支持混合事务和分析处理的数据库管理系统综述

Survey on Database Management Systems Supporting HTAP

数据库管理系统根据应用场景分为事务型(OLTP)系统和分析型(OLAP)系统.随着实时数据分析需求增长,OLTP任务和OLAP任务混合的场景越来越普遍,业界开始重视支持混合事务和分析处理(HTAP)的数据库管理系统.这种HTAP数据库系统除了需要满足高性能的事务处理外,还需要满足实时分析对数据新鲜度的要求.因此,对数据库系统的设计与实现提出了新的挑战.近年来,在工业界和学术界涌现了一批架构多样、技术各异的原型和产品.综述HTAP数据库的背景和发展现状,并且从存储和计算的角度对现阶段的HTAP数据库进行分类.在此基础上,按照从下往上的顺序分别总结HTAP系统在存储和计算方面采用的关键技术.在此框架下介绍各类系统的设计思想、优劣势以及适用的场景.此外,结合HTAP数据库的评测基准和指标,分析各类HTAP数据库的设计与其呈现出的性能与数据新鲜度的关联.最后,结合云计算、人工智能和新硬件技术为HTAP数据库的未来研究和发展提供思路.

Database management systems are divided into transactional(OLTP)systems and analytical(OLAP)systems according to application scenarios.With the growing demand for real-time data analysis and the increasing popularity of mixed OLTP and OLAP tasks,the industry has begun to focus on database management systems that support hybrid transactional/analytical processing(HTAP).An HTAP database system not only needs to meet the requirements of high-performance transaction processing but also supports real-time analysis for data freshness.Therefore,it poses new challenges to the design and implementation of database systems.In recent years,some prototypes and products with diverse architectures and technologies have emerged in industry and academia.This study reviews the background and development status of HTAP databases and classifies current HTAP databases from the perspective of storage and computing.On this basis,this study summarizes the key technologies used in the storage and computing of HTAP systems from bottom to top.Under this framework,the design ideas,advantages and disadvantages,and applicable scenarios of various systems are introduced.In addition,according to the evaluation benchmarks and metrics of HTAP databases,this study also analyzes the relationship between the design of various HTAP databases and their performance as well as data freshness.Finally,this study combines cloud computing,artificial intelligence,and new hardware technologies to provide ideas for future research and development of HTAP databases.