https://github.com/taskiq-python/taskiq-faststream
FastStream - Taskiq integration to provide you with a great scheduling feature
https://github.com/taskiq-python/taskiq-faststream
asyncio faststream kafka nats python rabbitmq taskiq taskiq-broker
Last synced: 6 months ago
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FastStream - Taskiq integration to provide you with a great scheduling feature
- Host: GitHub
- URL: https://github.com/taskiq-python/taskiq-faststream
- Owner: taskiq-python
- License: mit
- Created: 2023-11-11T18:04:21.000Z (almost 2 years ago)
- Default Branch: develop
- Last Pushed: 2024-10-26T20:47:49.000Z (12 months ago)
- Last Synced: 2025-04-12T07:22:03.171Z (6 months ago)
- Topics: asyncio, faststream, kafka, nats, python, rabbitmq, taskiq, taskiq-broker
- Language: Python
- Homepage:
- Size: 134 KB
- Stars: 35
- Watchers: 2
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Taskiq - FastStream
---
The current package is just a wrapper for [**FastStream**](https://faststream.airt.ai/0.2/?utm_source=github&utm_medium=acquisition&utm_campaign=measure) objects to make them compatible with [**Taskiq**](https://taskiq-python.github.io/) library.
The main goal of it - provide **FastStream** with a great **Taskiq** tasks [scheduling](https://taskiq-python.github.io/guide/scheduling-tasks.html) feature.
## Installation
If you already have **FastStream** project to interact with your Message Broker, you can add scheduling to it by installing just a **taskiq-faststream**
```bash
pip install taskiq-faststream
```
If you starting with a clear project, you can specify **taskiq-faststream** broker by the following distributions:
```bash
pip install taskiq-faststream[rabbit]
# or
pip install taskiq-faststream[kafka]
# or
pip install taskiq-faststream[nats]
```
## Usage
The package gives you two classes: `AppWrapper` and `BrokerWrapper`
These are just containers for the related **FastStream** objects to make them **taskiq**-compatible
To create scheduling tasks for your broker, just wrap it to `BrokerWrapper` and use it like a regular **taskiq** Broker.
```python
# regular FastStream code
from faststream.nats import NatsBroker
broker = NatsBroker()
@broker.subscriber("test-subject")
async def handler(msg: str):
print(msg)
# taskiq-faststream scheduling
from taskiq.schedule_sources import LabelScheduleSource
from taskiq_faststream import BrokerWrapper, StreamScheduler
# wrap FastStream object
taskiq_broker = BrokerWrapper(broker)
# create periodic task
taskiq_broker.task(
message="Hi!",
# If you are using RabbitBroker, then you need to replace subject with queue.
# If you are using KafkaBroker, then you need to replace subject with topic.
subject="test-subject",
schedule=[{
"cron": "* * * * *",
}],
)
# create scheduler object
scheduler = StreamScheduler(
broker=taskiq_broker,
sources=[LabelScheduleSource(taskiq_broker)],
)
```
To run the scheduler, just use the following command
```bash
taskiq scheduler module:scheduler
```
Also, you can wrap your **FastStream** application the same way (allows to use lifespan events and AsyncAPI documentation):
```python
# regular FastStream code
from faststream import FastStream
from faststream.nats import NatsBroker
broker = NatsBroker()
app = FastStream(broker)
@broker.subscriber("test-subject")
async def handler(msg: str):
print(msg)
# wrap FastStream object
from taskiq_faststream import AppWrapper
taskiq_broker = AppWrapper(app)
# Code below omitted 👇
```
A little feature: instead of using a final `message` argument, you can set a message callback to collect information right before sending:
```python
async def collect_information_to_send():
return "Message to send"
taskiq_broker.task(
message=collect_information_to_send,
...,
)
```
Also, you can send a multiple message by one task call just using generator message callback with `yield`
```python
async def collect_information_to_send():
"""Sends 10 messages per task call."""
for i in range(10):
yield i
taskiq_broker.task(
message=collect_information_to_send,
...,
)
```