architecture-paradigm-event-driven

---
name: architecture-paradigm-event-driven
description: |
  Structure systems around asynchronous, event-based communication to decouple
  producers and consumers for improved scalability and resilience.

  Triggers: event-driven, message queue, pub/sub, asynchronous processing, event bus,
  real-time processing, loose coupling, event choreography, event orchestration

  Use when: real-time or bursty workloads (IoT, trading, logistics), multiple
  subsystems react to same events, system extensibility is high priority

  DO NOT use when: selecting from multiple paradigms - use architecture-paradigms first.
  DO NOT use when: simple request-response patterns suffice.
  DO NOT use when: strong ordering guarantees are critical without careful design.

  Consult this skill when designing event-driven systems or implementing messaging patterns.
version: 1.0.0
category: architectural-pattern
tags: [architecture, event-driven, asynchronous, decoupling, scalability, resilience]
dependencies: []
tools: [message-broker, event-stream-processor, distributed-tracing]
usage_patterns:
  - paradigm-implementation
  - real-time-processing
  - system-extensibility
complexity: high
estimated_tokens: 800
---

# The Event-Driven Architecture Paradigm

## When to Employ This Paradigm
- For real-time or bursty workloads (e.g., IoT, financial trading, logistics) where loose coupling and asynchronous processing are beneficial.
- When multiple, distinct subsystems must react to the same business or domain events.
- When system extensibility is a high priority, allowing new components to be added without modifying existing services.

## Adoption Steps
1. **Model the Events**: Define canonical event schemas, establish a clear versioning strategy, and assign ownership for each event type.
2. **Select the Right Topology**: For each data flow, make a deliberate choice between choreography (e.g., a simple pub/sub model) and orchestration (e.g., a central controller or saga orchestrator).
3. **Engineer the Event Platform**: Choose the appropriate event brokers or message meshes. Configure critical parameters such as message ordering, topic partitions, and data retention policies.
4. **Plan for Failure Handling**: Implement robust mechanisms for handling message failures, including Dead-Letter Queues (DLQs), automated retry logic, idempotent consumers, and tools for replaying events.
5. **Instrument for Observability**: Implement comprehensive monitoring to track key metrics such as consumer lag, message throughput, schema validation failures, and the health of individual consumer applications.

## Key Deliverables
- An Architecture Decision Record (ADR) that documents the event taxonomy, the chosen broker technology, and the governance policies (e.g., for naming, versioning, and retention).
- A centralized schema repository with automated CI validation and consumer-driven contract tests.
- Operational dashboards for monitoring system-wide throughput, consumer lag, and DLQ depth.

## Risks & Mitigations
- **Hidden Coupling through Events**:
  - **Mitigation**: Consumers may implicitly depend on undocumented event semantics or data fields. Publish a formal event catalog or schema registry and use linting tools to enforce event structure.
- **Operational Complexity and "Noise"**:
  - **Mitigation**: Without strong observability, diagnosing failed or "stuck" consumers is extremely difficult. Enforce the use of distributed tracing and standardized alerting across all event-driven components.
- **"Event Storming" Analysis Paralysis**:
  - **Mitigation**: While event storming workshops are valuable, they can become unproductive if not properly managed. Keep modeling sessions time-boxed and focused on high-value business contexts first.