Galo: guided automated learning for query workload re-optimization
Fetter Damasio, Guilherme
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Traditional query optimization techniques often fail when logical subtleties in business queries and schemas circumvent them. Query performance problem determination is typically performed manually in consultation with experts through the analysis of query execution plans (QEPs). Galo, a novel graph-based system, is presented in this work. Galo's knowledge base is built on RDF and SPARQL, W3C graph database standards, which is well suited for manipulating and querying over SQL query plans, which are graphs themselves. Galo acts as a third-tier of optimization, after query rewrite and cost-based optimization, as a query-plan rewrite. Galo's knowledge base is also an invaluable tool for database experts to debug query performance issues by tracking to known issues/solutions and refine optimizer with new and better tuned techniques by the development team. An experimental study of the effectiveness of the developed techniques is demonstrated over a synthetic query workload.