Simone Sanfratello
Jun 15, 2022
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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