eventiq 0.1.11

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: 0.1.11

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

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

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About

The package utilizes pydantic 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 coming

Optional Dependencies

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

Motivation

• 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.

Features

• Modern, asyncio based python 3.8+ syntax
• Minimal dependencies, only pydantic and python-json-logger are required
• Automatic message parsing based on type annotations (like FastAPI)
• Code hot-reload
• Highly scalable: each service can process hundreds of tasks concurrently, all messages are load balanced between all instances by default
• Resilient - at least once delivery for all messages by default
• Customizable & pluggable message encoders (json, msgpack, custom)
• Json formatted logger
• Multiple broker support (Nats, Kafka, Rabbitmq, Redis, PubSub, and more coming)
• Easily extensible via Middlewares and Plugins
• Cloud Events standard as base message structure (no more python specific *args and **kwargs in messages)
• AsyncAPI documentation generation from code
• Twelve factor app approach - stdout logging, configuration through environment variables
• Out-of-the-box integration with Prometheus (metrics) and OpenTelemetry (tracing)
• Application bootstrap via .yaml file (see examples/configuration)