We currently worked with an API that had to scale to serve multiple regions and millions of users. Introducing caching for such numbers is a common and battle-tested way to reduce costs. With services like Stellate, this is no longer impossible even when using GraphQL.

That is great news, however, also poses some challenges. We need to pay attention to how structure our types so we can fully utilize caching.

How to rethink some GraphQL schema designs so we can have the best of the data-driven paradigm of GQL and have amazing caching capabilities at the same time?

Apollo’s response caching has been rebuilt from the ground up for speed, control, and scale—and it’s easier than ever to use. In this session, you’ll see how to flip on supergraph-level caching with directives and instantly cut latency, all without touching subgraph code.

We’ll get into the good stuff: policy-driven caching, surrogate keys for precise invalidations, and patterns to keep data fresh across a federated graph. You’ll see a live demo of the cache debugger in Apollo Sandbox, complete with expanded metrics and distributed tracing so you actually know what’s going on. We’ll also show off the new invalidation engine—it’s dramatically faster, handles more complex scenarios, and is just plain nicer to work with.

The GraphQL community has come together to standardize how people can build distributed systems with GraphQL as an orchestrator. In this talk I will explain the general idea that we have for GraphQL as an Orchestrator in this space and how the new specification is tackling this. We will look at the progress we have made since last GraphQL Conf in the GraphQL composite schema working group and also get some sneak peaks at our early RFCs and prototypes. I will outline how this new specification is taking the best ideas of existing solutions in the market to make the next big leap towards mainstream adoption. This will allow anyone to build tooling by implementing the spec or parts of the spec that seamlessly integrate with other vendors.

Join Alexis, Principal Engineer at Moonpig, as he explains how they built a scalable Federated GraphQL backend using AWS serverless (Lambda, SNS, SQS, DynamoDB) and a federated architecture. Discover how their event-driven approach and custom caching strategies keep their system fast and adaptable for diverse client needs, along with a sneak peek at future performance improvements.