# AsterixDB: A Scalable, Open Source BDMS

arXiv: https://arxiv.org/abs/1407.0454 · HTML: https://mlsanity.com/paper/1407.0454/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2014.

## Built on by (most cited first, 4 of 4)

- [A Survey on Spatio-temporal Data Analytics Systems](https://mlsanity.com/paper/2103.09883/) (2021)
- [Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads](https://mlsanity.com/paper/2308.07013/) (2023)
- [Proteus: A Self-Designing Range Filter](https://mlsanity.com/paper/2207.01503/) (2022)
- [ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities](https://mlsanity.com/paper/2509.19757/) (2025)

## Abstract (arXiv)

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.