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VOLAP: A Scalable Distributed Real-Time OLAP System for High-Velocity Data
- Source :
- IEEE Transactions on Parallel and Distributed Systems. 29:226-239
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2018.
-
Abstract
- This paper presents VelocityOLAP (VOLAP), a distributed real-time OLAP system for high-velocity data. VOLAP makes use of dimension hierarchies, is highly scalable, exploits both multi-core and multi-processor parallelism, and can guarantee serializable execution of insert and query operations. In contrast to other high performance OLAP systems such as SAP HANA or IBM Netezza that rely on vertical scaling or special purpose hardware, VOLAP supports cost-efficient horizontal scaling on commodity hardware or modest cloud instances. Experiments on 20 Amazon EC2 nodes with TPC-DS data show that VOLAP is capable of bulk ingesting data at over 600 thousand items per second, and processing streams of interspersed insertions and aggregate queries at a rate of approximately 50 thousand insertions and 20 thousand aggregate queries per second with a database of 1 billion items. VOLAP is designed to support applications that perform large aggregate queries, and provides similar high performance for aggregations ranging from a few items to nearly the entire database.
- Subjects :
- Distributed database
Computer science
business.industry
Online analytical processing
Serialization
Distributed computing
Aggregate (data warehouse)
Cloud computing
02 engineering and technology
Computational Theory and Mathematics
Hardware and Architecture
SAP HANA
020204 information systems
Signal Processing
Scalability
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Queries per second
business
Subjects
Details
- ISSN :
- 10459219
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Parallel and Distributed Systems
- Accession number :
- edsair.doi...........4432b067a87be709c82957e90952350f
- Full Text :
- https://doi.org/10.1109/tpds.2017.2743072