A Design of Hybrid Transactional and Analytical Processing Database for Energy Efficient Big Data Queries

A Design of Hybrid Transactional and Analytical Processing Database for Energy Efficient Big Data Queries

연구 분야: Databases

학회: International Conference on Green, Pervasive, and Cloud Computing

With the prominent development of cloud computing and pervasive computing, huge volume of big data is accumulated in an ever-increasing manner. To process such huge volume of big data in an energy efficient manner is a popular topic in both industry and academia area. In this work, we discuss how to implement a hybrid transactional and analytical processing database to provide energy efficient big data processing capability. More specifically, PostgreSQL (PG) database is an excellent solution for handling Online Transactional Processing (OLTP) workloads. For OLTP databases to process Online Analytical Processing (OLAP) queries, the traditional solution is to dump the data from PG to an OLAP database such as Greenplum for further analysis. Such solution faces the challenges of extra energy consumption, data island, data inconsistency, to name a few. Hybrid Transactional and Analytical Processing (HTAP) systems, on the other hand, support running both transactional and analytical processing workloads on the same database, which has been achieved great attention recently. In this work, we propose a design of HTAP database by enhancing the high available OLTP PG clusters to support OLAP workloads, via the massively parallel processing (MPP) architecture. In our MPP PG cluster, the data is not split and each PG server maintains an identical replica of the whole data. Moreover, to speed up the execution efficiency, we split the data into multiple virtual parts and each PG server within the cluster only scan the pre-assigned data partition. A set of experiments on the public TPC-H dataset are conducted to evaluate the feasibility of our proposal.