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https://github.com/michaelawyu/nanopie

A lightweight framework for building Python microservices and API services with best practices.
https://github.com/michaelawyu/nanopie

api-service microservices python

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A lightweight framework for building Python microservices and API services with best practices.

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README 

          
# nanopie



## Overview



nanopie is a lightweight Python framework for writing microservices and API

backends. The framework provides pluggable solutions for a number of tasks,

such as input validation, serialization/deserialization, authentication,

logging, and tracing, commonly encountered in microservice and API backend

development.



**Note**: nanopie in still in Development. At this moment, nanopie supports

only HTTP (RESTful) microservices/API backends.



## Features



* **Bring your own transport**



    Python has a large number of web framework + server solutions, such

    [flask](https://flask.palletsprojects.com/en/1.1.x/) +

    [gunicorn](http://www.gunicorn.org/),

    [quart](https://pgjones.gitlab.io/quart/) + 

    [uvicorn](https://www.uvicorn.org/),

    and [Tornado](http://www.tornadoweb.org/); many of these options are

    mature, time-tested, and widely-adopted, and it is not nanopie's design

    goal to create another system for processing requests. Instead, nanopie

    leaves the request processing part up to a transport of your choice.

    This allows developers to use nanopie with any web framework and

    server they are most familiar with; it is also possible to

    switch transports at will as you see fit without having to drastically

    change the code.



* **Data modeling and validation**



    nanopie provides a data modeling mechansim, based on metaclasses

    in Python ([PEP 3115](https://www.python.org/dev/peps/pep-3115/)),

    that helps developers model the inputs and outputs to the endpoints of

    their microservices and API backends, making it easy to access data

    idiomatically. nanopie models also features automatic data validation,

    saving the trouble of writing additional checking logic in the code.



    ``` python

    from nanopie import Model, IntField, StringField



    class User(Model):

        name = StringField(max_length=20, min_length=1, pattern="[a-zA-Z]*")

        age = IntField(maximum=100, minimum=0)



    user = User(name="Albert Wesker", age=49)

    print(user.name)

    print(user.age)



    # This will raise an exception

    user = User(name="Link in BotW", age=117)

    ```



    In addition, nanopie models work seamlessly with the built-in

    serialization/deserialization solution; once configured, nanopie

    can automatically parse incoming requests into data model objects,

    and return data model objects as responses in interchangable formats.



* **Pluggable authentication, serialization, logging, and tracing solutions**



    nanopie provides a number of solutions for common tasks in microservices and

    API backend development, known as handlers. You can add handlers to the

    endpoints of your microservices and API backends so that they can perform

    the tasks when requests arrive at the endpoint:

    `HTTPBasicAuthenticationHandler`, for example, authenticates requests

    based on the `Basic` HTTP Authentication Scheme

    ([RFC 7617](https://tools.ietf.org/html/rfc7617)),

    and `OpenTelemetryTracingHandler`, for another example, traces the execution

    of your endpoint and writes the trace to `STDOUT/STDERR` every time a

    request is being processed.



    Handlers can be chained together to execute a sequence of actions.

    A typically pattern is to chain an authentication handler,

    a logging handler, a tracing handler, a serialization handler together

    for an endpoint, so that when a request arrives at the endpoint,

    it is authenticated, logged, and traced by respective handlers, with its

    payload deserialized into an object of nanopie data model automatically.



    ![Handlers](https://github.com/michaelawyu/nanopie/blob/master/docs/images/handlers_alt.png?raw=true)



## Installation



To install nanopie, run the following command:



``` sh

pip install nanopie

```



## Getting started



The following example showcases how to use nanopie to write a simple

microservice with RESTful HTTP API for user management with one

endpoint, `get_user`, which returns a user with a specific user ID.



### Setup



Install `nanopie` and `flask`, a popular Python web micro-framework in your

project. A `Flask` app will serve as the transport of your `nanopie`

microservice in this example:



``` sh

pip install nanopie flask

```



### Defining the data model



The microservice you will build in this example, as introduced at the beginning

of this section, has only one endpoint, which returns the information of

a specific user to clients. To model the user information, create a `user.py`

script in your project with the following `nanopie` data model:



``` python

from nanopie import (

    Model,

    IntField,

    StringField

)



class User(Model):

    name = StringField(max_length=20, min_length=1, pattern="[a-zA-Z]*")

    age = IntField(maximum=100, minimum=0)

```



The `User` model includes two fields, `name`, and `age`, which takes a

`String` value and a `Integer` value respectively. 



Note that both fields have hints and constraints specified:

    

* The `name` field must be an alphabetic string with a maximum of 20

characters and a minimum of 1 character.

* The `age` field must be an integer no smaller than `1` and no greater

than `100`.



`nanopie` can use these hints and constraints to validate data.



