Event-driven architecture (EDA) enables systems that scale horizontally, remain loosely coupled, and respond to changes in real-time. As Rishikesh Baidya, our CTO, has implemented through our web development services: "EDA transforms monolithic nightmares into elegant, scalable systems—but only when you understand the patterns and trade-offs."
Understanding Event-Driven Architecture
Core Concepts
Event: An immutable fact that something happened—contains what occurred, when, and relevant data.
Event Producer: Creates and publishes events without knowing who will consume them.
Event Consumer: Subscribes to events and processes them independently.
Event Broker: Infrastructure that routes events from producers to consumers (Kafka, RabbitMQ, etc.).
Event Types
Domain Events
Business-meaningful occurrences in your domain:
{
"eventType": "OrderPlaced",
"eventId": "evt-a1b2c3d4",
"timestamp": "2025-06-28T10:00:00Z",
"version": "1.0",
"payload": {
"orderId": "ord-456",
"customerId": "cust-789",
"items": [{"sku": "WIDGET-01", "quantity": 2}],
"total": 99.99,
"currency": "USD"
}
}Integration Events
- For cross-service communication with explicit contracts:
- Versioned schemas for evolution
- Serializable formats (JSON, Avro, Protobuf)
- Clear ownership and documentation
Technical Events
- System-level signals for observability:
- Health checks and heartbeats
- Metrics and telemetry
- Audit logs and compliance events
Architecture Patterns
Pattern 1: Event Notification
Simple signaling—consumer queries for details:
┌──────────┐ "OrderPlaced" ┌──────────┐
│ Producer │ ─────────────────→ │ Consumer │
└──────────┘ └────┬─────┘
│
▼ Query for order details
┌──────────┐
│ API │
└──────────┘Best for: Minimal coupling, when consumers always need fresh data.
Pattern 2: Event-Carried State Transfer
Event contains all necessary data:
┌──────────┐ {full order data} ┌──────────┐
│ Producer │ ──────────────────────→ │ Consumer │
└──────────┘ │ stores │
│ locally │
└──────────┘Best for: Consumer autonomy, reduced query load, offline capability.
Pattern 3: Event Sourcing
Events as the source of truth—current state derived from event history:
Commands → Domain Logic → Events → Event Store
│
▼
┌─────────────────┐
│ Projections │
│ (Read Models) │
└─────────────────┘Best for: Audit requirements, time-travel debugging, complex domains.
Pattern 4: CQRS (Command Query Responsibility Segregation)
Separate read and write models optimized for their purpose:
Commands ──→ Write Model ──→ Events ──→ Read Model ←── Queries
(Domain) (Denormalized)Best for: High-read systems, complex queries, different scaling needs.
Technology Stack
| Technology | Strengths | Best For |
|---|---|---|
| Apache Kafka | High throughput, replay, partitioning | Event streaming, high volume |
| RabbitMQ | Flexible routing, multiple protocols | Traditional messaging, complex routing |
| AWS EventBridge | Serverless, AWS integration, rules | AWS-native applications |
| Redis Streams | Low latency, simple setup | Real-time, moderate volume |
| NATS | Lightweight, fast, simple | Microservices, edge computing |
Schema Management
Use schema registries for evolution and type safety:
// Avro schema example
{
"type": "record",
"name": "OrderPlaced",
"namespace": "com.example.events",
"fields": [
{"name": "orderId", "type": "string"},
{"name": "customerId", "type": "string"},
{"name": "total", "type": "double"},
{"name": "timestamp", "type": "long", "logicalType": "timestamp-millis"}
]
}Implementation Patterns
Idempotent Consumers
async function handleEvent(event: Event): Promise {
// Check if already processed
const exists = await db.processedEvents.findOne({
eventId: event.eventId
}); if (exists) {
console.log(Event ${event.eventId} already processed, skipping);
return;
}
// Process within transaction
await db.transaction(async (tx) => {
await processEvent(event, tx);
await tx.processedEvents.insert({
eventId: event.eventId,
processedAt: new Date()
});
});
}
Transactional Outbox Pattern
Ensure reliable event publishing with database transactions:
BEGIN TRANSACTION;
-- Business operation
INSERT INTO orders (id, customer_id, total)
VALUES ('ord-123', 'cust-456', 99.99); -- Store event in outbox
INSERT INTO outbox (id, event_type, payload, created_at)
VALUES ('evt-789', 'OrderPlaced', '{"orderId":"ord-123"...}', NOW());
COMMIT;
-- Separate process polls outbox and publishes to broker
-- Then marks events as published
Saga Pattern for Distributed Transactions
Challenges and Solutions
Eventual Consistency
- Mitigation strategies:
- Communicate processing state to users
- Design UIs that handle eventual consistency gracefully
- Use optimistic UI updates with rollback
Event Ordering
Events may arrive out of order across partitions:
// Use sequence numbers for ordering
interface OrderedEvent {
eventId: string;
aggregateId: string;
sequenceNumber: number; // Monotonically increasing per aggregate
payload: unknown;
}// Consumer tracks last processed sequence
async function handleOrderedEvent(event: OrderedEvent) {
const lastSequence = await getLastSequence(event.aggregateId);
if (event.sequenceNumber <= lastSequence) {
return; // Already processed or duplicate
}
if (event.sequenceNumber > lastSequence + 1) {
// Gap detected - events arrived out of order
await bufferForReordering(event);
return;
}
await processEvent(event);
}
Error Handling
- Dead letter queues for failed events
- Exponential backoff retry strategies
- Alerting on DLQ accumulation
- Manual intervention tools for poison messages
- Circuit breakers for downstream failures
Best Practices Checklist
Related Resources
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