The study addresses configuration parameter tuning in databases, reviewing frameworks and methodologies including Bayesian optimization, reinforcement learning, and workload characterization for improved performance.
Our analysis highlights the main objectives of database tuning, identifying overhead, adaptivity, and safety as critical constraints that influence the tuning process.
We delve into workload characterization methods, providing an exploration of how to effectively model workloads by examining both query types and runtime metrics.
Through feature pruning, we discuss strategic approaches that can streamline both workload and configuration levels, enhancing the efficiency of the database tuning process.
#database-management-systems #configuration-tuning #bayesian-optimization #reinforcement-learning #workload-characterization
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