Lightweight problem determination in DBMSs using data stream analysis techniques
Problem determination in a database management system can be a difficult task given the complexity of the system and the large amount of data that must be collected and analyzed. Monitoring the system for this data incurs overhead and has a detrimental effect on application performance. As an alternative to the standard practice of storing the performance data and performing offline analysis, we examine an approach where monitoring data is produced as a continuous data stream and data stream mining techniques are applied. We implement this approach as a prototype system called Tempo on IBM DB2®. Tempo implements Top-K analysis, which is a common task performed by database administrators for problem determination. Top-K analysis typically identifies the set of most frequently occurring events, or the highest consumers of system resources. Our experimental evaluation indicates that Tempo is time and space efficient, incurs low overhead, and produces accurate results.
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