Query performance problem determination is visually 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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机译：通过分析查询计划，在与专家咨询的视觉上执行查询性能问题确定。然而，这是过度耗时的，人类错误，昂贵的过程。 Galo是一种自动化此过程的新系统。该工具在脱机学习阶段中自动了解查询计划中的查询计划中的重复问题模式，以构建计划重写补救措施的知识库。 Galo S知识库是基于RDF和SPARQL构建的，这非常适合于操纵和查询SQL查询计划，这些计划是图形本身。然后，它在线使用知识库来重新优化排队的查询以执行以提高性能，通常很大。
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