Abstract
Query performance problem determination is usually performed manually in consultation with experts through the analysis of query plans. However, this is an excessively time consuming, human error-prone, and costly process. GALO is a novel system that automates this process. The tool automatically learns recurring problem patterns in query plans over workloads in an offline learning phase to build a knowledge base of plan rewrite remedies. GALO's knowledge base is built on RDF and SPARQL, which is well-suited for manipulating and querying over SQL query plans, which are graphs themselves. It then uses the knowledge base online to re-optimize queries queued for execution to improve performance, often quite dramatically.
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