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Unpredictability in query runtimes is addressed by iQCAR (inter Query Contention Analyzer). This system models inter-query interferences to pinpoint contention sources and their impact on performance.

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Area of Science:

  • Database Systems
  • Distributed Computing
  • Performance Analysis

Background:

  • Shared clusters face unpredictable query runtimes due to resource contention from inter-query interactions.
  • Identifying the root cause of these contentions is crucial for optimizing performance.

Purpose of the Study:

  • Introduce iQCAR (inter Query Contention Analyzer), a system to formally model and diagnose inter-query resource contentions.
  • Provide a framework for attributing blame and understanding the impact of contention on query performance.

Main Methods:

  • Leverage a multi-level directed acyclic graph (iQC-Graph) to model interferences between concurrent queries.
  • Develop an interactive interface for step-wise exploration of query execution stages and resource contention.

Main Results:

  • iQCAR enables identification of top-k contention sources and victims.
  • The system diagnoses high-contention nodes/resources and ranks their performance impact.
  • Users can explore how scheduler rules resolve contentions in subsequent executions.

Conclusions:

  • iQCAR offers a systematic approach to analyze and resolve query runtime unpredictability in shared clusters.
  • The tool facilitates deep exploration and understanding of resource contention dynamics.
  • Effective contention management through iQCAR can significantly improve query performance.