As it becomes increasingly common for transaction processing systems to operate on datasets that fit within the main memory of a single machine or a cluster of commodity machines, traditional mechanisms for guaranteeing transaction durability---which typically involve synchronous log flushes---incur increasingly unappealing costs to otherwise lightweight transactions. Many applications have turned to periodically checkpointing full database state. However, existing checkpointing methods---even those which avoid freezing the storage layer---often come with significant costs to operation throughput, end-to-end latency, and total memory usage.

As applications are becoming increasingly dynamic, the notion that a schema can be created in advance for an application and remain relatively stable is becoming increasingly unrealistic. This has pushed application developers away from traditional relational database systems and away from the SQL interface, despite their many well-established benefits. Instead, developers often prefer self-describing data models such as JSON, and NoSQL systems designed specifically for their relaxed semantics.

As more data management software is designed for deployment in public and private clouds, or on a cluster of commodity servers, new distributed storage systems increasingly achieve high data access throughput via partitioning and replication. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. This article describes Calvin, a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. This allows near-linear scalability on a cluster of commodity machines, without eliminating traditional transactional guarantees, introducing a single point of failure, or requiring application developers to reason about data partitioning. By replicating transaction inputs instead of transactional actions, Calvin is able to support multiple consistency levels—including Paxos-based strong consistency across geographically distant replicas—at no cost to transactional throughput. Furthermore, Calvin introduces a set of tools that will allow application developers to gain the full performance benefit of Calvin's server-side transaction scheduling mechanisms without introducing the additional code complexity and inconvenience normally associated with using DBMS stored procedures in place of ad hoc client-side transactions.

Many distributed storage systems achieve high data access throughput via partitioning and replication, each system with its own advantages and tradeoffs. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. Calvin is a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. Unlike previous deterministic database system prototypes, Calvin supports disk-based storage, scales near-linearly on a cluster of commodity machines, and has no single point of failure. By replicating transaction inputs rather than effects, Calvin is also able to support multiple consistency levels---including Paxos-based strong consistency across geographically distant replicas---at no cost to transactional throughput.