MixT: a language for mixing consistency in geodistributed transactions

Abstract

Programming concurrent, distributed systems is hard—especially when these systems mutate shared, persistent state replicated at geographic scale. To enable high availability and scalability, a new class of weakly consistent data stores has become popular. However, some data needs strong consistency. To manipulate both weakly and strongly consistent data in a single transaction, we introduce a new abstraction: mixed-consistency transactions, embodied in a new embedded language, MixT. Programmers explicitly associate consistency models with remote storage sites; each atomic, isolated transaction can access a mixture of data with different consistency models. Compile-time information-flow checking, applied to consistency models, ensures that these models are mixed safely and enables the compiler to automatically partition transactions. New run-time mechanisms ensure that consistency models can also be mixed safely, even when the data used by a transaction resides on separate, mutually unaware stores. Performance measurements show that despite their stronger guarantees, mixed-consistency transactions retain much of the speed of weak consistency, significantly outperforming traditional serializable transactions.