Why GraphQL for Text Analytics

GraphQL offers a different API paradigm from REST: instead of exposing many specialized endpoints, you provide a single endpoint that accepts structured queries. Those queries are matched against your server’s resolver functions, which return exactly the fields the client asked for. For a Python backend that wraps a text-analytics library, this means you can ship one API surface and let clients compose their own requests without waiting on new endpoints or doc updates.

This article walks through two complementary setups: a self-hosted GraphQL API built with Flask, Graphene and NLTK, and a serverless alternative where Fauna generates and hosts the entire GraphQL layer from a schema file.

Exposing an Edit-Distance API with Flask and Graphene

The reference project in this walkthrough relies on Pipenv for dependency isolation. From the project directory you can create the virtual environment and install what’s listed in the Pipfile.

For day-to-day development, the project defines command aliases in the Pipfile so a single command starts Flask:

Flask defaults to port 5000. Once running, you can open the GraphQL Playground, an IDE that uses GraphQL introspection to display live documentation for your schema. The Flask application itself is initialized in the conventional way:

Switching to production means running under a WSGI server. A gunicorn alias handles that:

The Core Feature: Levenshtein Distance

Levenshtein distance, also called edit distance, is the minimum number of single-character edits required to transform one string into another. For two strings a and b, with lengths |a| and |b|, the recursive definition is:

Where:

Here 1(ai≠bj) is an indicator returning 0 when the characters match and 1 otherwise, and lev(a,b)(i,j) denotes the distance between the first i characters of a and the first j characters of b.

A practical example: transforming machinlt learning into machine learning requires three edits, giving a Levenshtein distance of 3. The metric underpins spell checkers, optical character recognition correction, and similarity scoring.

Schema Design with Graphene

Every GraphQL schema needs at least one query. The example uses a simple health-check query:

query {
  healthcheck
}

The meaningful operation is a mutation that accepts variable inputs and returns the computed distance. Since the Python ecosystem lacks tooling like graphql-tools, Graphene is used with a code-first approach to define the schema. Input types are expressed as Graphene classes, and the mutation’s resolver invokes NLTK’s implementation rather than reimplementing the matrix algorithm:

{
  "input": {
    "s1": "test1",
    "s2": "test2"
  }
}

Executing the mutation with the variables test1 and test2 yields a distance of 1. NLTK handles the computation cleanly, so there’s no reason to roll your own edit-distance function when a canonical library exists.

Going Serverless with Fauna

Self-hosting a server means provisioning and maintaining your own database instance. Fauna sidesteps that by offering a serverless, document-oriented database as a managed service. You upload a schema file, and Fauna takes on the operational burden — scaling, maintenance and indexing — so you can focus on application logic.

Bootstrapping the Database

After signing up for a Fauna account, go to the dashboard and create a new database. A dialog asks for a name; whether you call it graphqlbyexample or anything else, skip the “pre-populate with demo data” option and save.

Uploading a Schema

Fauna’s GraphQL option accepts a raw schema file with a .gql or .graphql extension. Create a file named schema.gql in your project directory and define your data types — in this example a User type and a Notes type with a relation between them:

Once uploaded, Fauna processes the file and navigates to a GraphQL playground where your API is live. You’ve effectively created a GraphQL server by uploading a plain text file.

Several conveniences come along automatically:

  • Collections: Fauna creates collections for you automatically — no manual table setup. Being a NoSQL store, Fauna treats a collection like a table and documents like rows.
  • Indexes: The schema upload generates the indexes Fauna uses for data retrieval, replacing the primary-key/foreign-key model of relational databases.
  • Queries, mutations and documentation: Fauna inspects your types and generates a sensible starting set of queries and mutations, all visible in the playground’s Docs tab.

Exercising the Generated API

The playground serves as a full GraphQL client, so you can test generation and retrieval cycles directly:

  1. Create a user with the pre-generated createUser mutation:

The new document appears under the User collection.

  1. Create a note tied to that user. Fauna assigns every document a reference ID, which you can copy from the collection view. Pass that ID as the author field in a createNote mutation:
  1. Retrieve data with the generated findUserByID or findNotesByID queries:

For a use case like listing all users, the generated schema won’t have an endpoint. That’s a schema update: append a custom query to schema.gql, save the file, and click the “Update Schema” option in the playground to re-upload it. After a brief update you can execute your new query and also use GraphQL’s field-selection capability to fetch only the attributes you want.

Calling the API from Python

The API works from any HTTP client, not just the playground. First, create a server key from the Security dashboard’s Keys tab, choosing the current database and the server role. Copy the key secret immediately and store it as an environment variable.

With requests installed, a small script demonstrates the flow. The endpoint URL comes from the Fauna GraphQL playground:

Building the request means constructing a GraphQL query — here findUserByID — loading the secret from your environment, and placing it in the request headers. Then send the HTTP request and inspect the response:

Check the response’s status_code; a successful request returns the user data, otherwise print an error. Running the script confirms the API is reachable outside of Fauna’s IDE.

Choosing Your Path

The two approaches here cover opposite ends of the operations spectrum. Flask plus Graphene gives you full control over server code, resolvers and the dependency stack — a good fit when your logic is custom or runs inside your own infrastructure. Fauna collapses the time-to-API dramatically: write a schema, upload it, and you get a hosted GraphQL server with collections, indexes, queries, mutations and docs handled for you. For text analytics in particular, the first path shows how easy it is to wrap a library like NLTK as a GraphQL service; the second shows how quickly you can stand up a complete data API for clients.