Optimizing Query Performance through Adaptive Indexing in Large-Scale Relational Databases
Keywords:
Adaptive indexing, Database cracking, Query optimization, Relational databases, Self-tuning systems, Performance benchmarkingAbstract
Adaptive indexing has emerged as a promising approach to improve query performance in large-scale relational database systems. Unlike static indexing, which requires upfront knowledge of query workloads, adaptive indexing incrementally builds indexes as queries are executed. This paper explores the core principles, benefits, and trade-offs of adaptive indexing mechanisms such as database cracking and adaptive merging. Using both historical literature and recent benchmarks, we provide an analytical perspective on the evolution and practical impact of adaptive indexing. Results show significant gains in query performance with minimal overhead.
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Copyright (c) 2024 Sharon Lidiya, Calvince Hightham (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
