Event-Driven Architecture

Master event-driven systems with pub/sub patterns, event sourcing, and scalable asynchronous design for modern applications

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What is Event-Driven Architecture?

Event-driven architecture (EDA) is a design pattern where system components communicate through the production, detection, and consumption of events. This asynchronous approach enables loosely coupled, scalable systems that can respond to real-time changes and handle massive throughput.

Key Characteristics

Asynchronous Communication: Components don't wait for responses
Loose Coupling: Publishers and consumers are decoupled
Scalability: Individual components can scale independently
Real-time Processing: Enables immediate response to events

Event-Driven System Calculator

Event Volume (K events/sec)

1000K/s

Consumer Count

Latency Requirement (ms)

100ms

Topic Partitions

System Metrics

Total Events/sec:1,000,000

Events per Consumer:200000/s

Throughput:976.6 MB/s

Replication Factor:3x

Weekly Storage:576782.2 GB

Scalability Score: 5/100 - Consider more partitions

Core Components of Event-Driven Systems

Event Producers

• Generate and publish events
• Include event metadata and timestamps
• Handle publishing failures gracefully
• Support batching for efficiency

Event Brokers

• Route events between systems
• Provide durability and persistence
• Support multiple delivery patterns
• Handle partitioning and replication

Event Consumers

• Subscribe to relevant event types
• Process events asynchronously
• Handle duplicate and out-of-order events
• Support dead letter queues

Production Implementation

Event-Driven E-commerce System

event_driven_ecommerce.py

Event-Driven Design Patterns

Pub/Sub Pattern

Publishers: Send events without knowing subscribers
Subscribers: Listen for events they care about
Benefits: Loose coupling, scalability
Use Cases: Notifications, real-time updates

Event Sourcing

Concept: Store events instead of current state
Replay: Rebuild state from event history
Benefits: Audit trail, time travel queries
Use Cases: Financial systems, compliance

CQRS

Separation: Read and write models separated
Commands: Change state, generate events
Queries: Read optimized projections
Use Cases: Complex domains, high read/write ratios

Event Streaming

Real-time: Continuous event processing
Ordered: Maintain event sequence
Durable: Events stored for replay
Use Cases: Analytics, ML pipelines

Real-World Examples

Netflix

Microservices Communication

Scale: 700+ microservices, billions of events/day
Pattern: Event-driven choreography
Benefits: Independent deployments, fault isolation
Tools: Apache Kafka, custom event bus

Uber

Real-time Location Tracking

Scale: 15M trips/day, location updates/sec
Pattern: Event streaming for live tracking
Benefits: Real-time matching, ETA updates
Tools: Apache Kafka, Apache Flink

Amazon

Order Processing Pipeline

Scale: Millions of orders/day
Pattern: Event sourcing for order history
Benefits: Audit trails, order replay
Tools: DynamoDB Streams, Lambda

Activity Feed Generation

Scale: 500M+ users, billions of activities
Pattern: Fan-out on write via events
Benefits: Real-time feeds, personalization
Tools: Apache Kafka, Apache Samza

Best Practices

✅ Do

•
Design idempotent consumers - Handle duplicate events gracefully
•
Use correlation IDs - Track events across service boundaries
•
Implement dead letter queues - Handle failed event processing
•
Version your events - Support schema evolution

❌ Don't

•
Rely on event ordering - Networks can reorder messages
•
Put business logic in events - Events should be data, not behavior
•
Create event chains - Avoid complex event dependencies
•
Ignore monitoring - Track event processing metrics

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