How to use GraphQL Apollo server with Serverless

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Posted on 28.12.2018

Last updated on 12.11.2024

Image by Chor Hung Tsang on Unsplash

Refill!

Step 1: Starter code

Step 2: Install packages

Step 7: Launch the application

Step 8: Playing around

Conclusion

Extra

In the previous article, we have discussed how to use Serverless locally with Webpack and then deploy it to the AWS cloud. Today we talk about how to spin up Apollo GraphQL server inside a lambda function.

GraphQL is a good alternative to REST. It has both upsides and downsides, but useful when dealing with complexly structured, deeply nested rich data. GraphQL allows us to pick only the requested subset of data we need to display client-side in each particular case.

Apollo is one of two widely-used implementations of GraphQL, it includes both server and client, and considered to be a simpler one.

Done with theory.

# Step 1: Starter code

I am going to use a repository from the previous article as starter code here. Feel free to clone it, or come up with something of your own.

So, in case we decided to proceed with the repository, rename folder ./lambda-app to something more relevant, like ./graphql.app

Also, just to be cool, in ./serverless.yml we could change the function name and the endpoint name from “hello” to “graphql”.

Don’t forget to run npm install inside ./graphql.app, if the repository is freshly-cloned.

# Step 2: Install packages

The key packages this time would be:

apollo-server-lambda — a binding for the apollo-server package on serverless
graphql-tag — contains *.graphql file loader for babel
graphql — a peer package for graphql-tag (“peer package” means that graphql-tag is an add-on for graphql and cannot dictate what version of graphql to install)
merge-graphql-schemas — allows to compose different type declarations into one structure, so we can properly split the code

cd ./graphql.app
npm install apollo-server-lambda merge-graphql-schemas axios
npm install graphql-tag graphql --save-dev

The code is licensed under the MIT license

# Step 3: Types

GraphQL is a strictly typed language. It means that it needs to know all data types we are going to use. For demo purposes, we are going to create a simple dataset based on famous https://swapi.co/

mkdir -p ./src/graphql/types

The code is licensed under the MIT license

We create graphql.app/src/graphql/types/index.js file:

import { mergeTypes } from 'merge-graphql-schemas';

import movie from './movie.graphql';
import character from './character.graphql';

export default mergeTypes([movie, character], { all: true });

The code is licensed under the MIT license

Now declare types. There will be two files, one for movies and another — for characters. We will not be digging too deep into the GraphQL syntax, it really goes beyond the scope, but at least we can see how resolvers work, and how to make queries with parameters and without ones.

So,

./graphql.app/src/graphql/types/movie.graphql

type Movie {
    id: String!
    title: String!
    characters: [Character]!
}

type Query {
    movies: [Movie]
    movie(id: String!): Movie!
}

The code is licensed under the MIT license

There are so-called root types in the declaration, it could be Query, Mutation and several other, each is specific for different situations. So, consider the Query type to be an “entry point” of your GraphQL API.

Square brackets mean that an array of values is expected. Exclamation mark means the attribute is mandatory.

A similar file is for characters:

./graphql.app/src/graphql/types/character.graphql

type Character {
    id: String!
    fullName: String!
    movies: [Movie]!
}

type Query {
    character(id: String!): Character!
}

The code is licensed under the MIT license

Note, that *.graphql files are written in a special kind of language, different from JavaScript. In order to import such kind of files, we need a special loader — a plugin for Babel, which reads the file and translates the declared types into an abstract syntax tree (AST) structures. After that, we combine these structures into one big super-structure with merge-graphql-schemas module.

So let’s enable the loader in ./webpack.config.js file. Find the section with module: {rules: […]} and insert a new rule into the array:

{
  test: /\.(graphql|gql)$/,
  exclude: /node_modules/,
  loader: 'graphql-tag/loader',
},

The code is licensed under the MIT license

Now that should work.

