vuralserhat86/event_driven

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.

Event Driven v1.1 - Enhanced

🔄 Workflow

Kaynak: Enterprise Integration Patterns & AWS Event-Driven Guide

Aşama 1: Event Design

• [ ] Schema: Event payload'unu (JSON) tanımla ve versiyonla (v1).
• [ ] Granularity: "OrderCreated" (Fat) vs "OrderReference" (Thin) kararını ver.
• [ ] Idempotency: Her event'e unique event_id ekle.

Aşama 2: Architecture Setup

• [ ] Producer: Event fırlatma noktasını belirle (Transaction sonrası?).
• [ ] Broker: Kafka/RabbitMQ/SQS seçimini load/latency ihtiyacına göre yap.
• [ ] Consumer: Hata durumunda (DLQ) retry stratejisini kur.

Aşama 3: Monitoring

• [ ] Tracing: OpenTelemetry ile request zincirini (Producer -> Broker -> Consumer) izle.
• [ ] Lag: Consumer lag süresini monitör et (Alarm kur).

Kontrol Noktaları

| Aşama | Doğrulama | |-------|-----------| | 1 | Event schema değişikliği geriye dönük uyumlu mu? | | 2 | Aynı event iki kere gelirse sistem bozuluyor mu? | | 3 | Sistem çöküp kalktığında kayıp mesaj var mı? |