<p align="center">
<img src="https://performancemedia.github.io/eventiq/assets/logo.svg" style="width: 250px">
</p>
<p align="center">
<em>Cloud native framework for building event driven applications in Python</em>
</p>
[](https://github.com/PyCQA/bandit)
[](https://github.com/charliermarsh/ruff)
*Note: This package is under active development and is not recommended for production use*
---
Version: 0.2.0
Documentation: https://performancemedia.github.io/eventiq/
Repository: https://github.com/performancemedia/eventiq
---
## About
The package utilizes `anyio` and `pydantic` as the only required dependencies.
For messages [Cloud Events](https://cloudevents.io/) format is used.
Service can be run as standalone processes, or included into starlette (e.g. FastAPI) applications.
## Installation
```shell
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`
- broker of choice: `nats`, `kafka`, `rabbitmq`, `redis`, `pubsub`
- custom message serializers: `msgpack`, `orjson`
- `prometheus` - Metric exposure via `PrometheusMiddleware`
- `opentelemetry` - tracing support
## Motivation
Python has many "worker-queue" libraries and frameworks, such as:
- [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html)
- [Dramatiq](https://dramatiq.io/)
- [Huey](https://huey.readthedocs.io/en/latest/)
- [arq](https://arq-docs.helpmanual.io/)
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
```python
# main.py
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}")
```
## Running the service
```shell
eventiq run main:service
```
## 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](http://supervisord.org/),
or web server like gunicorn.
## Features
- Modern, `asyncio` based python 3.8+ syntax
- Minimal dependencies, only `anyio` and `pydantic` 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)
- 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)
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"description": "<p align=\"center\">\n<img src=\"https://performancemedia.github.io/eventiq/assets/logo.svg\" style=\"width: 250px\">\n\n</p>\n<p align=\"center\">\n<em>Cloud native framework for building event driven applications in Python</em>\n</p>\n\n\n\n\n\n\n\n[](https://github.com/PyCQA/bandit)\n[](https://github.com/charliermarsh/ruff)\n\n*Note: This package is under active development and is not recommended for production use*\n\n---\nVersion: 0.2.0\n\nDocumentation: https://performancemedia.github.io/eventiq/\n\nRepository: https://github.com/performancemedia/eventiq\n\n---\n## About\n\nThe package utilizes `anyio` and `pydantic` as the only required dependencies.\nFor messages [Cloud Events](https://cloudevents.io/) format is used.\nService can be run as standalone processes, or included into starlette (e.g. FastAPI) applications.\n\n## Installation\n\n```shell\npip install eventiq\n```\n\n## Multiple broker support (in progress)\n\n- Stub (in memory using `asyncio.Queue` for PoC, local development and testing)\n- NATS (with JetStream)\n- Redis Pub/Sub\n- Kafka\n- Rabbitmq\n- Google Cloud PubSub\n- And more coming\n\n## Optional Dependencies\n - `cli` - `typer`\n - broker of choice: `nats`, `kafka`, `rabbitmq`, `redis`, `pubsub`\n - custom message serializers: `msgpack`, `orjson`\n - `prometheus` - Metric exposure via `PrometheusMiddleware`\n - `opentelemetry` - tracing support\n\n## Motivation\n\nPython has many \"worker-queue\" libraries and frameworks, such as:\n\n- [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html)\n- [Dramatiq](https://dramatiq.io/)\n- [Huey](https://huey.readthedocs.io/en/latest/)\n- [arq](https://arq-docs.helpmanual.io/)\n\nHowever, those libraries don't provide a pub/sub pattern, useful for creating\nevent driven and loosely coupled systems. Furthermore, the majority of those libraries\ndo not support `asyncio`. This is why this project was born.\n\n## Basic usage\n\n\n```python\n# main.py\nimport asyncio\nfrom eventiq import Service, CloudEvent, Middleware\nfrom eventiq.backends.nats.broker import JetStreamBroker\n\n\nclass SendMessageMiddleware(Middleware):\n async def after_broker_connect(self, broker: \"Broker\") -> None:\n print(f\"After service start, running with {broker}\")\n await asyncio.sleep(10)\n for i in range(100):\n await broker.publish(\"test.topic\", data={\"counter\": i})\n print(\"Published event(s)\")\n\nbroker = JetStreamBroker(url=\"nats://localhost:4222\")\nbroker.add_middleware(SendMessageMiddleware())\n\nservice = Service(name=\"example-service\", broker=broker)\n\n@service.subscribe(\"test.topic\")\nasync def example_run(message: CloudEvent):\n print(f\"Received Message {message.id} with data: {message.data}\")\n\n```\n## Running the service\n\n```shell\neventiq run main:service\n```\n\n## Scaling\n\nEach message is load-balanced (depending on broker) between all service instances with the same `name`.\nTo scale number of processes you can use containers (docker/k8s), [supervisor](http://supervisord.org/),\nor web server like gunicorn.\n\n## Features\n\n- Modern, `asyncio` based python 3.8+ syntax\n- Minimal dependencies, only `anyio` and `pydantic` are required\n- Automatic message parsing based on type annotations (like FastAPI)\n- Code hot-reload\n- Highly scalable: each service can process hundreds of tasks concurrently,\n all messages are load balanced between all instances by default\n- Resilient - at least once delivery for all messages by default\n- Customizable & pluggable message encoders (json, msgpack, custom)\n- Multiple broker support (Nats, Kafka, Rabbitmq, Redis, PubSub, and more coming)\n- Easily extensible via Middlewares and Plugins\n- Cloud Events standard as base message structure (no more python specific `*args` and `**kwargs` in messages)\n- AsyncAPI documentation generation from code\n- Twelve factor app approach - stdout logging, configuration through environment variables\n- Out-of-the-box integration with Prometheus (metrics) and OpenTelemetry (tracing)\n- Application bootstrap via `.yaml` file (see examples/configuration)\n\n",
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