fastapi celery
时间: 2023-09-26 16:04:51 浏览: 33
您想了解关于 FastAPI 和 Celery 的结合使用吗?如果是的话,我可以为您提供一些信息。FastAPI 是一个基于 Python 的现代 Web 框架,它可以帮助您快速地构建高性能的 Web 应用程序和 API。Celery 是一个 Python 的异步任务队列/消息中间件,它可以帮助您在分布式环境中进行任务调度和处理。
结合使用 FastAPI 和 Celery 可以实现异步任务处理的能力。具体地说,您可以通过 FastAPI 的路由函数来触发 Celery 的异步任务,并在任务完成后使用 WebSocket 或者其他方式向客户端推送结果。FastAPI 和 Celery 的结合使用需要通过一些额外的设置和配置来实现,例如使用 Celery 的 Redis 作为消息中间件,以及使用 FastAPI 的异步支持等等。如果您需要更详细的信息,可以参考 FastAPI 和 Celery 的官方文档。
相关问题
celery在fastapi中的应用
在FastAPI中使用Celery可以实现异步任务的执行。以下是一些基本步骤:
1. 安装Celery
可以使用pip安装Celery:
```
pip install celery
```
2. 创建Celery实例
在FastAPI应用中,可以使用`Celery`类创建一个Celery实例。例如:
```python
from celery import Celery
app = Celery('tasks', broker='pyamqp://guest@localhost//')
```
其中,`tasks`是应用的名称,`broker`是消息中间件的地址。
3. 定义任务
可以使用`@app.task`装饰器定义Celery任务。例如:
```python
@app.task
def add(x, y):
return x + y
```
4. 启动Celery worker
可以使用以下命令启动Celery worker:
```
celery -A tasks worker --loglevel=info
```
其中,`tasks`是应用的名称。
5. 调用任务
在FastAPI应用中,可以使用`delay`方法调用Celery任务。例如:
```python
from fastapi import FastAPI
from tasks import add
app = FastAPI()
@app.get("/add")
async def add_numbers(x: int, y: int):
result = add.delay(x, y)
return {"task_id": result.id}
```
其中,`add`是Celery任务的名称,`delay`方法会异步执行任务,并返回任务ID。
6. 获取任务结果
可以使用`AsyncResult`类获取任务的执行结果。例如:
```python
from tasks import add
result = add.delay(4, 4)
print(result.get())
```
以上是在FastAPI中使用Celery的基本步骤,希望对你有所帮助。
fastapi Schedule
FastAPI doesn't have built-in support for scheduling tasks, but it is possible to implement scheduling functionality using external libraries or tools.
One popular library for scheduling tasks in Python is Celery. Celery is a distributed task queue that allows you to run tasks asynchronously and schedule them for execution at specific times or intervals.
To use Celery with FastAPI, you would need to configure the Celery instance and define the tasks you want to schedule. Here's an example:
```python
from celery import Celery
app = Celery('tasks', broker='pyamqp://guest@localhost//')
@app.task
def add(x, y):
return x + y
@app.task
def multiply(x, y):
return x * y
```
In this example, we define two tasks: `add` and `multiply`. These tasks can be scheduled for execution at specific times or intervals using Celery's scheduling features.
To start the Celery worker and scheduler, you can use the following commands:
```bash
celery -A tasks worker --loglevel=info
celery -A tasks beat --loglevel=info
```
The first command starts the worker that executes the tasks, while the second command starts the scheduler that schedules the tasks.
You can then use FastAPI to trigger the tasks by calling their corresponding functions. For example:
```python
from fastapi import FastAPI
from tasks import add, multiply
app = FastAPI()
@app.post('/add')
def add_numbers(x: int, y: int):
result = add.delay(x, y)
return {'task_id': result.id}
@app.post('/multiply')
def multiply_numbers(x: int, y: int):
result = multiply.delay(x, y)
return {'task_id': result.id}
```
In this example, we define two endpoints `/add` and `/multiply` that trigger the `add` and `multiply` tasks, respectively. The `delay` method is used to schedule the tasks for execution asynchronously.
Note that this is just a simple example, and there are many other libraries and tools that can be used for scheduling tasks in Python. Celery is just one option that is commonly used in conjunction with FastAPI.