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Conference Paper
Sweet KIWI: Statistics-Driven OLAP Acceleration using Query Column Sets
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- Authors
- Sung-Soo Kim, Taewhi Lee, Moonyoung Chung, Jongho Won
- Issue Date
- 2016-03
- Citation
- International Conference on Extending Database Technology (EDBT) 2016, pp.680-681
- Language
- English
- Type
- Conference Paper
- Abstract
- KIWI is a SQL-on-Hadoop system enabling batch and interactive analytics for big data. In database systems, materialized views, stored pre-computed results for queries, are one of the most commonly used techniques to improve the query processing speed. However, the key challenge in using materialized views is maintaining their freshness as base data changes. This paper introduces a new approach for accelerating OLAP query processing using query workload statistics and query column sets instead of materialized views. We present an architecture of SQL-on-Hadoop system using query column sets of original tables in database. The experimental results demonstrate that our system can provide improved performance by 1.77x on average in terms of TPC-H query processing.
- KSP Keywords
- Big-data, Database systems, Hadoop system, Improved performance, New approach, OLAP query processing, Processing speed, SQL-on-hadoop, TPC-H, interactive analytics, materialized views