# Step 4: Resolvers

In GraphQL each complex type can have its own resolver. Typically, a resolver is an asynchronous function which acquires data from a (usually) remote source. This is a key feature of GraphQL: when a certain data subset is requested, only the corresponding resolvers get executed.

Resolvers are mandatory for root types because the GraphQL server needs to know which code to execute in order to process the request. On the contrary, basic types like String, Int, Boolean and so on do not require a resolver.

mkdir -p ./src/graphql/resolvers

The code is licensed under the MIT license

Just like before, we create ./graphql.app/src/graphql/resolvers/index.js. Inside the file, we also use merge-graphql-schemas to compose different resolvers into one declaration.

import { mergeResolvers } from 'merge-graphql-schemas';
import movieResolver from './movie';
import characterResolver from './character';

const resolvers = [movieResolver, characterResolver];

export default mergeResolvers(resolvers);

The code is licensed under the MIT license

./graphql.app/src/graphql/resolvers/movie.js

export default {
    Query: {
        movies: async (source, args, { dataSources }, state) => {
            return dataSources.movieSource(null);
        },
        movie: async (source, args, { dataSources }, state) => {
            // by using "args" argument we can get access
            // to query arguments
            const { id } = args;

            const result = dataSources.movieSource([id]);
            if (result && result[0]) {
                return result[0];
            }

            return null;
        },
    },

    Movie: {
        characters: async (source, args, { dataSources }) => {
            console.dir('Executing Movie.characters resolver');
            return dataSources.characterSource(source.characters);
        },
    },
};

The code is licensed under the MIT license

A resolver function receives four arguments:

source — an object that contains values from the parent resolver. In case we would like to get characters, the source will contain [1, 2, 3],
args — gives access to query parameters. In case we call for movie, args will be containing id key,
context — an object that is shared between all resolvers within the query, it can contain an authentication token, a dataSources object which we will be passing as a parameter when creating the server,
state — an advanced argument which contains information about the current query. The most interesting part is an AST point of the query the resolver was executed at.

Now, the other one, for characters:

./graphql.app/src/graphql/resolvers/character.js

export default {
    Character: {
        movies: async (source, args, { dataSources }, state) => {
            return dataSources.movieSource(source.movies);
        },
    },
};

The code is licensed under the MIT license

# Step 5: Data sources

Here I would like to illustrate something. As I have already mentioned before, resolvers can get data from different sources, asynchronous or not.

So, let’s make those two data sources, for both movies and characters.

For the movies, the data source would be just a local array.

./graphql.app/src/graphql/dataSources/movie.js

const movies = [
    {
        id: '1',
        title: 'A New Hope',
        characters: [1, 2, 3, 4, 5],
    },
    {
        id: '2',
        title: 'The Empire Strikes Back',
        characters: [1, 2, 3],
    },
    {
        id: '3',
        title: 'Return of the Jedi',
        characters: [30, 31, 45],
    },
];

export default ids => {
    if (ids === null) {
        return movies;
    }

    return movies.filter(movie => ids.indexOf(movie.id) >= 0);
};

The code is licensed under the MIT license

For characters, the data source is a little bit more complex, so it gets data from a remote source and process the result in order to make it suitable for us. We also use super-simple memoization technique there, just to make it work a little bit faster.

./graphql.app/src/graphql/dataSources/character.js

import axios from 'axios';

// such kind of memoization is not good for production
// use it like this only for demo purposes,
// normally it is better to have something
// like Redis here
const cache = {};

const extractId = url => {
    const found = url.match(/(\d+)\/$/);
    if (found.length) {
        return found[1];
    }

    return null;
};

export default async ids => {
    if (!ids || !ids.length) {
        return [];
    }

    const result = [];
    const missing = [];
    ids.forEach(id => {
        // check what we already have in the cache
        if (cache[id]) {
            result.push(cache[id]);
        } else {
            missing.push(id);
        }
    });

    if (missing.length) {
        // still having cache miss? request then!
        (await Promise.all(
            missing.map(id =>
                axios
                    .get(`https://swapi.co/api/people/${id}/`)
                    .catch(() => null),
            ),
        )).forEach(res => {
            // process the result as it does not
            // have an appropriate format
            if (res) {
                const data = res.data;
                const id = extractId(data.url);
                if (id) {
                    const character = {
                        id,
                        fullName: data.name,
                        movies: data.films.map(filmURL => extractId(filmURL)),
                    };

