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Evaluating the integration of Esper complex event processing engine and message brokers.

Guadalupe Ortiz1, Adrian Bazan-Muñoz1, Winfried Lamersdorf2

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Summary

This study evaluates Esper Complex Event Processing (CEP) with RabbitMQ and Apache Kafka, finding efficient real-time data processing. Performance varies by architecture, offering guidance for system developers.

Keywords:
Apache KafkaComplex event processingEsperEvaluationInternet of ThingsRabbitMQ

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Area of Science:

  • Computer Science
  • Data Engineering
  • Software Architecture

Background:

  • The proliferation of IoT and smart environments generates vast data, demanding low-latency processing systems.
  • Existing Complex Event Processing (CEP) solutions often lack performance evaluations under varying loads, hindering new deployments.
  • Scalability and fault tolerance in high-throughput data processing remain critical research areas.

Purpose of the Study:

  • To evaluate the performance of Esper CEP engine versions integrated with RabbitMQ and Apache Kafka.
  • To benchmark different software architectures for real-time data processing under increasing data rates.
  • To provide developers with data-driven insights for selecting appropriate CEP and messaging broker solutions.

Main Methods:

  • Defined a benchmark using Esper CEP event patterns and representative operators.
  • Tested three architectures: Esper/RabbitMQ, Esper Enterprise/Kafka (single instance), and Esper Enterprise/Kafka (distributed).
  • Measured CPU, RAM, latency, and throughput at increasing event input rates to identify system overload points.

Main Results:

  • All architectures exhibited low CPU consumption across tested data rates.
  • Memory usage was balanced and similar up to 10,000-15,000 events/second.
  • Efficient response times were observed up to 10,000-15,000 events/second, varying by architecture and pattern.

Conclusions:

  • Esper CEP offers efficient real-time data processing solutions with varying strengths in integration, scalability, and fault tolerance.
  • The choice of CEP engine and messaging broker depends on specific system requirements.
  • Performance benchmarks provide crucial data for informed system design decisions in IoT and smart environments.