How Twitter Built a Massive Machine Learning Pipeline Using Kafka

What this post added

This post details the development of a custom Kafka Streams join DSL variant to address specific requirements of Twitter's machine learning logging pipeline. It describes the custom state store implementations (KeySortedStore, TimeSortedStore, JoinedIndicatorStore) and their data models, as well as the procedures for normal processing and the punctuator for emitting matched records. The focus is on handling feature-label joins with delayed label arrival and maintaining serving order, achieving high throughput for millions of events per second.