Realtime Content Deduplication at Scale: How We Cut Cost by 86% (sharechat.com)

Ainews247's latest deep dive reveals how a real-time content deduplication pipeline was dramatically optimized to reduce cost by 86%, a significant breakthrough for large-scale AI/ML streaming systems handling massive event volumes. With over 180 million monthly active users generating billions of events per day, the original Node.js and Redis-based deduplication solution faced prohibitive memory and CPU costs, driven by storing enormous state in Redis and Node’s single-threaded limitations. By migrating to Apache Flink, a framework tailored for distributed, stateful stream processing, the team revamped the architecture to balance efficient state management and parallel compute, unlocking key performance gains. Technically, Flink’s hybrid in-memory and local disk state handling replaced costly Redis memory storage, while checkpointing ensured stable fault tolerance. To overcome stability issues from an initially massive 200GB state, they innovated a data model shift—from storing individual (userId, postId) pairs to grouping post IDs by user and hour buckets—cutting state footprint to 15GB and improving checkpoint efficiency. Additional engineering challenges like state restoration, external HTTP enrichment calls, and autoscaling were addressed with state caching and thoughtful system integrations. This work exemplifies how architectural rethinking and state optimization in stream processing can drastically cut cloud costs while maintaining throughput and deduplication quality, offering valuable lessons for AI/ML teams managing noisy, high-volume data streams in real time.