Handling Unknown Event Ordering in Distributed Systems

Handling Events Coming in an Unknown Order

TLDR: When events arrive out of order and you don't know which events to expect, the challenge shifts from simple reordering to determining completeness. The solution involves correlation patterns and workflow aggregation to gather information as it arrives.

Summary:

This article tackles a sophisticated problem in event-driven architecture that goes beyond typical ordering issues. While most discussions focus on scenarios where you know which events to expect but not their order, this piece addresses the more complex situation where you receive events like "ItemRemovedFromCart" without knowing if a corresponding "ItemAddedToCart" event exists or is simply delayed.

The author uses an excellent analogy of a department store with multiple cash registers and queues. Just as you can't predict which customer will be served first across different queues, distributed systems can't guarantee ordering between different modules or services. This fundamental constraint shapes how we must design our event handling strategies.

The core insight revolves around correlation and aggregation patterns. Rather than trying to enforce strict ordering, the approach involves collecting events as they arrive and continuously checking for completeness. The payment verification workflow serves as a practical example where multiple pieces of information from different sources - payment gateway responses, fraud scores, merchant limits - must be correlated before making a final decision.

The workflow pattern described essentially creates a state machine that accumulates evidence over time. Each incoming event updates the current state, and after each update, the system evaluates whether sufficient information exists to proceed with the next action. This approach transforms the ordering problem into a completeness detection problem.

For architecture teams, this pattern is particularly valuable when building systems that integrate multiple external services or when implementing complex business processes that span multiple bounded contexts. The key is designing your aggregates to be resilient to partial information and capable of making progress incrementally rather than requiring all data upfront.

Key takeaways:

• Transform ordering problems into completeness detection problems through correlation patterns
• Use workflow aggregation to collect events incrementally and evaluate readiness after each update
• Design systems to handle partial information gracefully rather than enforcing strict event sequences

Tradeoffs:

• Gain resilience to network delays and service failures but sacrifice immediate consistency and predictable processing times
• Enable parallel processing and better fault tolerance but increase complexity in state management and completeness logic

Link: Handling Events Coming in an Unknown Order

Disclaimer: This article was generated using newsletter-ai powered by claude-sonnet-4-20250514 LLM. While we strive for accuracy, please verify critical information independently.