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.

MongoDB is a popular distributed database that supports replication, horizontal partitioning (sharding), a flexible document schema and ACID guarantees on the document level. While it is generally grouped with "NoSQL" databases, MongoDB provides many features similar to those of traditional RDBMS such as secondary indexes, an ad hoc query language, support for complex aggregations, and new as of version 4.0 multi-statement, multi-document ACID transactions. We looked for a well understood OLTP workload benchmark to use in our own system performance test suite to establish a baseline of transaction performance to enable flagging performance regressions, as well as improvements as we continue to add new functionality. While there exist many published and widely used benchmarks for RDBMS OLTP workloads, there are none specifically for document databases. This paper describes the process of adapting an existing traditional RDBMS benchmark to MongoDB query language and transaction semantics to allow measuring transaction performance. We chose to adapt the TPC-C benchmark even though it assumes a relational database schema and SQL, hence extensive changes had to be made to stay consistent with MongoDB best practices. Our goal did not include creating official TPC-C certifiable results, however, every attempt was made to stay consistent with the spirit of the original benchmark specification as well as to be compliant to all specification requirements where possible. We discovered that following best practices for document schema design achieves better performance than using required normalized schema. All the source code used and validation scripts are published in github to allow the reader to recreate and verify our results.