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Tuning of Elasticsearch Configuration: Parameter Optimization Through Simultaneous Perturbation Stochastic

Hårek Haugerud1, Mohamad Sobhie1,2, Anis Yazidi1

  • 1Department of Computer Science, OsloMet-Oslo Metropolitan University, Oslo, Norway.

Frontiers in Big Data
|June 1, 2022
PubMed
Summary

This study optimizes Elasticsearch performance by dynamically tuning configuration parameters. The Simultaneous Perturbation Stochastic Approximation method improved data insertion and response times by over 40%.

Keywords:
ElasticsearchSimultaneous Perturbation Stochastic Approximationconfigurationoptimizationparameter tuning

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Area of Science:

  • Computer Science
  • Database Systems
  • Performance Optimization

Background:

  • Elasticsearch is a popular search engine, but default configurations hinder performance with large datasets and limited server resources.
  • Dynamic tuning of Elasticsearch configuration parameters is crucial for improving performance and resource utilization.

Purpose of the Study:

  • To develop an automated method for optimizing Elasticsearch configuration parameters without instance restarts.
  • To enhance Elasticsearch performance, specifically data insertion capacity and system response time.

Main Methods:

  • Application of the Simultaneous Perturbation Stochastic Approximation (SPSA) method for multi-parameter optimization.
  • Implementation of an algorithm that observes performance and adjusts Elasticsearch configurations automatically.

Main Results:

  • Achieved over 40% improvement in combined data insertion capacity.
  • Demonstrated significant enhancement in system response time.

Conclusions:

  • Automated, dynamic configuration tuning of Elasticsearch is feasible and highly effective.
  • The SPSA-based approach offers a practical solution for improving Elasticsearch performance in resource-constrained environments.