Principles of Eventual Consistency

Abstract

In globally distributed systems, shared state is never perfect. When communication is neither fast nor reliable, we cannot achieve strong consistency, low latency, and availability at the same time. Unfortunately, abandoning strong consistency has wide ramifications. Eventual consistency, though attractive from a performance viewpoint, is challenging to understand and reason about, both for system architects and programmers. To provide robust abstractions, we need not just systems, but also principles: we need the ability to articulate what a consistency protocol is supposed to guarantee, and the ability to prove or refute such claims.In this tutorial, we carefully examine both the what and the how of consistency in distributed systems. First, we deconstruct consistency into individual guarantees relating the data type, the conflict resolution, and the ordering, and then reassemble them into a hierarchy of consistency models that starts with linearizability and gradually descends into sequential, causal, eventual, and quiescent consistency. Second, we present a collection of consistency protocols that illustrate common techniques, and include templates for implementations of arbitrary replicated data types that are fully available under partitions. Third, we demonstrate that our formalizations serve their purpose of enabling proofs and refutations, by proving both positive results (the correctness of the protocols) and a negative result (a version of the CAP theorem for sequential consistency).