GraphQL to “Join” APIs

As we move from monolith to microservices architecture, we have a challenge of data sprawl. Earlier the data that used to be present in one single database is now distributed in multiple different data stores (often heterogeneous technologies) exposed externally by an API and you wish you could “join” APIs just like you could join tables in a database schema.

What is GraphQL?

GraphQL is a query language and runtime designed by Facebook for APIs, offering a more efficient and flexible approach compared to traditional REST APIs. It allows clients to specify the exact data they need, preventing over-fetching or under-fetching of information and “join” data from related REST APIs providing integrated data for processing or execution.

Key characteristics of GraphQL include:

1. Data on Demand: Clients define the data structure they require, and the server responds with precisely that information. This contrasts with REST APIs, where servers dictate the response structure. Think of this as an ability to select selected columns from a table in the RDBMS world when you use SQL.
2. Unified Endpoint: GraphQL APIs typically have a single endpoint, simplifying interactions. Clients construct queries to request specific data, eliminating the need for multiple endpoints common in traditional RESTful APIs.
3. Type System: GraphQL utilizes a type system to specify the available data structure in the API. This enhances query validation and facilitates tools like autocompletion and introspection.
4. Introspection: Clients can query the API schema itself to discover available types and fields, easing exploration and understanding. This provides the client with the ability to discover the details of API.
5. Batching and Merging: Multiple queries can be combined and efficiently processed in a single request, optimizing network usage and minimizing round trips.
6. Versioning and Evolution: GraphQL simplifies the evolution of APIs without disrupting existing clients. Clients can request only the fields they need, and changes to the server schema do not impact unaffected clients.

In summary, GraphQL offers a more adaptable and efficient method for clients to interact with APIs, making it particularly well-suited for modern web and mobile applications. Its widespread adoption stems from its advantages in data fetching and user-friendly design.

GraphQL for “data stitching”

GraphQL can be employed to merge and consolidate data from diverse APIs. This capability is often referred to as “schema stitching” or “schema federation.”

Here’s a breakdown:

1. Schema Stitching: GraphQL allows the amalgamation of multiple schemas into a single, cohesive schema. Each API is represented as a distinct GraphQL schema, and a stitching mechanism is used to seamlessly combine them. This empowers clients to request data from different sources in a unified manner.
2. Federation: GraphQL supports a federated approach where each API maintains its own GraphQL schema, and a gateway is responsible for amalgamating them. This is particularly useful in microservices architectures, where various services independently manage their data.
3. Query Resolvers: For each type or field in the combined schema, developers define resolvers that fetch data from the corresponding API or data source. These resolvers act as connectors between the GraphQL schema and the actual data.
4. Data Fetching: When a client sends a query requesting data from multiple APIs, the server orchestrates the retrieval of data from each source, aggregates the results, and sends the consolidated response back to the client.

Utilizing GraphQL in this manner provides a potent means of integrating data from disparate sources, enabling developers to create a unified API tailored to the specific requirements of their applications. This approach enhances flexibility, minimizes over-fetching, and simplifies the client’s interaction with diverse services. It’s crucial, however, to thoughtfully design and manage the schema stitching or federation process to ensure efficient and optimized data retrieval.