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World Congress 2022 β€’ Jun 15, 2022

Event based cache invalidation in GraphQL

Simone Sanfratello

Simi San Fratello reveals the ultimate fix for stale GraphQL data. Master true event-based cache invalidation using Redis and Mercurius to keep interactive apps perfectly synchronized.

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#1 about 3 min

Introduction to GraphQL resolvers and the Mercurius ecosystem

Treating GraphQL resolvers as pure functions provides a foundation for effective memoization.

#2 about 4 min

Balancing time-based expiration and event-based caching strategies

Comparing scheduled expiration against write-event triggers reveals different tradeoffs for data consistency.

#3 about 2 min

Selecting optimal caching approaches based on data volatility

Static reference lists suit scheduled refreshes while live application settings require immediate event updates.

#4 about 4 min

Comparing in-memory and Redis storage for cache scalability

Redis enables shared caching across multiple application instances while in-memory storage serves lightweight repetitive entries.

#5 about 4 min

Targeting cache entries using reference indexes in Mercurius

Linking serialization keys to entity references allows precise cache invalidation upon data writes.

#6 about 7 min

Setting up a baseline GraphQL API in Fastify

Defining schemas and basic resolvers establishes the necessary infrastructure for testing cache implementations.

#7 about 5 min

Configuring default caching policies and storage in Mercurius

Applying specific expiration durations and storage types at the query level isolates caching behaviors.

#8 about 4 min

Implementing event-based cache invalidation for updated mutations

Returning referenced entity identifiers from mutation resolvers successfully clears stale associated records.

#9 about 4 min

Optimizing concurrent requests via asynchronous cache deduplication

Resolving duplicated concurrent requests through shared promises stops redundant database queries under load.

#10 about 4 min

Addressing the N+1 problem and partial query caching

Applying dataloaders at the resolver level prevents severe performance bottlenecks when fetching associated nodes.

Matching moments

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Solving data fetching demands with GraphQL

Florian Bader Florian Bader Β· WWC Europe 2026

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Mitigating security risks and expensive computational joins in GraphQL

Gregor Bauer Gregor Bauer Β· WWC 2024

1:28 min

Optimizing performance across JSON methodologies and backend stacks

Alen Pokos Β· LIVE

4:51 min

Answering questions on content cache invalidation and storage

Rowdy Rabouw Rowdy Rabouw Β· WWC 2023

4:41 min

Managing content scale and cache invalidation bottlenecks

Kyle Mathews Β· WWC 2022

1:49 min

Exploring the n+1 query problem with naive resolvers

William Lyon Β· LIVE

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