Lazy updates for distributed search structure

Abstract

Very large database systems require distributed storage, which means that they need distributed search structures for fast and efficient access to the data. In this paper, we present an approach to maintaining distributed data structures that uses lazy updates , which take advantage of the semantics of the search structure operations to allow for scalable and low-overhead replication. Lazy updates can be used to design distributed search structures that support very high levels of concurrency. The alternatives to lazy update algorithms (eager updates) use synchronization to ensure consistency, while lazy update algorithms avoid blocking. Since lazy updates avoid the use of synchronization, they are much easier to implement than eager update algorithms. We demonstrate the application of lazy updates to the dB-tree, which is a distributed B + tree that replicates its interior nodes for highly parallel access. We develop a correctness theory for lazy updates so that our algorithms can be applied to other distributed search structures.