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AsterixDB: a scalable, open source BDMS

Proceedings of the VLDB Endowment, Volume 7, Issue 14

Pages 1905 - 1916

https://doi.org/10.14778/2733085.2733096

Published: 01 October 2014 Publication History

Abstract

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.

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Cited By

Jahangiri SCarey MFreytag J(2024)Memory Management in Complex Join Queries: A Re-evaluation StudyProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698565(933-942)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698565
Kaushik SSarkar S(2024)Anatomy of the LSM Memory BufferProceedings of the Tenth International Workshop on Testing Database Systems10.1145/3662165.3662766(23-29)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3662165.3662766
Raman AHuynh ALu JAthanassoulis M(2024)Benchmarking Learned and LSM Indexes for Data SortednessProceedings of the Tenth International Workshop on Testing Database Systems10.1145/3662165.3662764(16-22)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3662165.3662764
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 7, Issue 14

October 2014

244 pages

ISSN:2150-8097

Editors:
H. V. Jagadish
University of Michigan
,
Aoying Zhou
East Normal University, China

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VLDB Endowment

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Published: 01 October 2014

Published in PVLDB Volume 7, Issue 14

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Cited By

Jahangiri SCarey MFreytag J(2024)Memory Management in Complex Join Queries: A Re-evaluation StudyProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698565(933-942)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698565
Kaushik SSarkar S(2024)Anatomy of the LSM Memory BufferProceedings of the Tenth International Workshop on Testing Database Systems10.1145/3662165.3662766(23-29)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3662165.3662766
Raman AHuynh ALu JAthanassoulis M(2024)Benchmarking Learned and LSM Indexes for Data SortednessProceedings of the Tenth International Workshop on Testing Database Systems10.1145/3662165.3662764(16-22)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3662165.3662764
Guo QZhang CZhang SLu J(2024)Multi-model query languages: taming the variety of big dataDistributed and Parallel Databases10.1007/s10619-023-07433-142:1(31-71)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10619-023-07433-1
Wang RGao CWang JKadam PTamerÖzsu MAref W(2024)Optimizing LSM-based indexes for disaggregated memoryThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00863-y33:6(1813-1836)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00778-024-00863-y
Huynh AChaudhari HTerzi EAthanassoulis M(2024)Towards flexibility and robustness of LSM treesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00826-933:4(1105-1128)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00778-023-00826-9
Wang JLu YWang QZhang YShu J(2023)Perseid: A Secondary Indexing Mechanism for LSM-Based Storage SystemsACM Transactions on Storage10.1145/363328520:2(1-28)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1145/3633285
Zhu ZHu XAthanassoulis M(2023)NOCAP: Near-Optimal Correlation-Aware Partitioning JoinsProceedings of the ACM on Management of Data10.1145/36267391:4(1-27)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626739
Margara ACugola GFelicioni NCilloni S(2023)A Model and Survey of Distributed Data-Intensive SystemsACM Computing Surveys10.1145/360480156:1(1-69)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604801
Pavlopoulou CCarey MTsotras V(2023)Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management SystemsACM SIGMOD Record10.1145/3604437.360446052:1(104-113)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1145/3604437.3604460
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