GraphQL is a query language. What is a query language? Simple: Query languages make it possible to query databases and various information systems to retrieve data. For example, you can extract very specific details about specific customers from a massive database using a query language. Say you want to know the last time Customer X purchased Item 1 from your online store. With the right query, you can find that information very quickly.
For example, a MySQL database query could look like this:
SELECT column_to_select FROM table_to_select WHERE certain_conditions_apply;
That’s how you query a MySQL database to retrieve specific data. That command is very much limited to one particular database server (MySQL). And if you run the query wrong, you can wind up with far too much data to comb through. Given the idea of a query is to retrieve specific data, that can become counterproductive.
That’s why back in 2012 Facebook began working on a query language that went beyond database servers and could work with Application Programming Interfaces (APIs). This new query language was created to ensure clients were presented with only the data they requested and nothing else. That query language was GraphQL, which was designed to be fast, flexible, and developer-friendly.
GraphQL is important, not only because it allows you to query an API, but also because it:
- Can be integrated with your applications and services.
- Allows your applications/services to describe the exact type and shape of data required from an API.
- Enables applications to call a single endpoint for a request.
- Is a great fit for complex systems and even microservices.
- Doesn’t suffer from over-fetching or under-fetching.
- Supports validation and type checking out of the box.
- Autogenerates API documentation.
- Is enterprise and production-ready.
- Is very easy to learn.
The over-fetching and under-fetching benefits can’t be overstated. With some other languages, you’ll find that queries will return either too much information or too little. Too much and a bottleneck can occur with a connected application. Too little data and the connected application won’t have the information it needs to continue a process. Fortunately, GraphQL hits the right balance.
How did GraphQL start?
GraphQL was started by Facebook engineers Lee Byron, Dan Schafer, and Nick Schrock. The project began because the Facebook mobile project developers had to work with considerable amounts of nested and interlinked data. Without an efficient means to query, the mobile version of Facebook failed to perform at an acceptable level. Instead, the developers realized they needed to be able to query the exact shape of the data for services like news feed, messaging, posts, comments, and likes.
This problem was made exponentially more complicated, given the massive number of users on the platform. This need led to GraphQL. What the Facebook engineers created was a language that was considerably faster than the competition at communicating with APIs, because it cuts down queries by choosing only select fields. This feature makes it ideal for very complex systems and microservices. And because all queries return a simple and predictable shape, GraphQL allows developers to more easily write specific queries according to need.
In the end, the Facebook developers wanted to get rid of multiple “dumb” endpoints and create a single “smart” endpoint that can accept very complex queries and then hand over the data to clients in whatever shape the client needs.
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How does GraphQL work?
One thing to remember is that GraphQL is a syntax that describes how to ask for data. This syntax, in turn, extracts data from a server and loads it into a client. At the same time, GraphQL allows the client to specify exactly what it needs, makes it easier to aggregate data from numerous sources, and uses a specific type system to describe data.
The GraphQL “layer” exists between the client and the data source, accepting queries from the client, retrieving data from the source, and sending it to the client. This also makes it possible for developers to create a system where multiple clients can simultaneously query GraphQL, which can retrieve the necessary data from the sources. With this type of setup, you’ll find queries are much more efficient, especially at scale.
GraphQL is comprised of a few components:
- Queries – the requests made to the GraphQL layer.
- Resolvers – tells GraphQL how and where to fetch the data.
- Schema – describes the functionality clients can utilize once they are connected to the GraphQL layer.
There are several open-source projects that work with GraphQL, such as:
- Apollo is a server platform that you can use as the GraphQL layer in your system.
- Offix is an offline client that allows mutations and queries to execute, even if a client is unreachable.
- Graphback is a command-line client that makes it possible for developers to generate GraphQL-enabled Node.js servers.
- OpenAPI-to-GraphQL is a command-line client and library that makes it possible to translate APIs that are described by the OpenAPI Specifications into GraphQL.
Conclusion
If your application or service won’t be deployed at a large scale, or can easily work with a single database like MySQL, GraphQL isn’t what you’re looking for. However, if you know you’ll have applications that will depend on APIs for data at scale, GraphQL might well be the perfect language for your application or service.
