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Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks.

Prajakta Kalmegh1, Shivnath Babu2, Sudeepa Roy1

  • 1Duke University, Durham, North Carolina.

Proceedings. ACM-SIGMOD International Conference on Management of Data
|April 12, 2021
PubMed
Summary
This summary is machine-generated.

Resource interferences in cluster computing cause unpredictable performance. Our inter-Query Contention Analyzer (iQCAR) accurately identifies which concurrent queries cause slowdowns, aiding database administrators.

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Area of Science:

  • Computer Science
  • Database Systems
  • Distributed Computing

Background:

  • Concurrent queries in cluster computing systems lead to resource interferences, causing unpredictable performance and missed Service Level Agreements (SLAs).
  • Diagnosing query performance degradation requires understanding resource contention and attributing slowdowns to specific concurrent queries or external factors.

Purpose of the Study:

  • To introduce an inter-Query Contention Analyzer (iQCAR) for attributing query slowdowns to specific concurrent queries.
  • To provide database administrators with tools to identify the root causes of resource conflicts and performance bottlenecks.

Main Methods:

  • Developed iQCAR, a system that models resource conflicts using a multi-level directed acyclic graph.
  • Implemented iQCAR to analyze inter-query resource interactions in online executions within a selected time window.

Main Results:

  • iQCAR accurately attributes blame for query slowdowns to concurrent queries.
  • The system enables administrators to compare impacts from concurrent queries and identify contentious queries, resources, and hosts.
  • Experiments on Apache Spark using TPC-DS queries demonstrated iQCAR's superior accuracy over overlap-time methods.

Conclusions:

  • iQCAR effectively identifies and quantifies inter-query resource contention, significantly improving the diagnosis of query performance issues.
  • The proposed method offers a more accurate approach to understanding and mitigating performance degradation in cluster computing environments.