Building a GraphQL API with Apollo-server
Have you ever wondered what it takes to build a GraphQL API? It might seem like a complicated and daunting task, but with Apollo-server, it can be a breeze.
In this article, we’ll take a look at the components required for building a GraphQL API, how to define a GraphQL schema and its Query type, and how to define resolvers to implement API operations. We’ll also provide an example of starting up an Apollo server and making an HTTP request using Axios.
Components required for building a GraphQL API
Before we dive into actually building the API, it’s important to understand what components are required. Two crucial components are the schema and resolvers.
The schema defines the types of data that can be queried, and the resolvers provide the actual implementation of the queries. These components work together to define the structure of your API and what it can do.
Defining a GraphQL schema and its Query type
The GraphQL schema is the backbone of your API. It defines the types of data that can be queried, and the shape of those queries.
The query type is a special type that defines the root of all queries. It’s what allows clients to query and retrieve data from your API.
Here’s an example of what a GraphQL schema and query type might look like:
“`
type Query {
hello: String
}
“`
In this example, we define a single query called “hello” that returns a string. This is a very simple example, but it illustrates the basic structure of a schema and query type.
Defining resolvers to implement API operations
While the schema defines the shape of your data and queries, the resolvers provide the actual implementation of those queries. Resolvers are functions that receive a specific query and return the data that should be returned.
For example, the resolver for our “hello” query might look like this:
“`
const resolvers = {
Query: {
hello: () => ‘Hello, world!’
}
};
“`
This resolver simply returns the string “Hello, world!” when the “hello” query is requested. Of course, your resolvers will likely be much more complex than this, but this simple example should give you an idea of how resolvers work.
Example of starting up an Apollo server and making an HTTP request using Axios
Now that we’ve defined our schema and resolvers, it’s time to start up our Apollo server. Here’s an example of how to do that:
“`
const { ApolloServer } = require(‘apollo-server’);
const typeDefs = require(‘./schema’);
const resolvers = require(‘./resolvers’);
const server = new ApolloServer({ typeDefs, resolvers });
server.listen().then(({ url }) => {
console.log(`Server running at ${url}`);
});
“`
In this example, we first import the required components (ApolloServer, typeDefs, and resolvers), then create a new Apollo server instance with those components.
Finally, we start the server and log the URL that it’s listening on. Once our server is running, we can make HTTP requests to it using a tool like Axios.
Here’s an example of how to make a query to our “hello” query using Axios:
“`
axios.post(‘http://localhost:4000’, {
query: `
query {
hello
}
`
}).then(res => {
console.log(res.data);
});
“`
In this example, we’re making a POST request to our server at http://localhost:4000. We’re passing in a GraphQL query as the request body, then logging the response data once the request is complete.
Again, this is a very simple example, but it illustrates how you can make requests to your API once it’s up and running.
Mutations in Apollo-server
So far, we’ve only talked about queries, but what about mutations? Mutations are another important aspect of GraphQL, and Apollo-server provides a way to define them as well.
Definition of mutations in GraphQL
Mutations allow clients to change data on the server, as opposed to just querying it. They’re essentially the write counterpart to queries.
In GraphQL, mutations are defined using a special mutation type.
Mutation type in Apollo and its resolvers
Just like the query type, the mutation type in Apollo defines the root of all mutation operations. Here’s an example of what a mutation type might look like:
“`
type Mutation {
addUser(name: String!, email: String!, password: String!): User!
}
“`
In this example, we define a mutation called “addUser” that accepts three arguments (name, email, and password) and returns a User object.
The exclamation marks on the argument types indicate that they’re required. Just like with queries, we need to define resolvers to implement our mutations.
Here’s an example of what a resolver for our “addUser” mutation might look like:
“`
const resolvers = {
Mutation: {
addUser: (_, { name, email, password }) => {
const newUser = { id: 1, name, email, password };
users.push(newUser);
return newUser;
}
}
};
“`
In this example, we’re defining a resolver for our “addUser” mutation. It accepts three arguments (name, email, and password), creates a new user object, and adds that object to our list of users.
Finally, the resolver returns the new user object.
Conclusion
In conclusion, building a GraphQL API with Apollo-server doesn’t have to be complicated. By understanding the components required (schema and resolvers), and by defining those components correctly, you can build a powerful API that can be queried using tools like Axios.
And by incorporating mutations, you can allow clients to write data to your server as well. With these tools at your disposal, building a GraphQL API has never been easier.
In this article, we discussed how to build a GraphQL API using Apollo-server. The components required for building a GraphQL API are a schema and resolvers, which work together to define the structure of your API.
We also discussed how to define a GraphQL schema, the Query type, and resolvers to implement API operations. An example of starting up an Apollo server and making an HTTP request using Axios was also provided.
Lastly, we talked about the importance of mutations in GraphQL, and how Apollo-server provides a way to define them through a mutation type. Building a GraphQL API might have seemed complicated at first, but with Apollo-server, it becomes a breeze.
With this information, you can build a powerful API that can be queried and offer even more features with mutations.