MongoDB is a distributed database that supports replication and horizontal partitioning (sharding). MongoDB replica sets consist of a primary that accepts all client writes and then propagates those writes to the secondaries. Each member of the replica set contains the same set of data. For horizontal partitioning, each shard (or partition) is a replica set. This paper discusses the design and rationale behind MongoDB's implementation of a cluster-wide logical clock and causal consistency. The design leveraged ideas from across the research community to ensure that the implementation adds minimal processing overhead, tolerates possible operator errors, and gives protection against non-trusted client attacks. While the goal of the team was not to discover or test new algorithms, the practical implementation necessitated a novel combination of ideas from the research community on causal consistency, security, and minimal performance overhead at scale. This paper describes a large scale, practical implementation of causal consistency using a hybrid logical clock, adding the signing of logical time ranges to the protocol, and introducing performance optimizations necessary for systems at scale. The implementation seeks to define an event as a state change and as such must make forward progress guarantees even during periods of no state changes for a partition of data.

Distributed databases offer high availability but can impose high costs on client applications in order to maintain strong consistency at all times. MongoDB is a document oriented database whose replication system provides a variety of consistency levels allowing client applications to select the trade-offs they want to make when it comes to consistency and latency, at a per operation level. In this paper we discuss the tunable consistency models in MongoDB replication and their utility for application developers. We discuss how the MongoDB replication system's speculative execution model and data rollback protocol help make this spectrum of consistency levels possible. We also present case studies of how these consistency levels are used in real world applications, along with a characterization of their performance benefits and trade-offs.