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name: architecture-paradigm-event-driven description: |

Triggers: driven, architecture, decoupling, asynchronous, scalability Asynchronous event-based communication to decouple producers/consumers for scalability and resilience.

Triggers: event-driven, message queue, pub/sub, asynchronous processing Use when: real-time workloads or multiple subsystems react to same events DO NOT use when: simple request-response patterns suffice. 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

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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 production-grade 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 detailed 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.

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag