AsterixDB

Author:

Alsubaiee Sattam1,Altowim Yasser1,Altwaijry Hotham1,Behm Alexander2,Borkar Vinayak1,Bu Yingyi1,Carey Michael1,Cetindil Inci1,Cheelangi Madhusudan3,Faraaz Khurram4,Gabrielova Eugenia1,Grover Raman1,Heilbron Zachary1,Kim Young-Seok1,Li Chen1,Li Guangqiang5,Ok Ji Mahn1,Onose Nicola6,Pirzadeh Pouria1,Tsotras Vassilis7,Vernica Rares8,Wen Jian9,Westmann Till9

Affiliation:

1. University of California, Irvine

2. Cloudera Inc.

3. Google

4. IBM

5. MarkLogic Corp.

6. Pivotal Inc.

7. University of California, Riverside

8. HP Labs

9. Oracle Labs

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.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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1. Competitive Data-Structure Dynamization;ACM Transactions on Algorithms;2024-06-28

2. Optimizing LSM-based indexes for disaggregated memory;The VLDB Journal;2024-06-19

3. Anatomy of the LSM Memory Buffer;Proceedings of the Tenth International Workshop on Testing Database Systems;2024-06-09

4. Benchmarking Learned and LSM Indexes for Data Sortedness;Proceedings of the Tenth International Workshop on Testing Database Systems;2024-06-09

5. Addressing the Nested Data Processing Gap: JSONiq Queries on Snowflake Through Snowpark;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

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