Distributed applications and web services, such as online stores or social networks, are expected to be scalable, available, responsive, and fault-tolerant. To meet these steep requirements in the face of high round-trip latencies, network partitions, server failures, and load spikes, applications use eventually consistent datastores that allow them to weaken the consistency of some data. However, making this transition is highly error-prone because relaxed consistency models are notoriously difficult to understand and test.

Partitioned Global Address Space (PGAS) environments simplify writing parallel code for clusters because they make data movement implicit - dereferencing global pointers automatically moves data around. However, it does not free the programmer from needing to reason about locality - poor placement of data can lead to excessive and even unnecessary communication. For this reason, modern PGAS languages such as X10, Chapel, and UPC allow programmers to express data-layout constraints and explicitly move computation. This places an extra burden on the programmer, and is less effective for applications with limited or data-dependent locality (e.g., graph analytics).