Finding database contention hotspots under large-scale workloads - A big data approach

Database plays an important role in transactional information systems. One significant performance impacting factor is data lock contention in transaction processing. In order to guide better database design, we propose a novel solution to identify contention hotspots displayed in DBMS transaction l...

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Bibliographic Details
Published in2016 IEEE International Conference on Big Data Analysis (ICBDA) pp. 1 - 5
Main Authors An Lei, Ping Sun, Weixiong Rao, Hong Min, Gong Su, Zhenhua Dong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2016
Subjects
Algorithm design and analysis
Clustering algorithms
database contention
Distributed databases
Engines
relation database system
Scalability
Spark
Sparks
transaction log
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Summary:Database plays an important role in transactional information systems. One significant performance impacting factor is data lock contention in transaction processing. In order to guide better database design, we propose a novel solution to identify contention hotspots displayed in DBMS transaction logs. To analyze the large volume of data collected in the transaction log, our solution employees big data engine Spark for better computation performance and scalability. A novel algorithm is also introduced to optimize computation for analyzing hotspots in the distributed cluster. The experimental results from a benchmark OLTP workload demonstrate the effectiveness and high scalability of our solution.
ISBN:1467395900
9781467395908
DOI:10.1109/ICBDA.2016.7509799