Inferencing Relational Database Tuning Actions with OnDBTuning Ontology

OnDBTuning is a relational database (automatic) tuning ontology. Ontologies are software artifacts that represent specific domain knowledge and can infer new knowledge. However, most cases involve only a formal and static description of concepts. Moreover, as database tuning involves many rules-ofthumb and black-box algorithms, it becomes challenging to describe these inference procedures. This research work first presents the OnDBTuning ontology solution focusing on the inference of tuning actions. Next, we provide an actual implementation using SPARQL Inferencing Notation (SPIN). Finally, we discuss a practical evaluation for index recommendation.

The case for NLP-enhanced database tuning

A large body of knowledge on database tuning is available in the form of natural language text. We propose to leverage natural language processing (NLP) to make that knowledge accessible to automated tuning tools. We describe multiple avenues to exploit NLP for database tuning, and outline associated challenges and opportunities. As a proof of concept, we describe a simple prototype system that exploits recent NLP advances to mine tuning hints from Web documents. We show that mined tuning hints improve performance of MySQL and Postgres on TPC-H, compared to the default configuration.

An Approach to Fine Tuning Database Performance in Application Software

Database tuning is crucial step to enhance the performance of the application software. There are many tools available in Microsoft to evaluate the performance of the stored procedures and identify them. This paper presents a comparative performance of actual and tuned sample stored procedures in SQL Server 2008 R2 of Application software (Microsoft). The results showed there is a marginal gain in the efficiency after the database tuning.

Evaluating Database Self-Tuning Strategies in a Comon Extensible Framework

Database automatic tuning tools are an essential class of database applications for database administrators (DBAs) and researchers. These selfmanagement systems involve recurring and ubiquitous tasks, such as data extraction for workload acquisition and more specific features that depend on the tuning strategy, such as the specification of tuning action types and heuristics. Given the variety of approaches and implementations, it would be desirable to evaluate existing database self-tuning strategies, particularly recent and new heuristics, in a standard testbed. In this paper, we propose a reuseoriented framework approach towards assessing and comparing automatic relational database tuning strategies. We employ our framework to instantiate three customized automated database tuning tools extended from our framework kernel, employing strategies using combinations of different tuning actions (indexes, partial indexes, and materialized views) for various RDBMSs. Finally, we evaluate the effectiveness of these tools using a known database benchmark. Our results show that the framework enabled instantiating useful self-tuning tools for these multiple RDBMSs with low effort by just extending well-defined framework hot-spots. Additionally, the instantiated tools provided significant improvements in execution cost of a query workload generated from benchmark query templates. Our framework is made available as an open-source and extensible testbed for the database research community, thus facilitating the further evaluation of database self-tuning strategies.