Greenplum: A Hybrid Database for Transactional and Analytical Workloads
Zhenghua Lyu, Huan Hubert Zhang, Gang Xiong, Haozhou Wang, Gang Guo,, Jinbao Chen, Asim Praveen, Yu Yang, Xiaoming Gao, Ashwin Agrawal, Alexandra, Wang, Wen Lin, Junfeng Yang, Hao Wu, Xiaoliang Li, Feng Guo, Jiang Wu, Jesse, Zhang, Venkatesh Raghavan

TL;DR
This paper presents enhancements to Greenplum, transforming it into a hybrid database capable of efficiently handling both transactional (OLTP) and analytical (OLAP) workloads while maintaining ACID properties.
Contribution
The paper introduces novel techniques such as a global deadlock detector, one-phase commit, and a resource group model to support hybrid workloads in Greenplum.
Findings
Improved OLTP performance without degrading OLAP capabilities.
Effective resource contention management between transactional and analytical queries.
Demonstrated scalability and efficiency on TPC-B and CH-benCHmark benchmarks.
Abstract
Demand for enterprise data warehouse solutions to support real-time Online Transaction Processing (OLTP) queries as well as long-running Online Analytical Processing (OLAP) workloads is growing. Greenplum database is traditionally known as an OLAP data warehouse system with limited ability to process OLTP workloads. In this paper, we augment Greenplum into a hybrid system to serve both OLTP and OLAP workloads. The challenge we address here is to achieve this goal while maintaining the ACID properties with minimal performance overhead. In this effort, we identify the engineering and performance bottlenecks such as the under-performing restrictive locking and the two-phase commit protocol. Next we solve the resource contention issues between transactional and analytical queries. We propose a global deadlock detector to increase the concurrency of query processing. When transactions that…
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Taxonomy
TopicsCloud Computing and Resource Management · Distributed systems and fault tolerance · Advanced Database Systems and Queries
