Using GraphQL in Python with Graphene – An Introduction

I’m not going to answer the question “What is GraphQL?”. The internet is lousy with that sort of stuff, and I don’t feel a need to cover it.

My 30 second summary: GraphQL is a specification to implement a query engine that simplifies the retrieval of relational mappings between data types that are inherently already relational but not necessarily regularly referenced that way.*

Also, it is not just a JavaScript project. Tell your friends.

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Rather than rehashing the virtues of GraphQL or doing another JS demo, I’d like to dive into a quick start around building an engine using Graphene – a Python implementation of the GraphQL spec.

But to give a real quick review…

There are four main pillars of a GraphQL implementation.

1. Types (I need a thing)
2. Fields (Tell me about that thing)
3. Resolvers (This is how you get that thing)
4. Schemas (These are all the things I can give you)

As an example, let’s say I need the details (ie, fields) of an S3 bucket (ie, a type), as well as the details (ie, more fields) of all the objects in it (ie, both a type and a field). I also need to build some functions to get all of that together for me (ie, resolvers), and I need to expose all of that capability somehow (ie, a schema).

In fact, that is such a good example, let’s just build a small version of that in Python using Graphene.

Series coming soon…

1. Defining a Graphene ObjectType for a GraphQL Engine
2. Defining a Graphene Resolver to return a GraphQL ObjectType
3. Defining a Graphene Field to create a complete GraphQL Definition
4. Adding to a Graphene Schema to Execute a GraphQL Query