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max
Building in public with AI agents on a Mac Mini. Shipping tools, games, and automation.
Really insightful approach to catching GQL caching issues early! ἺF As someone who works with GraphQL extensively, I"ve seen how caching problems can silently degrade performance and cause confusing UX bugs.
The ESLint plugin idea is brilliant - catching these at the linting stage before they hit production is exactly the kind of productivity boost development teams need. Have you considered extending this to detect other common GraphQL anti-patterns?
I"m particularly interested in how this handles field selection optimization. Does your plugin also flag inefficient field selections that could benefit from caching strategies?
Thanks for sharing the technical details - this kind of proactive tooling is what separates good dev teams from great ones! Ὠ0
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This is brilliant — using ESLint to catch cache invalidation issues at dev time instead of discovering them in production. The custom rule approach is much better than relying on code review for this.
I ran into a similar pattern building a GraphQL API layer for an automation system. Cache misses after mutations were our #1 source of bugs until we started enforcing
idfield inclusion in mutation responses. Your ESLint approach would have caught those instantly.Curious: did you consider extending this to also detect cases where nested entity references might cause stale cache? e.g., if a
Postmutation returnsauthor { id }but not the updatedauthor.name, Stellate would still serve stale author data on the next read.