OLTP and transactional workloads are increasingly common in computer systems, ranging from e-commerce to warehousing to inventory management. It is valuable to provide priority scheduling in these systems, to reduce the response time for the most important clients, e.g. the "big spenders". Two-phase locking, commonly used in DBMS, makes prioritization difficult, as transactions wait for locks held by others regardless of priority. Common lock scheduling solutions, including non-preemptive priority inheritance and preemptive abort, have performance drawbacks for TPC-C type workloads. The contributions of this paper are two-fold: (i) We provide a detailed statistical analysis of locking in TPC-C workloads with priorities under several common preemptive and non-preemptive lock prioritization policies. We determine why non-preemptive policies fail to sufficiently help high-priority transactions, and why preemptive policies excessively hurt low-priority transactions, (ii) We propose and implement a policy, POW, that provides all the benefits of preemptive prioritization without its penalties.

Transactional workloads are a hallmark of modern OLTP and Web applications, ranging from electronic commerce and banking to online shopping. Often, the database at the core of these applications is the performance bottleneck. Given the limited resources available to the database, transaction execution times can vary wildly as they compete and wait for critical resources. As the competitor is "only a click away", valuable (high-priority) users must be ensured consistently good performance via QoS and transaction prioritization. This paper analyzes and proposes prioritization for transactional workloads in traditional database systems (DBMS). This work first performs a detailed bottleneck analysis of resource usage by transactional workloads on commercial and noncommercial DBMS (IBM DB2, Post-greSQL, Shore) under a range of configurations. Second, this work implements and evaluates the performance of several preemptive and nonpreemptive DBMS prioritization policies in PostgreSQL and Shore. The primary contributions of this work include (i) understanding the bottleneck resources in transactional DBMS workloads and (ii) a demonstration that prioritization in traditional DBMS can provide 2x-5x improvement for high-priority transactions using simple scheduling policies, without expense to low-priority transactions.