GraphQL, introduced by Facebook in 2015, is a powerful query language for APIs and a runtime for executing those queries against your existing data. When GraphQL is deployed within GenAI on a monolithic application framework, it can bring numerous benefits and several challenges. It is particularly interesting to evaluate how GraphQL works within a monolithic application — a software architecture where the user interface and data access code are combined into a single program from a single platform.
Interaction between monolithic architecture and GraphQL
Monolithic applications are designed as a single, indivisible unit, where application components (such as the database, client-side user interface, and server-side application) are interconnected and interdependent. Each module is designed for a specific operation, but is connected to the others, forming a single, coherent system.
GenAI, an artificial intelligence model, can leverage GraphQL to efficiently access and manipulate data within a monolithic application. By using GraphQL, GenAI can query the specific data it needs to process, reducing the amount of unnecessary data retrieved and improving efficiency.
The working mechanism of GraphQL in monolithic applications
1. Creation of data requests
The process starts when the client, or front end of the monolithic application, sends a request to the server, or back end. This is not just any request; it is a GraphQL query that outlines the structure of the required data, specifying the exact data fields required by the client.
In GraphQL, a query is structured as follows:
graphql
query
user(id: "1")
name
email
friends
name
2. Server-side data aggregation
After receiving this GraphQL query, the server does not pull data from only one endpoint. Instead, it collects the necessary data from the various modules that make up a monolithic application. This is a key difference from REST APIs, which generally require multiple round trips to different endpoints to collect the required data. With GraphQL, the server does all the hard work, making a single, efficient call to retrieve exactly what is needed.
Here is an example of the query above:
javascript
const resolvers =
Query:
user(parent, args, context, info)
return context.db.loadUserByID(args.id);
,
,
User:
friends(user)
return context.db.loadFriendsForUser(user);
,
,
;3. Preparation of the answer and delivery
After the server collects all the necessary data, it creates a response. But instead of sending a generic, predefined object, the server shapes the response to match the structure defined by the original GraphQL query. This ensures that the client gets exactly what they asked for, without any unnecessary or redundant data, thereby reducing network load and improving overall application performance.
After the server has done its job of gathering data, it sends back a response to the client. The server’s response corresponds to the form of the query. For the above request, a possible answer could be:
json
"data":
"user":
"name": "John Doe",
"email": "[email protected]",
"friends": [
"name": "Jane Doe"
,
"name": "Richard Roe"
]
Advantages of using GraphQL in monolithic applications
1. Efficient loading of data
The most significant benefit of using GraphQL in a monolithic application is that it enables accurate, efficient data loading. By allowing the client to determine exactly what data they need, the amount of data that needs to be transferred is minimized, reducing bandwidth usage and improving load times.
2. Reduced server load
Since the server can retrieve all the required data in a single trip, the overall load on the server is reduced, leading to improved performance.
3. Improved developer experience
GraphQL provides more efficient data querying and better performance, leading to a superior developer experience. Its type system helps ensure that the application is built correctly from the start, reducing errors and bugs.
In conclusion, introducing GraphQL into a monolithic application provides more efficient, and integrating GraphQL within GenAI on a monolithic application framework can improve efficiency, performance, and developer experience. However, complex query, error and security management needs to be carefully considered.