In CQRS/ES projects, projections play a vital role in providing efficient read models and query capabilities. Projections are denormalized views of the event stream that capture the state of the system at a given point in time. However, ensuring the accuracy and consistency of projections can be challenging, especially when dealing with idempotency. This article aims to explore the concept of idempotency in the context of projections in CQRS/ES projects. We will delve into the significance of idempotency, discuss common challenges in maintaining idempotency, and present strategies and best practices for handling idempotency in projections.

What is Idempotency?

Idempotency refers to a property or characteristic of an operation or function where multiple identical requests or invocations have the same effect as a single request. In other words, if an idempotent operation is performed multiple times, the result remains the same as if it were performed only once. In the context of software development and systems design, idempotency is an important concept to ensure the consistency and reliability of operations, especially in distributed and fault-tolerant systems. It helps prevent unintended side effects or duplicate actions caused by retries, network issues, or other system failures. An idempotent operation can be retried or repeated without causing any change in the system beyond the initial invocation. For example, if an HTTP PUT request is idempotent, sending the same request multiple times will result in the same state as sending it once. Similarly, applying an idempotent event in an event-sourced system will not alter the system’s state if the event has already been applied.

Role of Idempotency in CQRS / ES Projections

Handling idempotency in CQRS/ES projections is essential for maintaining the accuracy, consistency, and reliability of the read models and query capabilities. Here are some key reasons why idempotency should be addressed in CQRS/ES projections:

• Consistent and Predictable Results: Idempotency ensures that performing the same operation multiple times produces the same outcome. In the context of projections, this means that repeated projection updates won’t introduce inconsistencies or undesired changes to the read models. It provides a consistent and predictable view of the system’s state.

• Error and Failure Resilience: In distributed systems, network issues, failures, or retry mechanisms can lead to duplicated commands or events being processed. By handling idempotency, projections can detect and discard duplicate commands or events, preventing redundant or conflicting updates to the read models. This resilience helps maintain data integrity and avoids unnecessary computation or processing.

How to Handle Idempotency in Projections

• Semantic Idempotency: In order to ensure idempotency in projections, a powerful approach is to leverage event data to prevent duplicate updates. This strategy, allows us to make informed decisions based on the event information. Let’s consider an example using a “CampaignStarted” event and its corresponding event handler

    public class CampaignEventHandlers : IEventHandler<CampaignStarted>
        {
            public void Handle(CampaignStarted @event)
            {
                if (CampaignAlreadyStarted(@event.CampaignId))
                    return;
  
                var campaign = MapToCampaign(@event);
                _db.Campaigns.Add(campaign);
            }
        }
  

  Semantic idempotency enables us to determine whether a campaign has already been stored in our read model by examining the campaign ID provided in the event. The event data acts as a reliable source of truth, guiding the decision-making process and ensuring that only relevant changes are applied. It’s worth noting that the implementation of semantic idempotency may vary depending on the specific needs of your CQRS/ES project. However, the key principle remains the same: utilizing event data to determine whether an operation should be performed, thus preventing unnecessary updates.

• De-Duplicating using message identifier: Handling idempotency in projections can also be accomplished by utilizing a message’s unique identifier. One approach involves checking if an event has already been processed by performing a lookup in a “ProcessedEvents” table. By implementing this strategy, we can effectively prevent duplicate event handling. A decorator pattern can be utilized to apply this approach across multiple event handlers.

    public class IdempotentEventHandlerDecorator<T> : IEventHandler<T>
        {
            private readonly IEventHandler<T> _decoratedEventHandler;
  
            public IdempotentEventHandlerDecorator(IEventHandler<T> decoratedEventHandler)
            {
                _decoratedEventHandler = decoratedEventHandler;
            }
  
            public void Handle(T @event)
            {
                if (EventAlreadyProcessed(@event.Id))
                    return;
  
                _decoratedEventHandler.Handle(@event);
  
                _db.ProcessedEvents.Add(@event);
            }
        }
  

  In the above implementation, the “IdempotentEventHandlerDecorator” acts as a wrapper around the actual event handler.

Handling Idempotency in Databases without Multi-Document Transaction Support

In situations where the database used for read models does not support multi-document transactions, an alternative approach is required to handle idempotency. One solution is to store the processed event IDs directly within the read models and check for their presence before processing an event. Let’s explore an example using the “CampaignBudgetIncreased” event and its corresponding event handler.

public class CampaignEventHandlers : IEventHandler<CampaignBudgetIncreased>
{
    public void Handle(CampaignBudgetIncreased @event)
    {
        var campaign = GetCampaignById(@event.CampaignId);

        if (campaign.ProcessedEventIds.Contains(@event.Id))
            return;

        campaign.IncreaseBudget(...);

        campaign.ProcessedEventIds.Add(@event.Id);
    }
}

By storing the processed event IDs directly within the read models, this approach enables efficient idempotency checking without relying on multi-document transactions. Each read model maintains its own list of processed event IDs, ensuring that duplicate events are properly detected and avoided during subsequent processing. It’s crucial to ensure that the collection is persisted alongside the campaign read model to maintain the idempotency state across system restarts or database operations.

Conclusion

Idempotency plays a critical role in handling projections within CQRS/ES projects, serving as a foundation for maintaining consistent and reliable read models. By recognizing the challenges and complexities involved in achieving idempotency, developers can employ effective strategies and best practices to overcome these obstacles and build robust and scalable projection systems.