cannula 0.0.1

Async GraphQL Helper Library

Cannula

GraphQL for people who like Python!

• Why Cannula
• Installation
• Quick Start
• Documentation

Why Cannula?

We wanted to make the world a better place, but we are programmers so we settled on making the web fun again. Too much attention has been given to Javascript client libraries. They all seem to compete on size and speed and features but most of them do not solve any of the actual problems you have. So while the todo application is quick and easy to follow the hard parts take a long time to complete. For starters Javascript use to be simple and it did not require using a transpiler. It was mostly JQuery and it sort of worked but it didn't get in your way.

Now a days if you want a fancy single page application you need to invest a good week or so planning out all the tools you will need to assemble your site. Every decision is full of sorrow and doubt as you google for the latest trends or how to setup unit tests. Or searching for a bootstrapped version of the library you like.

Using GraphQL you can simplify your web application stack and reduce dependencies to achieve the same customer experience without regret. By using just a few core libraries you can increase productivity and make your application easier to maintain.

Our Philosophy:

1. Make your site easy to maintain.
2. Document your code.
3. Don't lock yourself into a framework.
4. Be happy!

Installation

Requires Python 3.6 or greater!

pip3 install cannula

Quick Start

Here is a small hello world example:

import asyncio
import typing
import sys

from graphql.language import parse

import cannula

api = cannula.API(__name__, schema="""
  type Message {
    text: String
  }
  extend type Query {
    hello(who: String): Message
  }
""")

class Message(typing.NamedTuple):
    text: str

# The query resolver takes a source and info objects and any arguments
# defined by the schema. Here we only accept a single argument `who`.
@api.resolver('Query')
async def hello(source, info, who):
    return Message(f"Hello, {who}!")

# Pre-parse your query to speed up your requests. Here is an example of how
# to pass arguments to your query functions.
SAMPLE_QUERY = parse("""
  query HelloWorld ($who: String!) {
    hello(who: $who) {
      text
    }
  }
""")

async def main():
    who = 'world'
    if len(sys.argv) > 1:
        who = sys.argv[1]

    results = await api.call(SAMPLE_QUERY, variables={'who': who})
    print(results)

loop = asyncio.get_event_loop()
loop.run_until_complete(main())

Now you should see the results if you run the sample on the command line:

$ PYTHONPATH=. python3 examples/hello.py
ExecutionResult(data={'hello': {'text': 'Hello, world!'}}, errors=None)
$ PYTHONPATH=. python3 examples/hello.py Bob
ExecutionResult(data={'hello': {'text': 'Hello, Bob!'}}, errors=None)

But what about Django integration or flask?

from django.contrib.auth.models import User

schema = """
  type User {
    username: String   # Only expose the fields you actually use
    first_name: String
    last_name: String
    made_up_field: String
  }
  extend type Query {
    getUserById(user_id: String): User
  }
"""

@api.query()
async def getUserById(source, info, user_id):
    return User.objects.get(pk=user_id)

@api.resolve('User')
async def made_up_field(source, info):
    return f"{source.get_full_name()} is a lying lier there is no 'made_up_field'"

Since GraphQL is agnostic about where or how you store your data all you need to do is provide a function to resolve a query. The results you return just need to match the schema and you are done.

Django and sqlalchemy already provide tools to query the database. And they work quite well. Or you may choose to use an async database library to make concurrent requests work even better. Try them all and see what works best for your team and your use case.

Documentation

A little light right now... Come back soon for more. Meanwhile have a look at our examples:

• Hello World
• Automatic Mocks
• A Large Custom UI