This course is about enterprise integration patterns proposed by Apache Camel.

The Camel Enterprise Integration Patterns (EIP) provide the interfaces to communicate with large component-based distributed systems. When designing the best integration solution for message-oriented applications, these patterns help save time and effort.

In this course, you'll learn about all four foundational patterns. You'll be introduced to Error Handling patterns, Deployment patterns, and Configuration patterns. Each pattern is discussed in detail to be used in distinct settings. You'll also gain a deep understanding of how each pattern works under different circumstances, and implement them appropriately when necessary using real-world examples to illustrate the design specifics. Camel EIPs offer advantages over traditional frameworks such as REST or SOAP regarding integration problems within an organization.

By the end of this course, you'll have hands-on practice with valuable interfaces and the simplification processes for integrating applications across an organization's services.

Using Apache Camel with Enterprise Integration Patterns

## Intent

This pattern allows the handling of peak loads and slow-running tasks by introducing temporal decoupling between consumers and producers using message queues.The Load Leveling pattern is also known as the **Decoupled Invocation pattern**.

## Context and problem

Depending on the nature of an application, the Request Load pattern will vary. Unless the application is a batch-based system where the processing flows are triggered at regular intervals with a predictable load, the load on the application will not be evenly distributed. Whether the traffic pattern is publicity-driven, market-driven, or seasonal, there are certain periods, such as early-evening hours, weekends, ends of the quarters, or the holiday season, where the load on the system will reach its peak. One way to cope with this instant load burst and the spike in resource utilization is to add more hardware to cover the peak loads. This is not a cost-effective approach as the resources are not utilized during the rest of the time when the load is not at its peak.


## Forces and solution

Load leveling with queues is a better approach to cope with such unpredictable peak loads. With this approach, a queue is introduced to store requests and decouple the execution from the request submission.

# Intent

This pattern allows the handling of peak loads and slow-running tasks by introducing temporal decoupling between consumers and producers using message queues.The Load Leveling pattern is also known as the **Decoupled Invocation pattern**.

# Context and problem

Depending on the nature of an application, the Request Load pattern will vary. Unless the application is a batch-based system where the processing flows are triggered at regular intervals with a predictable load, the load on the application will not be evenly distributed. Whether the traffic pattern is publicity-driven, market-driven, or seasonal, there are certain periods, such as early-evening hours, weekends, ends of the quarters, or the holiday season, where the load on the system will reach its peak. One way to cope with this instant load burst and the spike in resource utilization is to add more hardware to cover the peak loads. This is not a cost-effective approach as the resources are not utilized during the rest of the time when the load is not at its peak.


# Forces and solution

Load leveling with queues is a better approach to cope with such unpredictable peak loads. With this approach, a queue is introduced to store requests and decouple the execution from the request submission.

Learn the Load Levelling design pattern and its usage.

About This Course

Foundational Patterns

Error Handling Patterns

Deployment Patterns

Conclusion

Load Levelling Pattern

Intent

Context and problem

Forces and solution