Here's How ShareChat Scaled Their ML Feature Store 1000X Without Scaling the Database

Data Science Tech Brief By HackerNoon

This story was originally published on HackerNoon at: https://hackernoon.com/heres-how-sharechat-scaled-their-ml-feature-store-1000x-without-scaling-the-database.
How ShareChat scaled its ML feature store to 1B features/sec on ScyllaDB, achieving 1000X performance without scaling the database.
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ShareChat scaled its ML feature store from failure at 1M features/sec to 1B features/sec using ScyllaDB optimizations, caching hacks, and relentless tuning. By rethinking schemas, tiling, and caching strategies, engineers avoided scaling the database, cut latency, and boosted cache hit rates—proving performance engineering beats brute-force scaling.