Processing through Data - oriented Execution Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering

Data management technology changes the world we live in by providing efficient access to huge volumes of constantly changing data and by enabling sophisticated analysis of those data. Recently there has been an unprecedented increase in the demand for data management services. In parallel, we have witnessed a tremendous shift in the underlying hardware technology toward highly parallel multicore processors. In order to cope with the increased demand and user expectations, data management systems need to fully exploit abundantly available hardware parallelism. Transaction processing is one of the most important and challenging database workloads and this dissertation contributes to the quest for scalable transaction processing software. Our research shows that in a highly parallel multicore landscape, rather than improving single-thread performance, system designers should prioritize the reduction of critical sections where hardware parallelism increases contention unbounded. In addition, this thesis describes solid improvements in conventional transaction processing technology. New transaction processing mechanisms show gains by avoiding the execution of unbounded critical sections in the lock manager through caching, and in the log manager by downgrading the critical sections to composable ones. More importantly, this dissertation shows that conventional transaction processing has inherent scalability limitations due to the unpredictable access patterns caused by the requestoriented execution model it follows. Instead, it proposes adopting a data-oriented execution model, and shows that transaction processing systems designed around data-oriented execution break the inherent limitations of conventional execution. The data-oriented design paves the way for transaction processing systems to maintain scalability as parallelism increases for the foreseeable future; as hardware parallelism increases, the benefits will only increase. In addition, the principles used to achieve scalability can be generally applied to other software systems facing similar scalability challenges as the shift to multicore hardware continues.