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Communication components for Pip.Services in Python

Project description

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Client library for sample data microservice

This is a client library to the sample data microservice. This library shall be used as a template to create clients to general purpose data microservices.

Supported functionality:

  • Null and Mock clients for testing
  • HTTP clients: REST and Commandable
  • GRPC clients: Plain and Commandable
  • AWS Lambda clients: Plain and Commandable

Key patterns implemented in this library:

Zero-time onboarding: A new developer doesn't have to have a prior khowledge of the code nor preinstalled and preconfigured development environment. To get started with any component he/she just need to do 3 simple steps:

Automated build and test processes: Clear, build and test actions are dockerized and scripted. The scripts shall be run before committing the code. And the same scripts shall be executed in automated CI/CD pipelines. That approach allows to make identical build and test actions across the entire delivery pipeline. And have a clear separation between developer and DevOps roles (developers are responsible for individual components, their build, test and packaging. DevOps are responsible for running CI/CD pipelines, assembling and testing entire system from individual components).

Multiple communication protocols: The library contains clients that allow to connect to the microservice several different ways, depending on the environment or client requirements. For instance: on-premises the microservice can be deployed as a docker container. Locally it can be called via GRPC interface and externally via REST. When the same microservice is deployed on AWS cloud as a Lambda function, it can be called using the LambdaClient. Moreover, several microservice can be packaged into a single process, essentially represending a monolith. In that scenario, then can be called using in-process calls using the DirectClient.

Monitoring and Observability: All clients are instrumented to collect logs of called operations, metrics that collect number of calls, average call times and number of erors, and traces. Depending on the deployment configuration that information can be sent to different destinations: console, Promethous, DataDog service, ApplicationInsights, CloudWatch and others.

Versioning: Data objects and clients are versioned from the beginning. When breaking changes are introduced into the microservice, it shall keep the old version of the interface for backward-compatibility and expose a new version of the interface simultaniously. Then client library will have a new set of objects and clients for the new version, while keeping the old one intact. That will provide a clear versioning and backward-compatibility for users of the microservice.

Quick links:

Contract

The contract of the microservice is presented below.

class EntityV1(IStringIdentifiable):
    def __init__(self, id: str = None, site_id: str = None, type: str = None, name: str = None, content: str = None):
        self.id = id            # Entity ID
        self.type = type        # ID of a work site (field installation)
        self.site_id = site_id  # Entity type: Type2, Type1 or Type3
        self.name = name        # Human readable name
        self.content = content  # String content

class IEntitiesClientV1(ABC):

    def get_entities(self, correlation_id: Optional[str], filter_params: FilterParams, paging: PagingParams) -> DataPage:
        raise NotImplementedError("Method is not implemented")

    def get_entities_by_id(self, correlation_id: Optional[str], entity_id: str) -> EntityV1:
        raise NotImplementedError("Method is not implemented")

    def get_entity_by_name(self, correlation_id: Optional[str], entity_name: str) -> EntityV1:
        raise NotImplementedError("Method is not implemented")

    def create_entity(self, correlation_id: Optional[str], entity: EntityV1) -> EntityV1:
        raise NotImplementedError("Method is not implemented")

    def update_entity(self, correlation_id: Optional[str], entity: EntityV1) -> EntityV1:
        raise NotImplementedError("Method is not implemented")

    def delete_entity_by_id(self, correlation_id: Optional[str], entity_id: str) -> EntityV1:
        raise NotImplementedError("Method is not implemented")

Get

Get the microservice source from GitHub:

git clone git@github.com:pip-templates-services/pip-service-data-python.git

Install the microservice dependencies:

pip install -r requirements.txt

Install the client library as a binary dependency:

pip install pip-client-data-python

Use

Install the client pip package as

pip install install pip-client-data-python

Inside your code get the reference to the client SDK

from pip_services3_commons.config import ConfigParams
from pip_services3_commons.data import FilterParams, PagingParams

from pip_service_data_python.data.EntityTypeV1 import EntityTypeV1
from pip_service_data_python.data.EntityV1 import EntityV1

from pip_client_data_python.clients.version1 import EntitiesCommandableHttpClientV1

Instantiate the client

# Create the client instance
client = EntitiesCommandableHttpClientV1()

Define client connection parameters

# Client configuration
http_config = ConfigParams.from_tuples(
	"connection.protocol", "http",
	"connection.host", "localhost",
	"connection.port", 8080
)
# Configure the client
client.configure(http_config)

Connect to the microservice

# Connect to the microservice
client.open(None)

# Work with the microservice
...

Call the microservice using the client API

# Define a entity
entity = EntityV1(
    id= '1',
    site_id= '1',
    type= EntityTypeV1.Type1,
    name= '00001',
    content= 'ABC'
)

# Create the entity
entity = self.client.create_entity(None, ENTITY1)

# Do something with the returned entity...

# Get a list of entities
page = self.client.get_entities(
    None,
    FilterParams.from_tuples(
        "name", "TestEntity",
    ),
    PagingParams(0, 10)
)

# Do something with the returned page...
# E.g. entity = page['data'][0]

Develop

For development you shall install the following prerequisites:

  • Python 3.6+
  • Visual Studio Code or another IDE of your choice
  • Docker

Install dependencies:

pip install -r requirements.txt

Before running tests launch infrastructure services and required microservices:

docker-compose -f ./docker-compose.dev.yml up

Run automated tests:

pytest

Generate GRPC protobuf stubs:

./protogen.ps1

Generate API documentation:

./docgen.ps1

Before committing changes run dockerized build and test as:

./build.ps1
./test.ps1
./package.ps1
./run.ps1
./clear.ps1

Contacts

This microservice was created and currently maintained by Sergey Seroukhov and Danil Prisyzhniy.

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