Day 50: Processor API - Building ML-Powered Content Recommendations
What We’re Building Today
You’ve mastered the Kafka Streams DSL. Now we’re dropping down to the metal—the Processor API. Think of it like this: the DSL is automatic transmission, great for 95% of cases. The Processor API is manual transmission for when you need precise control over every gear shift.
Today we’re building StreamSocial’s content recommendation engine that processes 50,000 content scoring events per second, maintains per-user ML model state, and delivers personalized recommendations with sub-10ms latency.
What You’ll Master:
Low-level stream processing with direct state store access
Custom state management for ML feature vectors
Real-time content scoring with multi-factor algorithms
Stateful processing patterns used by Netflix and Spotify
Why Netflix, Spotify, and YouTube Use the Processor API
When Spotify needs to track your listening patterns across 80 million songs and generate “Discover Weekly,” the DSL isn’t enough. They need:
Precise State Control: Custom state stores optimized for ML feature vectors, not just key-value pairs Complex Processing Logic: Multi-stage scoring algorithms that don’t fit DSL operators Performance Optimization: Direct control over batching, flushing, and state access patterns Custom Scheduling: Periodic tasks like model retraining that run on your schedule, not Kafka’s