                    // put to the cache
                    cache[character.id] = character;
                    // and to the result
                    result.push(character);
                }
            }
        });
    }

    return result.filter(x => !!x);
};

The code is licensed under the MIT license

# Step 6: The handler

It is time to modify our handler code. Edit the file ./graphql.app/src/index.js like the following:

import { ApolloServer } from 'apollo-server-lambda';
import resolvers from './graphql/resolvers';
import typeDefs from './graphql/types';
import characterSource from './graphql/dataSources/character';
import movieSource from './graphql/dataSources/movie';

// creating the server
const server = new ApolloServer({
    // passing types and resolvers to the server
    typeDefs,
    resolvers,

    // initial context state, will be available in resolvers
    context: () => ({}),

    // an object that goes to the "context" argument
    // when executing resolvers
    dataSources: () => {
        return {
            characterSource,
            movieSource,
        };
    },
});

const handler = (event, context, callback) => {
    const handler = server.createHandler();

    // tell AWS lambda we do not want to wait for NodeJS event loop
    // to be empty in order to send the response
    context.callbackWaitsForEmptyEventLoop = false;

    // process the request
    return handler(event, context, callback);
};

export default handler;

The code is licensed under the MIT license

You may have a question appeared though: “Hmm, but if this code is executed every time the function gets executed, isn’t it too much to start the ApolloServer on each hit?”. Answer: when you start your application and receive a very first hit, the lambda function executes all the code and moves from “cold” state to “warm”. If another hit comes within a range of several seconds, it causes execution of only the handler code, while all the object produced with the local, outer code remain the same. It dramatically improves performance.

# Step 7: Launch the application

All set, spinning up…

npx serverless offline start -r eu-central-1 --noTimeout --port 3000 --host 0.0.0.0

The code is licensed under the MIT license

Hopefully, no error occurred, and our GraphQL Serverless endpoint is now online 🚀. We could also deploy this application into the wild, the process of how to do so is described in the previous article.

# Step 8: Playing around

Apollo server offers a pretty web-interface to play nicely with different queries. To access the interface, just make a GET request to the endpoint:

http://localhost:3000/graphql

First, try to execute a quite complex query, like this one:

query {
    movies {
        title
        characters {
            id
            fullName
        }
    }
}

The code is licensed under the MIT license

You will notice, that in the console a message “Executing Movie.characters resolver” appeared 3 times, once per each movie item. Now, if we try the following query:

query {
    movies {
        id
        title
    }
}

The code is licensed under the MIT license

We got no messages like that anymore because we have not requested “characters”. It means, that resolvers and therefore data sources get executed on-demand, and it definitely adds some flexibility to our code.

You may notice, that when declaring types, we have introduced a cyclic link: Movies → Characters → Movies → Characters →… So, we could also try to make a query like this, and technically it will work!

query {
    movies {
        title
        characters {
            fullName
            movies {
                title
                characters {
                    fullName
                    movies {
                        title
                    }
                }
            }
        }
    }
}

The code is licensed under the MIT license

How cool is that?

What we could also do is to send a parametrized query because we have declared it in our types:

query {
    movie(id: "1") {
        id
        title
        characters {
            fullName
        }
    }
}

The code is licensed under the MIT license

The parameter could be something more complex, than just a string. We could have created a query that accepts objects or arrays, this is useful to make a rich query that can do filtering or sorting based on different criteria.

# Conclusion

Now we know how to set up an Apollo GraphQL server working on lambda and run it locally and in production. We barely scratched the surface of GraphQL world, but this is already a production-ready use-case and could be a nice start.