Exploitation of versions for handling updates to shared data

Abstract

A multi-version concurrency control mechanism increases the degree of concurrency between transactions by maintaining multiple versions of data items. This thesis presents studies on using multi-version concurrency control mechanisms to control the consistency of cached data in a client-server database management system environment and to maintain a data warehouse while queries are performed on the data warehouse. The efficiency of algorithms managing data caches has a major impact on the performance of systems that utilize client-side data caching. In these systems, two versions of data can be maintained without additional overhead by exploiting the replication of data in the server``s buffer and clients`` caches. In this thesis, we study the performance implications of versions in these systems. For this purpose, we present a new concurrency control scheme employing versions and a new cache consistency algorithm based on the scheme: Two Version-Callback Locking (2VCBL). We present simulation results that compare 2V-CBL with a leading cache consistency algorithm, Asynchronous Avoidance-based Cache Consistency (AACC). Our experimental results indicate that exploiting versions improves the performance of the systems utilizing client side data caching. 2V-CBL is shown to provide good performance, particularly outperforms AACC when some clients run only read-only transactions. A data warehouse is an integrated database whose data is collected from several data sources, and supports on-line analytical processing (OLAP). Typically, a query to the data warehouse tends to be complex and involves a large volume of data. To keep the data at the warehouse consistent with the source data, changes to the data sources should be propagated to the data warehouse periodically. Because the propagation of the changes (maintenance) is batch processing, it takes long time. Since both query transactions and maintenance transactions are long and involve large volumes of data, tradit...