Chapter 7: Competing Consumers and Load Balancing

In this section

• 7.1 Understanding Competing Consumers
  • Competing Consumers Pattern: Definition and Benefits
    Explore the Competing Consumers pattern in Event-Driven Architecture, focusing on its definition, mechanism, and benefits such as load distribution, scalability, fault tolerance, and resource optimization.
  • Competing Consumers in Event-Driven Architecture: Use Cases and Applications
    Explore the diverse use cases of the Competing Consumers pattern in Event-Driven Architecture, including high-volume order processing, real-time data ingestion, and more.
  • Implementing Competing Consumers in Event-Driven Architectures
    Explore the implementation of Competing Consumers in Event-Driven Architectures, focusing on messaging brokers, queue structures, stateless consumers, and more.
• 7.2 Load Balancing Strategies
  • Round Robin Load Balancing in Event-Driven Architectures
    Explore the Round Robin load balancing strategy in event-driven architectures, focusing on its implementation, advantages, disadvantages, and practical use cases with RabbitMQ.
  • Least Connections Load Balancing in Event-Driven Architectures
    Explore the Least Connections load balancing strategy in event-driven architectures, its implementation, advantages, disadvantages, and practical examples.
  • Weighted Load Balancing in Event-Driven Architectures
    Explore the concept of Weighted Load Balancing in Event-Driven Architectures, its implementation mechanisms, advantages, disadvantages, and practical use cases.
• 7.3 Scaling Consumer Applications
  • Horizontal Scaling Techniques for Event-Driven Architectures
    Explore horizontal scaling techniques to enhance throughput, fault tolerance, and cost-effectiveness in event-driven systems. Learn about auto-scaling, containerization, load balancing, and best practices for scaling consumer applications.
  • Managing Consumer State in Event-Driven Architectures
    Explore strategies for managing consumer state in event-driven systems, focusing on stateless and stateful consumer designs, and leveraging external state stores and idempotent processing.
  • Ensuring Stateless Consumers in Event-Driven Architectures
    Explore the principles and benefits of designing stateless consumers in event-driven architectures, focusing on scalability, fault tolerance, and flexibility.
• 7.4 Monitoring and Optimizing Consumer Performance
  • Key Metrics to Track in Event-Driven Architectures
    Explore essential metrics for monitoring and optimizing consumer performance in event-driven architectures, including message throughput, processing latency, and more.
  • Identifying Bottlenecks in Event-Driven Architectures
    Explore techniques for identifying and resolving bottlenecks in event-driven systems, focusing on queue depth analysis, consumer performance metrics, and more.
  • Tools for Monitoring Consumers: Enhancing Performance and Reliability
    Explore comprehensive tools and strategies for monitoring consumer performance in event-driven architectures, including Prometheus, Grafana, ELK Stack, distributed tracing, and cloud-native solutions.