Integrated approach to recovery and high availability in an updatable, distributed data warehouse

摘要

Any highly available data warehouse will use some form of data replication to ensure that it can continue to service queries despite machine failures. In this thesis, I demonstrate that it is possible to leverage the data replication available in these environments to build a simple yet efficient crash recovery mechanism that revives a crashed site by querying remote replicas for missing updates. My new integrated approach to recovery and high availability, called HARBOR (High Availability and Replication-Based Online Recovery), targets updatable data warehouses and offers an attractive alternative to the widely used log-based crash recovery algorithms found in existing database systems. Aside from its simplicity over log-based approaches, HARBOR also avoids the runtime overhead of maintaining an on-disk log, accomplishes recovery without quiescing the system, allows replicated data to be stored in non-identical formats, and supports the parallel recovery of multiple sites and database objects. To evaluate HARBOR's feasibility, I compare HARBOR's runtime overhead and recovery performance with those of two-phase commit and ARIES, the gold standard for log-based recovery, on a four-node distributed database system that I have implemented.