Skip to main content

gRPC with autogen by Pydantic models

Project description

Pydantic & gRPC

py-grpcio is a microframework and high-level wrapper of grpcio to simplify work with the original library using abstractions, useful python objects and pydantic models.

Examples of use are given below and also duplicated in the example directory.


Install latest

pip install py-grpcio

Example

Models

Pydantic models that describe messages for client-server interaction.

from uuid import UUID, uuid4
from datetime import datetime

from pydantic import Field

from py_grpcio import Message

from example.server.service.enums import Names


class PingRequest(Message):
    id: UUID = Field(default_factory=uuid4)


class PingResponse(Message):
    id: UUID
    timestamp: datetime = Field(default_factory=datetime.now)


class ComplexModel(Message):
    name: Names


class ComplexRequest(Message):
    id: UUID
    model: ComplexModel


class ComplexResponse(Message):
    id: UUID
    model: ComplexModel

Server

Basic implementation of gRPC services on the server side.

You need to describe the service abstractly and duplicate this service on the client side.

from abc import abstractmethod

from py_grpcio import BaseService

from example.server.service.models import PingRequest, PingResponse, ComplexRequest, ComplexResponse


class BaseExampleService(BaseService):
    @abstractmethod
    async def ping(self: 'BaseExampleService', request: PingRequest) -> PingResponse:
        ...

    @abstractmethod
    async def complex(self: 'BaseExampleService', request: ComplexRequest) -> ComplexResponse:
        ...

Full implementation of the gRPC service with methods.

from example.server.service.base import BaseExampleService
from example.server.service.models import PingRequest, PingResponse, ComplexRequest, ComplexResponse


class ExampleService(BaseExampleService):
    async def ping(self: 'ExampleService', request: PingRequest) -> PingResponse:
        return PingResponse(id=request.id)

    async def complex(self: 'BaseExampleService', request: ComplexRequest) -> ComplexResponse:
        return ComplexResponse(**request.model_dump())

Run the ExampleService on Server.

from py_grpcio import BaseServer

from example.server.service import ExampleService


if __name__ == '__main__':
    server: BaseServer = BaseServer()
    server.add_service(service=ExampleService)
    server.run()

Note that on the client side, this class must be named the same as it is named in the full server-side implementation.

That is, if on the server we call the base class as BaseExampleService and the class with the implementation of methods as ExampleService, then on the client side the abstract service should be called ExampleService.

Client

from abc import abstractmethod

from py_grpcio import BaseService

from example.server.service.models import PingRequest, PingResponse, ComplexRequest, ComplexResponse


class ExampleService(BaseService):
    @abstractmethod
    async def ping(self: 'ExampleService', request: PingRequest) -> PingResponse:
        ...

    @abstractmethod
    async def complex(self: 'ExampleService', request: ComplexRequest) -> ComplexResponse:
        ...

Calling the ExampleService endpoints by Client.

from uuid import uuid4
from asyncio import run

from loguru import logger

from example.client.services.example import (
    ExampleService, PingRequest, PingResponse, ComplexModel, ComplexRequest, ComplexResponse, Names
)

service: ExampleService = ExampleService(host='127.0.0.1')


async def main() -> None:
    response: PingResponse = await service.ping(request=PingRequest())
    logger.info(f'ping response: {response}')

    response: ComplexResponse = await service.complex(
        request=ComplexRequest(id=uuid4(), model=ComplexModel(name=Names.NAME_1))
    )
    logger.info(f'complex response: {response}')


if __name__ == '__main__':
    run(main())

Notes

  • You can use the library on the client side even if the server is implemented differently by simply describing it as an abstract service

  • The client can also be implemented using other libraries, the server that uses py-grpcio will still be able to accept such requests

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

py_grpcio-1.9.1.tar.gz (12.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

py_grpcio-1.9.1-py3-none-any.whl (16.7 kB view details)

Uploaded Python 3

File details

Details for the file py_grpcio-1.9.1.tar.gz.

File metadata

  • Download URL: py_grpcio-1.9.1.tar.gz
  • Upload date:
  • Size: 12.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for py_grpcio-1.9.1.tar.gz
Algorithm Hash digest
SHA256 3603d0e4e958b0cc8594cd8f6cdf99642ec3317984ed47526a001129501f65e4
MD5 33b30edffca3c7fcb8282e1a5fdf0b3e
BLAKE2b-256 ca99c6f119bc91b19d7b98ed26a1873698e5f4b80d98e924db5741469158ce30

See more details on using hashes here.

File details

Details for the file py_grpcio-1.9.1-py3-none-any.whl.

File metadata

  • Download URL: py_grpcio-1.9.1-py3-none-any.whl
  • Upload date:
  • Size: 16.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for py_grpcio-1.9.1-py3-none-any.whl
Algorithm Hash digest
SHA256 e30dae611beec392df49c8bbeca1e945814577fd713f0521fa7f2b6dff00c10e
MD5 1a067a39778a3c847d46bac60aa36237
BLAKE2b-256 42cebf7bfd152ad257b766bf84b2630ae17bb2cb860b73cbe6430046ddc8116c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page