Apache Kafka is a scalable publish-subscribe messaging system with its core architecture as a distributed commit log. It was originally built at LinkedIn as its centralized event pipelining platform for online data integration tasks. Over the past years developing and operating Kafka, we extend its log-structured architecture as a replicated logging backbone for much wider application scopes in the distributed environment. In this abstract, we will talk about our design and engineering experience to replicate Kafka logs for various distributed data-driven systems at LinkedIn, including source-of-truth data storage and stream processing.

Linked In is among the largest social networking sites in the world. As the company has grown, our core data sets and request processing requirements have grown as well. In this paper, we describe a few selected data infrastructure projects at Linked In that have helped us accommodate this increasing scale. Most of those projects build on existing open source projects and are themselves available as open source. The projects covered in this paper include: (1) Voldemort: a scalable and fault tolerant key-value store, (2) Data bus: a framework for delivering database changes to downstream applications, (3) Espresso: a distributed data store that supports flexible schemas and secondary indexing, (4) Kafka: a scalable and efficient messaging system for collecting various user activity events and log data.