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Programming efficient distributed, concurrent systems requires new abstractions that go beyond traditional sequential programming. But programmers already have trouble getting sequential code right, so simplicity is essential. The core problem is that low-latency, high-availability access to data requires replication of mutable state. Keeping replicas fully consistent is expensive, so the question is how to expose asynchronously replicated objects to programmers in a way that allows them to reason simply about their code. We propose an answer to this question in our ongoing work designing a new language, Gallifrey, which provides orthogonal replication through restrictions with merge strategies, contingencies for conflicts arising from concurrency, and branches, a novel concurrency control construct inspired by version control, to contain provisional behavior. 

To achieve good performance, modern applications often partition and replicate their state across multiple geographically-distributed nodes. While this approach reduces latency in the common case, it can be challenging for programmers to use correctly, especially in applications that require strong consistency. We show how to achieve strong consistency while avoiding coordination by using predictive treaties, a mechanism that can significantly reduce distributed coordination without losing strong consistency. The central insight behind our approach is that many computations can be expressed in terms of predicates over distributed state that can be partitioned and enforced locally. Predictive treaties improve on previous work by allowing the locally enforced predicates to depend on time. Intuitively, by predicting the evolution of system state, coordination can be significantly reduced compared to static approaches. We implemented predictive treaties in a distributed system that exposes them via an intuitive programming model. We evaluate performance on several benchmarks, including TPC-C, showing that predictive treaties can significantly increase performance by orders of magnitude and can even outperform customized algorithms.