The infrastructure beneath a worldwide social network has to continually serve billions of variable-sized media objects such as photos, videos, and audio clips. These objects must be stored and served with low latency and high throughput by a system that is geo-distributed, highly scalable, and load-balanced. Existing file systems and object stores face several challenges when serving such large objects. We present Ambry, a production-quality system for storing large immutable data (called blobs). Ambry is designed in a decentralized way and leverages techniques such as logical blob grouping, asynchronous replication, rebalancing mechanisms, zero-cost failure detection, and OS caching. Ambry has been running in LinkedIn's production environment for the past 2 years, serving up to 10K requests per second across more than 400 million users. Our experimental evaluation reveals that Ambry offers high efficiency (utilizing up to 88% of the network bandwidth), low latency (less than 50 ms latency for a 1 MB object), and load balancing (improving imbalance of request rate among disks by 8x-10x).

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.