eventiq 0.1.1

Cloud native framework for building event driven applications in Python.

Cloud native framework for building event driven applications in Python

Note: This package is under active development and is not recommended for production use

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Version: 0.1.1

Documentation: https://performancemedia.github.io/eventiq/

Repository: https://github.com/performancemedia/eventiq

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About

The package utilizes pydantic, async_timeout and python-json-logger as the only required dependencies. For messages Cloud Events format is used. Service can be run as standalone processes, or included into starlette (e.g. FastAPI) applications.

Installation

pip install eventiq

Multiple broker support (in progress)

• Stub (in memory using asyncio.Queue for PoC, local development and testing)
• NATS (with JetStream)
• Redis Pub/Sub
• Kafka
• Rabbitmq
• Google Cloud PubSub
• And more comming

Optional Dependencies

• cli - click and aiorun
• broker of choice: nats, kafka, rabbitmq, redis, pubsub
• custom message serializers: msgpack, orjson
• prometheus - Metric exposure via PrometheusMiddleware

Motivation

Python has many "worker-queue" libraries and frameworks, such as: - Celery - Dramatiq - Huey - arq

However, those libraries don't provide a pub/sub pattern, useful for creating event driven and loosely coupled systems. Furthermore, the majority of those libraries do not support asyncio. This is why this project was born.

Basic usage

import asyncio
from eventiq import Service, CloudEvent, Middleware
from eventiq.backends.nats.broker import JetStreamBroker


class SendMessageMiddleware(Middleware):
    async def after_broker_connect(self, broker: "Broker") -> None:
        print(f"After service start, running with {broker}")
        await asyncio.sleep(10)
        for i in range(100):
            await broker.publish("test.topic", data={"counter": i})
        print("Published event(s)")

broker = JetStreamBroker(url="nats://localhost:4222")
broker.add_middleware(SendMessageMiddleware())

service = Service(name="example-service", broker=broker)

@service.subscribe("test.topic")
async def example_run(message: CloudEvent):
    print(f"Received Message {message.id} with data: {message.data}")


if __name__ == "__main__":
    service.run()

Scaling

Each message is load-balanced (depending on broker) between all service instances with the same name. To scale number of processes you can use containers (docker/k8s), supervisor, or web server like gunicorn.

TODOS:

• More tests
  • Integration tests with docker-compose and all backends
• Docs + tutorials
• OpenTelemetry Middleware (?)