Building KVStore for ML Workloads at Pinterest | Jia Zhan

Pinterest's KVStore, a distributed key-value store, is critical for machine learning feature serving, supporting key functions like home feed, related pins, search, and ad ranking. Jia Zhan, a software engineer at Pinterest, details KVStore's architecture, which accommodates both bulk data uploads from S3 and real-time streaming updates, using ROSDB as its storage engine and Apache Helix for shard management. Challenges include network throttling, addressed by fine-grained S3 rate limiting and traffic pacing, and noisy neighbor problems in the multi-tenant platform, mitigated by rate limiting, quota management, and priority-based load shedding. KVStore handles 170 million requests per second across 28,000 virtual machines, processing 5,000 petabytes of data daily and is evolving to meet demands for near real-time processing, higher throughput, and larger feature sets.