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
01:34 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
03:38 MIN

Q&A on cache strategies and dynamic content

Offline first!

Addressing GraphQL security and implementation challenges
03:29 MIN

Addressing GraphQL security and implementation challenges

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

Keeping cached data fresh with CDN invalidation
02:48 MIN

Keeping cached data fresh with CDN invalidation

Accelerating Authentication Architecture: Taking Passwordless to the Next Level

Q&A on GraphQL, team structure, and vendor software
13:52 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
12:03 MIN

Q&A on caching strategies and implementation details

Advanced Caching Patterns used by 2000 microservices

How GraphQL improves upon REST's limitations
02:44 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?

Using caching to manage slow network device responses
02:04 MIN

Using caching to manage slow network device responses

Challenges of building React and React Native apps

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