Transaction Processing (TP) plays a primary role in the design and implementation of IT applications and services. Many TP systems exploit lock-based concurrency control to guarantee atomicity and isolation of transactions that access shared data. In this article, we show that transaction data access patterns, in particular the order of data accesses along the transaction execution, have a noticeable impact on how lock-based concurrency control affects performance. We show that the performance can remarkably change depending on whether transactions, or a percentage of them, access data items following some common ordering rule or not. We investigate on this aspect and its root causes through an analytical modeling approach, and with the evidence of data gathered through both simulation and the execution of real transactional workloads. Finally, we show how the findings of our study can be easily exploited for improving the performance of common transactional workloads.
Di Sanzo, P., Quaglia, F. (2023). On the Effects of Transaction Data Access Patterns on Performance in Lock-based Concurrency Control. IEEE TRANSACTIONS ON COMPUTERS, 1-14 [10.1109/TC.2022.3222084].
On the Effects of Transaction Data Access Patterns on Performance in Lock-based Concurrency Control
Di Sanzo, Pierangelo
;
2023-01-01
Abstract
Transaction Processing (TP) plays a primary role in the design and implementation of IT applications and services. Many TP systems exploit lock-based concurrency control to guarantee atomicity and isolation of transactions that access shared data. In this article, we show that transaction data access patterns, in particular the order of data accesses along the transaction execution, have a noticeable impact on how lock-based concurrency control affects performance. We show that the performance can remarkably change depending on whether transactions, or a percentage of them, access data items following some common ordering rule or not. We investigate on this aspect and its root causes through an analytical modeling approach, and with the evidence of data gathered through both simulation and the execution of real transactional workloads. Finally, we show how the findings of our study can be easily exploited for improving the performance of common transactional workloads.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.