### Writing the microservice



Create a Python script, `main.py`, as follows in your project. The script

first creates a Flask app, then creates a `FlaskService` object that nanopie

provides for using a Flask app as the transport of your microservice. The

`FlaskService` object includes a number of methods (decorators) for specifying

HTTP microservice or API backend endpoints; in the case of this project, you

will create a `GET` endpoint which returns a `User` object as defined by

the `User` model detailed in the previous section.



``` python

from flask import Flask

from nanopie import (

    FlaskService

)



# Import the User model created in the last step

from user import User



# Create a Flask app

app = Flask(__name__)

# Create a nanopie microservice with the Flask app as transport

svc = FlaskService(app=app)



# Create a HTTP RESTful API endpoint with the `GET` HTTP method

@svc.get(name="get_user", rule="/users/")

def get_user(uid):

    # Always return the same user regardless of the ID provided

    return User(name="Albert Wesker", age=49)



if __name__ == "__main__":

    app.run(debug=True, port=8080)

```



Run the script:



``` sh

python main.py

```



Open the browser of your system and visit `127.0.0.1:8080/users/1`. It should

return the following JSON string:



``` json

{"name": "Albert Wesker", "age": 49}

```



### Adding authentication capabilities



At this moment your microservice accepts any request from the network. To

protect data from unauthorized sources, you should authenticate

requests in the microservice. nanopie provides a number of pluggable

authentication solutions, known as authentication handlers, that perform

the authentication of requests automatically; for this project, you will

enable HTTP OAuth2 Bearer Token with JWT ([RFC 6750](https://tools.ietf.org/html/rfc6750),

[RFC 7519](https://tools.ietf.org/html/rfc7519)) based authentication in the

service using the

authentication handler `HTTPOAuth2BearerJWTAuthenticationHandler`.



To enable authentication, edit `main.py` and create

a `HTTPOAuth2BearerJWTAuthenticationHandler`:



``` python

from flask import Flask

from nanopie import (

    FlaskService,

    HTTPOAuth2BearerJWTModes,

    HTTPOAuth2BearerJWTAuthenticationHandler

)



# Import the User model created in the last step

from user import User



# Create a Flask app

app = Flask(__name__)



# Create an authentication handler

authentication_handler = HTTPOAuth2BearerJWTAuthenticationHandler(

    # The secret used for generating JWT tokens

    key_or_secret='my-secret',

    # The algorithm used for generating JWT tokens

    algorithm='HS256',

    # Specify that the token comes in the URI query string of the HTTP request

    mode=HTTPOAuth2BearerJWTModes.URI_QUERY

)



# Create a nanopie microservice with the authentication handler just created

svc = FlaskService(app=app,

                   authn_handler=authentication_handler)



...

```



Run the script again:



``` sh

python main.py

```



Open the browser of your system and visit `127.0.0.1:8080/users/1`. Without

a valid token, your service should return a `401 Unauthorized` error.



You can generate a valid token using the algorithm and secret specified in

the code snippet. There are many Python packages available for this purpose;

you can also use [`jwt.io`](https://jwt.io/) to get a token for testing

purposes interactively. Alternatively, use the token below:



```

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.EpM5XBzTJZ4J8AfoJEcJrjth8pfH28LWdjLo90sYb9g

```



And point your browser to `127.0.0.1/users/1?access_token=YOUR-ACCESS-TOKEN`

(replace `YOUR-ACCESS-TOKEN` with a token of your own). Your request is now

authenticated and you should see the user data returned to you.



### Adding logging and tracing capabilities



It is common for developers to use logging and tracing to observe their

microservices and API backends in action. Similar to authentication

handlers, nanopie provides a number of pluggable logging and tracing solutions

as well, integrating with the

[standard Python logging module](https://docs.python.org/3/library/logging.html)

and [OpenTelemetry](https://opentelemetry.io/) respectively.



To enable logging and tracing, edit `main.py` for another time and create

a `LoggingHandler` and `OpenTelemetryTracingHandler`:



``` python

from flask import Flask

from nanopie import (

    FlaskService,

    HTTPOAuth2BearerJWTModes,

    HTTPOAuth2BearerJWTAuthenticationHandler,

    LoggingHandler,

    OpenTelemetryTracingHandler

)



# Import the User model created in the last step

from user import User



# Create a Flask app

app = Flask(__name__)



# Create an authentication handler

...



# Create a logging handler and a tracing handler.

logging_handler = LoggingHandler()

tracing_handler = OpenTelemetryTracingHandler()



# Create a nanopie microservice with an authentication handler,

# a logging handler, and a tracing handler.

svc = FlaskService(app=app,

                   authn_handler=authentication_handler,

                   logging_handler=logging_handler,

                   tracing_handler=tracing_handler)

```



Run the script for another time:



``` sh

python main.py

```



Open the browser of your system and visit

`127.0.0.1/users/1?access_token=YOUR-ACCESS-TOKEN`. Check the output

in the terminal running the Python script, you should see outputs similar

to the snippets below:



```

# The log entry nanopie writes when the execution of an endpoint begins.

{"host": "YOUR-HOST", "logger": "nanopie.logging.base", "level": "INFO", "module": "base", "func": "__call__", "message": "Entering span unspecified_span."}



# The trace of the execution of the endpoint.

{"name": "unspecified", "context": {"trace_id": "7732924096377129845968717310647764965", "span_id": "7510401874971537686", "trace_state": "{}", "is_remote": "False"}, "kind": "SpanKind.SERVER", "parent": null, "start_time": "2020-08-11T20:23:01.350515Z", "end_time": "2020-08-11T20:23:01.351445Z", "attributes": "{}"}



# The long entry nanopie writes when the execution of an endpoint completes.

{"host": "YOUR-HOST", "logger": "nanopie.logging.base", "level": "INFO", "module": "base", "func": "__call__", "message": "Exiting span unspecified_span."}

```



## What's next



Learn more about nanopie in its [documentation](https://nanopie.readthedocs.io).