arXiv:1805.08520cs.DB
MonetDBLite: An Embedded Analytical Database
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
While traditional RDBMSes offer a lot of advantages, they require significant effort to setup and to use. Because of these challenges, many data scientists and analysts have switched to using alternative data management solutions. These alternatives, however, lack features that are standard for RDBMSes, e.g. out-of-core query execution. In this paper, we introduce the embedded analytical database MonetDBLite. MonetDBLite is designed to be both highly efficient and easy to use in conjunction with standard analytical tools. It can be installed using standard package managers, and requires no configuration or server management. It is designed for OLAP scenarios, and offers near-instantaneous data transfer between the database and analytical tools, all the while maintaining the transactional guarantees and ACID properties of a standard relational system. These properties make MonetDBLite highly suitable as a storage engine for data used in analytics, machine learning and classification tasks.
Hannes and me developed both MonetDBLite and DuckDB, precisely for the need that you described :) We noticed that there was no easy RDBMS aimed at single-machine analytical workloads, whereas these kind of workloads are very common (e.g. R/Python data science workloads).…
mytherinView on HNParent context