A Drop-in Middleware for Serializable DB Clustering across Geo-distributed Sites Enrique Saurez1, Bharath Balasubramanian2, Richard Schlichting3 Brendan Tschaen2 Shankaranarayanan Puzhavakath Narayanan,2 Zhe Huang2, Umakishore Ramachandran1 Georgia Institute of Technology1 AT&T Labs - Research2 United States Naval Academy3
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[email protected] ABSTRACT formance needs of clients.1 However, many of these services Many geo-distributed services at web-scale companies still use databases (DBs) like MariaDB [54] and PostgreSQL [50] rely on databases (DBs) primarily optimized for single-site that are primarily optimized for single site deployments even performance. At AT&T this is exemplified by services in the when they have clustering solutions. For eample, in Mari- network control plane that rely on third-party software that aDB Galera [28] synchronous clustering [29] all replicas are uses DBs like MariaDB and PostgreSQL, which do not pro- updated on each commit, which is prohibitively expensive vide strict serializability across sites without a significant across sites with WAN latencies on the order of hundreds performance impact. Moreover, it is often impractical for of milliseconds. Similarly, in PostgreSQL master-slave [49] these services to re-purpose their code to use newer DBs op- clustering, requests from all sites are sent to a single master timized for geo-distribution. In this paper, a novel drop-in replica, compromising on performance and availability. solution for DB clustering across sites called Metric is pre- Although new geo-distributed DBs have been developed sented that can be used by services without changing a single that improve the performance of cross-site transactionality line of code.