Developers have access to a wide range of storage APIs and functionality in large-scale systems, such as relational databases, key-value stores, and namespaces. However, this diversity comes at a cost: each API is implemented by a complex distributed system that is difficult to develop and operate. Delos amortizes this cost by enabling different APIs on a shared codebase and operational platform. The primary innovation in Delos is a log-structured protocol: a fine-grained replicated state machine executing above a shared log that can be layered into reusable protocol stacks under different databases. We built and deployed two production databases using Delos at Facebook, creating nine different log-structured protocols in the process. We show via experiments and production data that log-structured protocols impose low overhead, while allowing optimizations that can improve latency by up to 100X (e.g., via leasing) and throughput by up to 2X (e.g., via batching).

Consensus-based replicated systems are complex, monolithic, and difficult to upgrade once deployed. As a result, deployed systems do not benefit from innovative research, and new consensus protocols rarely reach production. We propose virtualizing consensus by virtualizing the shared log API, allowing services to change consensus protocols without downtime. Virtualization splits the logic of consensus into the VirtualLog, a generic and reusable reconfiguration layer; and pluggable ordering protocols called Loglets. Loglets are simple, since they do not need to support reconfiguration or leader election; diverse, consisting of different protocols, codebases, and even deployment modes; and composable, via RAID-like stacking and striping. We describe a production database called Delos(1) which leverages virtual consensus for rapid, incremental development and deployment. Delos reached production within 8 months, and 4 months later upgraded its consensus protocol without downtime for a 10X latency improvement. Delos can dynamically change its performance properties by changing consensus protocols: we can scale throughput by up to 10X by switching to a disaggregated Loglet, and double the failure threshold of an instance without sacrificing throughput via a striped Loglet.