Efficiently Paginating GraphQL Queries with Connection Patterns

In this blog post, we will explore the concept of efficiently paginating GraphQL queries with connection patterns. The need for pagination arises when you want to fetch a large dataset from your server and display it in smaller, more manageable chunks. This is important for improving the performance of your application and providing a better user experience. GraphQL offers an elegant way to achieve this using the connection pattern. We'll discuss the basics of GraphQL and the connection pattern, and then walk through examples to help you understand how to implement this in your own projects. By the end of this post, you will have a solid grasp of how to paginate your GraphQL queries efficiently.

Understanding GraphQL

GraphQL is a query language for your API and a runtime for executing those queries against your data. Unlike REST, which exposes a fixed set of endpoints, GraphQL allows the client to request exactly the data it needs and nothing more. This makes it particularly well-suited for modern applications that need to be flexible and responsive to changing data requirements.

GraphQL Basics

A GraphQL API has three fundamental building blocks:

  1. Queries: Used to request data from the API. They are read-only operations and do not modify the data.
  2. Mutations: Used to modify data, such as creating, updating, or deleting records.
  3. Subscriptions: Used to receive real-time updates when data changes.

To interact with a GraphQL API, clients send a query or mutation to the server, specifying the fields they want to receive in the response. The server then processes the request, fetching the data from the underlying data sources and returning it in the requested shape.

The Connection Pattern

The connection pattern is a convention for structuring GraphQL queries and responses to support pagination in a consistent and flexible manner. It was introduced by Facebook's Relay project, but its usefulness has been recognized by the broader GraphQL community, and it is now considered a best practice for pagination.

The Basic Structure

A connection consists of a few key components:

  • Edges: An array of objects representing the individual items in the connection.
  • Node: The actual data for each item in the connection. Each edge contains a node.
  • Cursor: A unique identifier for each edge, used for pagination. Cursors are typically opaque strings, and their internal structure should not be relied upon by clients.
  • PageInfo: An object containing information about the current page, such as whether there are more items to fetch.

When using the connection pattern, your GraphQL schema should expose a field for fetching a connection object, and the object should have fields for edges, nodes, cursors, and pageInfo.

Implementing the Connection Pattern

Let's now implement the connection pattern for a simple blog application. We'll start by defining a GraphQL schema and then move on to writing the server-side code to handle pagination.

Schema Definition

type Query { posts(first: Int, after: String): PostConnection } type PostConnection { edges: [PostEdge] pageInfo: PageInfo } type PostEdge { cursor: String! node: Post! } type Post { id: ID! title: String! content: String! } type PageInfo { endCursor: String hasNextPage: Boolean! }

In this schema, we have a posts query field that accepts two arguments: first and after. The first argument specifies the maximum number of items to return, and the after argument is used to fetch items after a specific cursor.

The PostConnection type has fields for edges and pageInfo. Each edge in the edges array contains a cursor and a node, where the node is of the Post type. Finally,the PageInfo type includes an endCursor and a hasNextPage field, which will help the client determine whether more items are available for fetching.

Server-side Implementation

Now, let's implement the server-side logic to handle the posts query with pagination. For this example, we will use JavaScript and the Apollo Server library, but the concepts can be applied to any language or GraphQL server implementation.

First, we need to create a simple function that will generate a cursor for each post. Cursors are typically base64-encoded strings derived from some property of the item, like its ID or creation timestamp. In this example, we'll use the post's ID.

const encodeCursor = (id) => Buffer.from(id.toString()).toString('base64'); const decodeCursor = (cursor) => parseInt(Buffer.from(cursor, 'base64').toString(), 10);

Next, we'll implement the resolver function for the posts query field. This function will fetch the requested number of posts from the data source and return them as a connection object.

const resolvers = { Query: { posts: async (_, { first, after }, { dataSources }) => { // Fetch posts from the data source const allPosts = await dataSources.postAPI.getAllPosts(); // If an "after" cursor was provided, filter the posts accordingly const startIndex = after ? allPosts.findIndex((post) => post.id === decodeCursor(after)) + 1 : 0; const slicedPosts = allPosts.slice(startIndex, startIndex + first); // Create edges with cursors const edges = slicedPosts.map((post) => ({ cursor: encodeCursor(post.id), node: post, })); // Determine if there are more items to fetch const hasNextPage = startIndex + first < allPosts.length; // Return the connection object return { edges, pageInfo: { endCursor: hasNextPage ? edges[edges.length - 1].cursor : null, hasNextPage, }, }; }, }, };

In this resolver, we fetch all posts from the data source and then filter them based on the provided after cursor if present. We then create the edges array by mapping over the filtered posts and generating a cursor for each one. Finally, we check if there are more items to fetch and return the connection object with the appropriate pageInfo.

Client-side Usage

To query paginated data from the client-side, we'll structure our GraphQL query using the connection pattern. Here's an example of fetching the first 5 posts:

query { posts(first: 5) { edges { cursor node { id title content } } pageInfo { endCursor hasNextPage } } }

To fetch the next set of posts, you can pass the endCursor from the previous response as the after argument:

query { posts(first: 5, after: "some-end-cursor") { edges { cursor node { id title content } } pageInfo { endCursor hasNextPage } } }


Q: Why should I use the connection pattern instead of simple pagination with offsets and limits?

A: The connection pattern offers several advantages over traditional offset and limit-based pagination:

  1. It provides a consistent way to paginate across different types in your schema.
  2. Cursors are more reliable than offsets when dealing with real-time updates, as new items can beinserted or removed without affecting the pagination of existing items.
  3. It allows for efficient fetching of additional data around the current page, such as the previous or next page.

Q: Can I use the connection pattern with bidirectional pagination?

A: Yes, you can easily extend the connection pattern to support bidirectional pagination by adding last and before arguments to your query field, similar to the first and after arguments. You would also need to update your server-side implementation to handle these arguments and adjust the pageInfo object to include information about whether there are previous items to fetch.

Q: How do I implement cursor-based pagination with different data sources, like SQL databases or REST APIs?

A: The implementation details will vary depending on your data source, but the general approach is the same. You need to fetch the items based on the provided cursor and generate a new cursor for each item in the result. For SQL databases, you can use the WHERE and ORDER BY clauses to filter and sort the items based on the cursor. For REST APIs, you may need to fetch all items and then manually filter and sort them in your server-side code.

Q: Can I use the connection pattern for other types of data fetching, like searching or filtering?

A: Yes, the connection pattern can be applied to any type of data fetching operation that involves returning a list of items. You just need to update your schema and server-side implementation to accept the necessary arguments for searching or filtering and return the resulting items as a connection object.

Q: How do I handle real-time updates with the connection pattern?

A: To handle real-time updates, you can use GraphQL subscriptions to listen for changes in your data and then update your client-side state accordingly. You might need to adjust your server-side code to generate and emit events when data changes and implement subscription resolvers that return the updated connection object.


In this blog post, we explored the connection pattern for efficiently paginating GraphQL queries. We discussed the basics of GraphQL and the connection pattern, and walked through examples of how to implement it in your own projects. By adopting the connection pattern in your GraphQL API, you can provide a consistent, flexible, and efficient way for clients to paginate through large datasets, improving the performance of your application and the user experience.

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