Simone Sanfratello

Event based cache invalidation in GraphQL

Stop serving stale data. Use event-based invalidation to keep your GraphQL cache instantly up-to-date.

Event based cache invalidation in GraphQL
#1about 2 minutes

Understanding GraphQL resolvers as cacheable functions

GraphQL resolvers can be treated as pure functions, allowing their results to be memoized and cached based on query arguments.

#2about 5 minutes

Comparing time-based and event-based cache invalidation

Time-based invalidation is simple but can serve stale data, whereas event-based invalidation keeps data synced but is more complex to implement.

#3about 4 minutes

Choosing a storage backend and generating cache keys

A cache can use fast in-memory storage for small, frequent data or a shared Redis instance for larger datasets across multiple nodes.

#4about 4 minutes

Using references for targeted cache invalidation

By tagging cache entries with references to the underlying data entities, you can precisely invalidate them when a write event occurs.

#5about 7 minutes

Live demo of setting up a basic GraphQL server

A Fastify server is configured with the Mercurius plugin to serve a GraphQL schema with user and country data from an in-memory database.

#6about 3 minutes

Live demo of implementing a time-based cache policy

The Mercurius Cache plugin is configured with a time-to-live policy for a query, using onHit and onMiss events to verify its effectiveness.

#7about 5 minutes

Live demo of solving stale data with event invalidation

An invalidation function is added to a mutation resolver, which uses references to clear the relevant user data from the cache upon update.

#8about 3 minutes

Exploring advanced features of Mercurius Cache

Mercurius Cache also supports custom key generation, request deduplication, programmatic invalidation with wildcards, and garbage collection for references.

#9about 4 minutes

Q&A on career advice and GraphQL best practices

Common GraphQL pitfalls are discussed, such as neglecting to use DataLoaders to solve the N+1 query problem.

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Matching moments

Using frameworks for automatic cache invalidation
35:10 MIN

Using frameworks for automatic cache invalidation

How Gatsby Cloud's real-time streaming architecture drives <5 second builds

Q&A on cache strategies and dynamic content
32:15 MIN

Q&A on cache strategies and dynamic content

Offline first!

Addressing GraphQL security and implementation challenges
17:32 MIN

Addressing GraphQL security and implementation challenges

GraphQL: Does it replace SQL, REST or Something Else?

Keeping cached data fresh with CDN invalidation
25:30 MIN

Keeping cached data fresh with CDN invalidation

Accelerating Authentication Architecture: Taking Passwordless to the Next Level

Implementing frontend caching in React Native apps
13:39 MIN

Implementing frontend caching in React Native apps

Optimizing Your App for Success: Tips and Techniques for managing slow devices

Q&A on GraphQL, team structure, and vendor software
44:31 MIN

Q&A on GraphQL, team structure, and vendor software

Building high performance and scalable architectures for enterprises

Q&A on caching strategies and implementation details
32:34 MIN

Q&A on caching strategies and implementation details

Advanced Caching Patterns used by 2000 microservices

How GraphQL improves upon REST's limitations
10:26 MIN

How GraphQL improves upon REST's limitations

REST in Peace? What does the API protocol of the future look like? Or do we have it already?

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