Onboarding a Python based REST API service

This article is part of a series of onboarding articles, which outline the process of onboarding REST API services to Zowe API Mediation Layer (API ML).

Role: API service developer

REST services based on Python can be onboarded with Zowe API ML using the Python Enabler.

note

For more information about the range of options to onboard API services with API ML, see the Onboarding Overview.

Introduction

The API ML onboarding Python enabler is a Python package which helps to simplify the process of onboarding a REST service written in Python with API ML.

Follow the steps described in this article to onboard a REST API service with API ML using the onboarding Python enabler.

Prerequisites

Ensure that you meet the following prerequisites:

• You satisfy the prerequisites from the Onboarding Overview.
• The REST API service to onboard is written in Python.
• The service is enabled to communicate with API ML Discovery Service over a TLS v1.2 secured connection.

Installing the Python dependency

Install the onboarding Python enabler package as a dependency of your service. Run the following pip command from your project directory:

pip install zowe-apiml-onboarding-enabler-python

note

If you have a multi-module project, ensure that you run the pip command from the submodule where your Python project is located.

Configuring your service

Create a yaml file named service-configuration.yml inside the /config directory at the same level of your app.py. Add the configuration properties as presented in the following configuration example.

Example:

 eureka:
    ssl: true
    host: localhost
    ipAddress: 127.0.0.1
    port: 10011
    servicePath: '/eureka'
    maxRetries: 30
    requestRetryDelay: 1000
    registryFetchInterval: 5


 instance:
    app: pythonservice
    scheme: https
    vipAddress: pythonservice
    instanceId: localhost:pythonservice:10018
    homePageUrl: https://localhost:10018/pythonservice
    hostName: 'localhost'
    ipAddr: '127.0.0.1'
    port: 10018
    securePort: 10018
    secureVipAddress: pythonservice
    statusPageUrl: https://localhost:10018/pythonservice/application/info
    healthCheckUrl: https://localhost:10018/pythonservice/application/health
    nonSecurePortEnabled': false
    securePortEnabled: true
    metadata:
       apiml.catalog.tile.id: 'cademoapps'
       apiml.catalog.tile.title: 'Sample Python Hello World'
       apiml.catalog.tile.description: 'Applications Hello'
       apiml.routes.api_v1.gatewayUrl: "api/v1"
       apiml.routes.api_v1.serviceUrl: "/pythonservice"
       apiml.apiInfo.0.apiId: org.zowe.pythonservice
       apiml.apiInfo.0.gatewayUrl: "api/v1"
       apiml.apiInfo.0.swaggerUrl: https://localhost:10018/pythonservice/apidoc
       apiml.apiInfo.0.version: 1.0.0
       apiml.service.title: 'Zowe Sample Python Service'
       apiml.service.description: 'Sample API services to demonstrate Python Onboarding Enabler'

 ssl:
    certificate: ssl/localhost.keystore.cer
    keystore: ssl/localhost.keystore.key
    caFile: ssl/localhost.pem
    keyPassword: password

Registering your service with API ML

To register your service with API ML, use the following procedure:

1. Inside your Python service app.py, add the following script to register your service with Eureka:

   from fastapi import FastAPI
   from onboarding_enabler_python.registration import PythonEnabler
       
   app = FastAPI()
   enabler = PythonEnabler(config_file="service-configuration.yml")
       
   @app.on_event("startup")
   def register_service():
      enabler.register()

2. Start your Python service and verify that the service is registered to Zowe API Mediation Layer.

Validating the discoverability of your API service by the Discovery Service

Once you build and start your service successfully, you can validate that your service is registered correctly with the API ML Discovery Service.

1. Validate successful onboarding.

2. Check that you can access your API service endpoints through the Gateway.

3. (Optional) Check that you can access your API service endpoints directly outside the Gateway.

Specific addresses and user credentials for the individual API ML components depend on your target runtime environment.

Notes:

• If you are working with a local installation of API ML, and you are using the provided dummy identity provider, enter user for both username and password. If API ML was installed by system administrators, ask them to provide you with actual addresses of API ML components and the respective user credentials.
• Wait for the Discovery Service to fully register your service. This process may take a few minutes after your service starts successfully.