OrpheusDB: Bolt-on Versioning for Relational Databases

Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently store, track, and query across dataset versions. While git and svn are highly effective at managing code, they are not capable of managing large unordered structured datasets efficiently, nor do they support analytic (SQL) queries on such datasets. We introduce ORPHEUSDB, a dataset version control system that “bolts on” versioning capabilities to a traditional relational database system, thereby gaining the analytics capabilities of the database “for free”, while the database itself is unaware of the presence of dataset versions. We develop and evaluate multiple data models for representing versioned data, as well as a light-weight partitioning scheme, LYRESPLIT, to further optimize the models for reduced query latencies. With LYRESPLIT, ORPHEUSDB is on average 10× faster in finding effective (and better) partitionings than competing approaches, while also reducing the latency of version retrieval by up to 20× relative to schemes without partitioning. LYRESPLIT can be applied in an online fashion as new versions are added, alongside an intelligent migration scheme that reduces migration time by 10× on average.