Evaluating data freshness in large scale replicated databases

Abstract

There is nowadays an increasing need for database replication, as the construction of high performance, highly available, and large-scale applications depends on it to maintain data synchronized across multiple servers. A particularly popular approach, used for instance byFacebook, is the MySQL open source database management system and its built-in asynchronous replication mechanism. The limitations imposed by MySQL on replication topologies mean that data has to go through a number of hops or each server has to handle a large number of slaves. This is particularly worrisome when updates are accepted by multiple replicas and in large systems. It is however difficult to accurately evaluate the impact of replication in data freshness, since one has to compare observations at multiple servers while running a realistic workload and without disturbing the system under test. In this paper we address this problem by introducing a tool that can accurately measure replication delays for any workload and then apply it to the industry standard TPC-C benchmark. This allows us to draw interesting conclusions about the scalability properties of MySQL replication.