Zeus: Locality-aware Distributed Transactions

TL;DR

Zeus is a distributed in-memory datastore optimized for local workloads, using dynamic sharding and single-node transactions to achieve high performance, fault tolerance, and simplicity compared to traditional distributed protocols.

Contribution

Introducing Zeus, a system that leverages locality-aware sharding and single-node transactions to improve performance and simplicity over existing distributed transactional systems.

Findings

Zeus can process millions of transactions per second.

It moves 250K objects per second per server.

Outperforms traditional distributed transactions on local workloads.

Abstract

State-of-the-art distributed in-memory datastores (FaRM, FaSST, DrTM) provide strongly-consistent distributed transactions with high performance and availability. Transactions in those systems are fully general; they can atomically manipulate any set of objects in the store, regardless of their location. To achieve this, these systems use complex distributed transactional protocols. Meanwhile, many workloads have a high degree of locality. For such workloads, distributed transactions are an overkill as most operations only access objects located on the same server -- if sharded appropriately. In this paper, we show that for these workloads, a single-node transactional protocol combined with dynamic object re-sharding and asynchronously pipelined replication can provide the same level of generality with better performance, simpler protocols, and lower developer effort. We present Zeus, an in-memory distributed datastore that provides general transactions by acquiring all objects involved in the transaction to the same server and executing a single-node transaction on them. Zeus is fault-tolerant and strongly-consistent. At the heart of Zeus is a reliable dynamic object sharding protocol that can move 250K objects per second per server, allowing Zeus to process millions of transactions per second and outperform more traditional distributed transactions on a wide range of workloads that exhibit locality.