pydiator-core 1.0.3

Pydiator

Installation

https://pypi.org/project/pydiator-core/

add your requirements.txt pydiator-core or run the pip install pydiator-core command

Examples

Console : https://github.com/ozgurkara/pydiator-core/blob/master/examples/main.py

Fastapi : https://github.com/ozgurkara/fastapi-pydiator

What is the pydiator?

Pydiator is an in-app communication method.

It provides that developing the code as an aspect. Also, it supports clean architecture infrastructure

It is using design patterns such as chain of responsibility, mediator, singleton.

Pydiator provides which advantages to developers and project?

• Is testable
• Has Use case support
• Has Aspect programming (Authorization, Validation, Cache, Logging, Tracer etc.) support
• Has Clean architecture support
• Expandable architecture via pipeline
• Is independent framework
• Has SOLID principles

How it works?

Pydiator knows 4 object types. These are;

1- Request object

• Is used for calling the use case.
• It should be inherited from BaseRequest

 class GetSampleByIdRequest(BaseRequest):
     def __init__(self, id: int):
         self.id = id

---

2- Response object

• Is used for returning from use case
• It should be inherited from BaseResponse

class GetSampleByIdResponse(BaseResponse):
     def __init__(self, id: int, title: str):
         self.id = id
         self.title = title 

---

3- Use Case

• Includes logic codes
• It should be inherited from BaseHandler
• It takes one parameter to handle. The parameter should be inherited BaseRequest

class GetSampleByIdUseCase(BaseHandler):
     async def handle(self, req: GetSampleByIdRequest):
         # related codes are here such as business
         return GetSampleByIdResponse(id=req.id, title="hello pydiatr")     

---

What is the relation between these 3 object types?

Every use case object only knows a request object

Every request object is only used by one use case object

How is the use case run?

Should be had a particular map between the request object and the use case object.

Mapping example;

    def set_up_pydiator():
        container = MediatrContainer()
        container.register_request(GetSampleByIdRequest, GetSampleByIdUseCase())
        #container.register_request(xRequest, xUseCase())
        pydiator.ready(container=container)

Calling example;

    await pydiator.send(GetByIdRequest(id=1))

or

    loop = asyncio.new_event_loop()
    response: GetByIdResponse = loop.run_until_complete(pydiator.send(GetByIdRequest(id=1)))
    loop.close()
    print(response.to_json())

---

4- Pipeline

The purpose of the pipeline is to manage the code as an aspect. For instance, you want to write a log for the request and the response of every use case. You can do it via a pipeline easily. You can see the sample log pipeline at this link.

You can create a lot of pipelines such as cache pipeline, validation pipeline, tracer pipeline, authorization pipeline etc.

Also, you can create the pipeline much as you want but you should not forget that every use case will be used in this pipeline.

You can add the pipeline to pipelines such as;

    def set_up_pydiator():
        container = MediatrContainer()        
        container.register_pipeline(LogPipeline())
        #container.register_pipeline(xPipeline())
        pydiator.ready(container=container)

How can I write custom pipeline?

• Every pipeline should be inherited BasePipeline
• Sample pipeline

    class SamplePipeline(BasePipeline):
        def __init__(self):
            pass

        async def handle(self, req: BaseRequest) -> object:

            # before executed pipeline and uce case

            response = await self.next().handle(req)

            # after executed next pipeline and use case            

            return response

How to run the Unit Tests

install tests/requirements.txt

pytest tests/