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Batched `.update()` calls using `.in()` based on shared hunger drops to reduce database roundtrips. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
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π‘ What: Replaced the N+1
supabase.update().eq()queries inside thefor (const pet of hungryPets)loop with batched updates using.in(). The pets are grouped by their new computedhunger_levelin a Map, and then a single update query is executed for each unique hunger level usingPromise.all().π― Why: The previous code ran a discrete network query to Supabase for every single pet returned by the
hungryPetsquery. This N+1 problem causes execution time to scale linearly with the number of pets, quickly hitting cron execution timeouts and bogging down connection pools in Supabase. By grouping by targethunger_level, we turn N requests into at mostmax_hunger / hunger_drop(i.e. ~5) requests, executing them in parallel.π Measured Improvement:
Based on a simulated mock test with 1000 items and a generic 10ms latency mock per query:
The performance improvement solves severe potential cron timeouts and Supabase connection pool exhaustion under load, scaling gracefully to tens of thousands of users.
PR created automatically by Jules for task 6481652668908516113 started by @marcin2121