TY - GEN
T1 - An Optimized Transaction Processing Scheme for Highly Contented E-commerce Workloads Optimized Scheme for Contended Workloads
AU - Zhang, Chunxi
AU - Zhang, Shuyan
AU - Chen, Ting
AU - Zhang, Rong
AU - Liu, Kai
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - High contention frequently explodes in E-commerce scenario when promotions are held. However, modern multi-core main-memory databases cannot achieve ideal performance under high contention. Transactions contending for the same resources must be executed serially in traditional architecture to guarantee correctness, which severely chokes database management systems. In this paper, we propose to optimize the transaction processing scheme for highly contended E-commerce workloads. First, we analyze the characteristics of these workloads in detail. Second, we design to filter ineffective operations at IO layer instead of sending them to executing layer, considering the limited number of items involved in the promotion. Third, we make out a homogeneous operation merging scheme to share database execution resources, e.g., locks, and improve parallelization. We implement a prototype, Filmer, to demonstrate our idea. Filmer launches filtering and merging for contended transactions to make full use of system resources and improve parallelization. Extensive experiments show that filtering and merging improve the throughput by up to 1.95x and 2.55x respectively.
AB - High contention frequently explodes in E-commerce scenario when promotions are held. However, modern multi-core main-memory databases cannot achieve ideal performance under high contention. Transactions contending for the same resources must be executed serially in traditional architecture to guarantee correctness, which severely chokes database management systems. In this paper, we propose to optimize the transaction processing scheme for highly contended E-commerce workloads. First, we analyze the characteristics of these workloads in detail. Second, we design to filter ineffective operations at IO layer instead of sending them to executing layer, considering the limited number of items involved in the promotion. Third, we make out a homogeneous operation merging scheme to share database execution resources, e.g., locks, and improve parallelization. We implement a prototype, Filmer, to demonstrate our idea. Filmer launches filtering and merging for contended transactions to make full use of system resources and improve parallelization. Extensive experiments show that filtering and merging improve the throughput by up to 1.95x and 2.55x respectively.
KW - concurrency control
KW - high contention
KW - transaction processing
UR - https://www.scopus.com/pages/publications/85152231629
U2 - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00189
DO - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00189
M3 - 会议稿件
AN - SCOPUS:85152231629
T3 - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
SP - 1202
EP - 1207
BT - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
Y2 - 18 December 2022 through 20 December 2022
ER -