Real-Time Analytics, Hybrid Transactional/Analytical Processing, In-Memory Data Management, and Non-Volatile Memory

Abstract

These days, real-time analytics is one of the most often used notions in the world of databases. Broadly, this term means very fast analytics over very fresh data. Usually the term comes together with other popular terms, hybrid transactional/analytical processing (HTAP) and in-memory data processing. The reason is that the simplest way to provide fresh operational data for analysis is to combine in one system both transactional and analytical processing. The most effective way to provide fast transactional and analytical processing is to store an entire database in memory. So on the one hand, these three terms are related but on the other hand, each of them has its own right to life. In this paper, we provide an overview of several in-memory data management systems that are not HTAP systems. Some of them are purely transactional, some are purely analytical, and some support real-time analytics. Then we overview nine in-memory HTAP DBMSs, some of which don't support real-time analytics. Existing real-time in-memory HTAP DBMSs have very diverse and interesting architectures although they use a number of common approaches: multiversion concurrency control, multicore parallelization, advanced query optimization, just in time compilation, etc. Additionally, we are interested whether these systems use non-volatile memory, and, if yes, in what manner. We conclude that an emergence of new generation of NVM will greatly stimulate its use in in-memory HTAP systems.