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celery-abc-0.0.1
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Create microservices with celery and abstract interfaces.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | celery-abc-0.0.1 |
| نام | celery-abc |
| نسخه کتابخانه | 0.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Mikhail Khimukhin |
| ایمیل نویسنده | greenblacksky@gmail.com |
| آدرس صفحه اصلی | https://github.com/GreenBlackSky/celery-abc |
| آدرس اینترنتی | https://pypi.org/project/celery-abc/ |
| مجوز | - |
# celery-abc
It's time for some dynamic meta-magic!
## What is it?
Set of tools to create microservice app with `celery` as message brocker.
## When to use it?
Say, you want to write some microservices with python. And you want them to work seamlessly with each other - call methods of one service from another like it is a single app.
You may use `celery` just for that. But you actually need to register tasks in the same app. Isn't that a little strange - to keep the code base of ALL the services in each and every of them?
Wouldn't it be more convinient, to share only interfaces? That way each developer could track what they can use of other services, but without actully storing their code.
## How to use it?
We'll need at least two services and a shared directory. In shared directory we'll create an abstract class `Interface`. Let's call one service `Caller` and the other - `Worker`. `Caller` has `Interface` imported, but knows nothing about the implementation. `Worker` has `Interface` implemented.
1. Create common directory, share it with all your services. Chances are - you'll need one anyway.
2. In the shared folder create python module. Define your interfaces there. For now, `celery-abc` requires for `Interface` to be abstract.
```python
from abc import ABC, abstractmethod
class Interface(ABC):
@abstractmethod
def do_some_stuff(self, arg):
pass
@abstractmethod
def do_more_stuff(self, arg):
pass
```
3. In your `Worker` service import `Interface` from shared module and implement it. Metaclass for your implementation should be `WorkerMetaBase` from `celery-abc`. You don't need to register it's methods, just instantiate your class with `celery`. Run your `worker` as a celery worker.
```python
from celery import Celery
from celery_abc import WorkerMetaBase
from shared import Interface
class Worker(Interface, metaclass=WorkerMetaBase):
def do_some_stuff(self, arg):
return self.do_more_stuff(arg)
def do_more_stuff(self, arg):
return f"Doing stuff {arg}"
celery_app = Celery(...)
Worker(celery)
```
4. In `Caller` service import `CallerMetaBase`, create a new class, that inherits from `Interface` and have `CallerMetaBase` as a metaclass. Instantiate it with your `celery` app. Unlike `worker`, `caller` may run just as regular python app. Now, you can call methods, that are implemented in the `Worker` =)
```python
class Caller(Interface, metaclass=CallerMetaBase):
pass
celery_app = Celery(...)
caller = Caller(celery)
result = caller.do_some_stuff('Hello!')
```
Here you go, now you can seamlessly call methods of `Worker` from `Caller`. As you can see, you can call `Worker`'s methods from each other. But you can not keep any instnce variables in `Worker`'s `self`. In the end, microservices are better when they are stateless.
## When not to use it?
* If you want to create statefull microservices - you still can use `celery-abc`, if only for comunication. But there may be other solutions.
* If you want to create complex system of classes. OOP is cool, but multiple and chaind inheritance are not supported at the moment.
* Method signature can't have `*args` and `**kwargs` in it. It is totally fine to pass argumetns this way, but capturing them is not supported yet.
## Interesting stuff
* Abstract methods
* Metaclasses
* Object-method
* Descriptors
* inspect
* worker's self
## Example
Project includes small example module. To run it, create inside `config.env` file with redis creds and run `docker-compose -f "tests\docker-compose.yaml" up -d --build`
```
RABBITMQ_DEFAULT_USER=...
RABBITMQ_DEFAULT_PASS=...
RABBITMQ_HOST=...
RABBITMQ_PORT=...
```
## In the future:
* Support inheritance
* Support capturing arguments
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9 | Python |
نحوه نصب
نصب پکیج whl celery-abc-0.0.1:
pip install celery-abc-0.0.1.whl
نصب پکیج tar.gz celery-abc-0.0.1:
pip install celery-abc-0.0.1.tar.gz