example below defines methods to count dogs and tricks for the TrainingSchool application

from abc import abstractmethod
from eventsourcing.persistence import Tracking, TrackingRecorder

class MaterialisedViewInterface(TrackingRecorder):
    @abstractmethod
    def incr_dog_counter(self, tracking: Tracking) -> None:
        pass

    @abstractmethod
    def incr_trick_counter(self, tracking: Tracking) -> None:
        pass

    @abstractmethod
    def get_dog_counter(self) -> int:
        pass

    @abstractmethod
    def get_trick_counter(self) -> int:
        pass

The MaterialisedViewInterface can be implemented as a concrete view class using a durable database such as PostgreSQL.

The example below counts dogs and tricks in memory, using "plain old Python objects".

from eventsourcing.popo import POPOTrackingRecorder

class InMemoryMaterialiseView(POPOTrackingRecorder, MaterialisedViewInterface):
    def __init__(self):
        super().__init__()
        self._dog_counter = 0
        self._trick_counter = 0

    def incr_dog_counter(self, tracking: Tracking) -> None:
        with self._database_lock:
            self._assert_tracking_uniqueness(tracking)
            self._insert_tracking(tracking)
            self._dog_counter += 1

    def incr_trick_counter(self, tracking: Tracking) -> None:
        with self._database_lock:
            self._assert_tracking_uniqueness(tracking)
            self._insert_tracking(tracking)
            self._trick_counter += 1

    def get_dog_counter(self) -> int:
        return self._dog_counter

    def get_trick_counter(self) -> int:
        return self._trick_counter

Define how events will be processed using the Projection class from the eventsourcing library.

The example below processes Dog events. The Dog.Registered events are processed by calling incr_dog_counter() on the materialised view. The Dog.TrickAdded events are processed by calling incr_trick_counter().

from eventsourcing.domain import DomainEventProtocol
from eventsourcing.dispatch import singledispatchmethod
from eventsourcing.projection import Projection
from eventsourcing.utils import get_topic


class CountProjection(Projection[MaterialisedViewInterface]):
    topics = (
        get_topic(Dog.Registered),
        get_topic(Dog.TrickAdded),
    )

    @singledispatchmethod
    def process_event(self, event: DomainEventProtocol, tracking: Tracking) -> None:
        pass

    @process_event.register
    def dog_registered(self, event: Dog.Registered, tracking: Tracking) -> None:
        self.view.incr_dog_counter(tracking)

    @process_event.register
    def trick_added(self, event: Dog.TrickAdded, tracking: Tracking) -> None:
        self.view.incr_trick_counter(tracking)

Run the projection with the ProjectionRunner class from the eventsourcing library.

The example below shows that when the projection is run, the materialised view is updated by processing the event of the upstream event-sourced TrainingSchool application. It also shows that when tricks are subsequently added to the application's aggregates, events continue to be processed, such that the trick counter is incremented in the downstream materialised view "read model".

import os
from eventsourcing.projection import ProjectionRunner

with ProjectionRunner(
    application_class=TrainingSchool,
    projection_class=CountProjection,
    view_class=InMemoryMaterialiseView,
) as runner:

    # Get "read model" instance from runner, because
    # state of materialised view is stored in memory.
    materialised_view = runner.projection.view

    # Wait for the existing events to be processed.
    materialised_view.wait(
        application_name=training_school.name,
        notification_id=training_school.recorder.max_notification_id(),
    )

    # Query the "read model".
    dog_count = materialised_view.get_dog_counter()
    trick_count = materialised_view.get_trick_counter()

    # Record another event in "write model".
    notification_id = training_school.add_trick('Fido', 'sit and stay')

    # Wait for the new event to be processed.
    materialised_view.wait(
        application_name=training_school.name,
        notification_id=notification_id,
    )

    # Expect one trick more, same number of dogs.
    assert dog_count == materialised_view.get_dog_counter()
    assert trick_count + 1 == materialised_view.get_trick_counter()

    # Write another event.
    notification_id = training_school.add_trick('Fido', 'jump hoop')

    # Wait for the new event to be processed.
    materialised_view.wait(
        training_school.name,
        notification_id,
    )

    # Expect two tricks more, same number of dogs.
    assert dog_count == materialised_view.get_dog_counter()
    assert trick_count + 2 == materialised_view.get_trick_counter()

See the Python eventsourcing package documentation for more information about projecting the state of an event-sourced application into materialised views that use a durable database such as SQLite and PostgreSQL.

More information

For more information, please refer to the Python eventsourcing library, the Python kurrentdbclient package, and the KurrentDB website.

Contributors

Install Poetry

The first thing is to check you have Poetry installed.

$ poetry --version

If you don't, then please install Poetry.

It will help to make sure Poetry's bin directory is in your PATH environment variable.

But in any case, make sure you know the path to the poetry executable. The Poetry installer tells you where it has been installed, and how to configure your shell.

Please refer to the Poetry docs for guidance on using Poetry.

Setup for PyCharm users

You can easily obtain the project files using PyCharm (menu "Git > Clone..."). PyCharm will then usually prompt you to open the project.

Open the project in a new window. PyCharm will then usually prompt you to create a new virtual environment.

Create a new Poetry virtual environment for the project. If PyCharm doesn't already know where your poetry executable is, then set the path to your poetry executable in the "New Poetry Environment" form input field labelled "Poetry executable". In the "New Poetry Environment" form, you will also have the opportunity to select which Python executable will be used by the virtual environment.

PyCharm will then create a new Poetry virtual environment for your project, using a particular version of Python, and also install into this virtual environment the project's package dependencies according to the pyproject.toml file, or the poetry.lock file if that exists in the project files.

You can add different Poetry environments for different Python versions, and switch between them using the "Python Interpreter" settings of PyCharm. If you want to use a version of Python that isn't installed, either use your favourite package manager, or install Python by downloading an installer for recent versions of Python directly from the Python website.

Once project dependencies have been installed, you should be able to run tests from within PyCharm (right-click on the tests folder and select the 'Run' option).

Because of a conflict between pytest and PyCharm's debugger and the coverage tool, you may need to add --no-cov as an option to the test runner template. Alternatively, just use the Python Standard Library's unittest module.

You should also be able to open a terminal window in PyCharm, and run the project's Makefile commands from the command line (see below).

Setup from command line

Obtain the project files, using Git or suitable alternative.

In a terminal application, change your current working directory to the root folder of the project files. There should be a Makefile in this folder.

Use the Makefile to create a new Poetry virtual environment for the project and install the project's package dependencies into it, using the following command.

$ make install

Please note, if you create the virtual environment in this way, and then try to open the project in PyCharm and configure the project to use this virtual environment as an "Existing Poetry Environment", PyCharm sometimes has some issues (don't know why) which might be problematic. If you encounter such issues, you can resolve these issues by deleting the virtual environment and creating the Poetry virtual environment using PyCharm (see above).

Project Makefile commands

You can start EventStoreDB using the following command.

$ make start-eventstoredb

You can run tests using the following command (needs EventStoreDB to be running).

$ make test

You can stop EventStoreDB using the following command.

$ make stop-eventstoredb

You can check the formatting of the code using the following command.

$ make lint

You can reformat the code using the following command.

$ make fmt

Tests belong in ./tests. Code-under-test belongs in ./eventsourcing_eventstoredb.

Edit package dependencies in pyproject.toml. Update poetry.lock and installed packages using the following command.

$ make update