Implementing a single GraphQL across multiple data sources

This article demonstrates schema stitching across multiple Fauna instances, creating a unified GraphQL API from disparate data sources. We'll also explore combining Fauna with other GraphQL services.

Understanding Schema Stitching

Schema stitching is a technique for building a single GraphQL API from multiple underlying GraphQL APIs.

Why Use Schema Stitching?

Large-scale applications often employ microservices for modularity. However, client applications frequently need data from multiple sources. A unified GraphQL API provides a clean interface, but building a monolithic server can be problematic. Fauna's native GraphQL per database is ideal, but multiple databases require connecting to multiple instances, creating tight coupling. Schema stitching solves this by combining multiple GraphQL services into a single, unified schema. This article covers:

1. Integrating multiple Fauna instances into a single GraphQL service.
2. Combining Fauna with other GraphQL APIs and data sources.
3. Creating a serverless GraphQL gateway using AWS Lambda.

Consolidating Multiple Fauna Instances

Let's combine multiple Fauna database instances (e.g., Product, Inventory, Review) into a single GraphQL service. Each database has its own GraphQL API (subgraph). Our goal is a unified graph for client applications to query data across all sources.

This unified graph will be our gateway service. We'll build this using Node.js.

1. Project Setup: Create a Node.js project:

   mkdir my-gateway
   cd my-gateway
   npm init --yes

2. Install Dependencies:

   npm i express express-graphql graphql @graphql-tools/schema @graphql-tools/stitch @graphql-tools/wrap cross-fetch --save

3. Basic GraphQL Server (gateway.js): Start with a simple Express GraphQL server:

   // gateway.js (initial version)
   // ... (Express server setup) ...

   (This initial code would be the bare-bones server, similar to the original example, but omitted for brevity to focus on the stitching aspects.)

4. Fauna Database Setup: Create three Fauna databases (Product, Inventory, Review) and generate admin keys. Define GraphQL schemas for each database (as shown in the original example).

5. Gateway Service Enhancement (gateway.js): Implement schema stitching using @graphql-tools/stitch. This section will include the makeRemoteExecutor and makeGatewaySchema functions as described in the original, but with improved clarity and potentially refactored for better readability. Key improvements would be:

   • Clearer comments explaining the purpose of each function.
   • Error handling for API calls.
   • Configuration of API keys via environment variables instead of hardcoding.

6. Stitching Third-Party APIs: Extend the gateway to include third-party GraphQL APIs (e.g., SpaceX API) using the same makeRemoteExecutor and stitchSchemas approach.

7. Deployment to AWS Lambda: Use the Serverless Framework to deploy the gateway to an AWS Lambda function. Install necessary dependencies (serverless, serverless-http, body-parser). Create a serverless.yaml file (as shown in the original). Modify gateway.js to integrate with serverless-http for proper Lambda function handling. Deploy using serverless deploy.

Conclusion

Schema stitching offers a powerful solution for building scalable and maintainable GraphQL APIs from multiple data sources. While this article uses Fauna and AWS Lambda, the principles apply broadly to other GraphQL services and serverless platforms. Alternative approaches like Apollo Federation provide similar functionality.

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