event-driven-architecture.md

Event-Driven Architecture Patterns

Introduction

Event-Driven Architecture (EDA) is an architectural pattern where components communicate through events. This document outlines standards and best practices for implementing event-driven architectures across Bayat projects, ensuring scalable, loosely coupled, and resilient systems.

Core Principles

Event-Centricity

• Model business activities and changes as events
• Design systems around event production, detection, and reaction
• Separate event creation from event handling
• Use events as the primary means of component communication
• Store events as the system of record when appropriate

Loose Coupling

• Minimize dependencies between components
• Ensure event producers have no knowledge of consumers
• Allow independent deployment of producers and consumers
• Enable technology heterogeneity
• Support independent scaling of components

Temporal Decoupling

• Enable asynchronous communication patterns
• Remove time dependencies between components
• Buffer events during activity spikes
• Handle consumer unavailability gracefully
• Support catch-up processing of events

Resilience

• Design for partial system availability
• Implement graceful degradation during failures
• Use event sourcing for reliable state reconstruction
• Ensure replay capability for recovery scenarios
• Design components to be eventually consistent

Event Fundamentals

Event Definition

• Event: An immutable record of something that has happened in the system

Event Anatomy

• Unique identifier
• Event type
• Timestamp
• Source identifier
• Payload data
• Metadata (correlation ID, version, etc.)
• Schema identifier

Event Types

• Domain Events: Represent business activities or state changes
• Integration Events: Used for communication between bounded contexts
• System Events: Represent technical or infrastructure occurrences
• Command Events: Request actions to be performed
• Query Events: Request information retrieval

Event Schema Design

• Define clear, consistent schema structures
• Use versioning for schema evolution
• Consider backward/forward compatibility
• Document event purpose and usage
• Include enough context for consumers

Schema Evolution Strategies

• Forward compatibility (new producers, old consumers)
• Backward compatibility (old producers, new consumers)
• Schema registry for version management
• Deprecation periods for schema changes
• Version identifiers in events

Core Patterns

Event Sourcing

• Store state changes as a sequence of events
• Reconstruct state by replaying events
• Maintain complete audit history
• Support point-in-time reconstruction
• Enable alternative projections

Implementation Considerations

• Event store performance characteristics
• Snapshotting for performance optimization
• Handling domain model evolution
• Concurrency control
• State reconstruction process

Command Query Responsibility Segregation (CQRS)

• Separate write operations from read operations
• Optimize command and query models independently
• Use events to update read models
• Support specialized read models for different use cases
• Allow optimized query performance

Implementation Considerations

• Consistency models between read and write sides
• Eventual consistency management
• Read model update strategies
• Caching considerations
• Query optimization techniques

Event-Carried State Transfer

• Include necessary state within events
• Reduce need for additional service calls
• Enable stateless event processing
• Support audit and replay capabilities
• Balance event size with processing efficiency

Saga Pattern

• Coordinate transactions spanning multiple services
• Implement compensating transactions for rollback
• Use events to trigger each step in the saga
• Support long-running business processes
• Handle partial failures gracefully

Implementation Approaches

• Choreography: Services react to events from other services
• Orchestration: Central coordinator manages the process flow
• Hybrid: Combination of choreography and orchestration

Event Communication Patterns

Publish-Subscribe

• Publishers emit events without knowledge of subscribers
• Subscribers receive events they've expressed interest in
• Support many-to-many communication
• Enable runtime subscriber registration/deregistration
• Decouple event producers and consumers

Request-Response over Events

• Implement request-response interactions asynchronously
• Use correlation IDs to match responses with requests
• Set appropriate timeouts for responses
• Handle missing responses gracefully
• Consider alternatives for latency-sensitive operations

Event Streaming

• Process continuous flows of events
• Support real-time analytics and monitoring
• Enable temporal event processing (windows, sessions)
• Implement stateful stream processing when needed
• Provide replay capabilities

Work Queue

• Distribute tasks among multiple worker instances
• Ensure exactly-once processing semantics when required
• Support priority-based processing
• Implement dead letter queues for failed processing
• Monitor queue depths and processing latency

Implementation Patterns

Event Broker Topology

Centralized Broker

┌───────────┐     ┌───────────────┐     ┌───────────┐
│ Producer A│────▶│  Event Broker │────▶│Consumer 1 │
└───────────┘     │               │     └───────────┘
                  │               │
┌───────────┐     │               │     ┌───────────┐
│ Producer B│────▶│               │────▶│Consumer 2 │
└───────────┘     └───────────────┘     └───────────┘

Distributed Broker

┌───────────┐     ┌───────────────┐     ┌───────────┐
│ Producer A│────▶│  Broker Node 1│────▶│Consumer 1 │
└───────────┘     └───────┬───────┘     └───────────┘
                          │
                          ▼
┌───────────┐     ┌───────────────┐     ┌───────────┐
│ Producer B│────▶│  Broker Node 2│────▶│Consumer 2 │
└───────────┘     └───────────────┘     └───────────┘

Multi-Cluster

┌───────────────────────┐     ┌───────────────────────┐
│     Cluster A         │     │     Cluster B         │
│  ┌───────┐  ┌───────┐ │     │ ┌───────┐  ┌───────┐  │
│  │Broker1│──│Broker2│ │     │ │Broker1│──│Broker2│  │
│  └───────┘  └───────┘ │     │ └───────┘  └───────┘  │
└─────────┬─────────────┘     └─────────┬─────────────┘
          │                             │
          └──────────┬──────────────────┘
                     │
                     ▼
          ┌────────────────────┐
          │   Replication     │
          └────────────────────┘

Event Mesh

• Implement distributed event routing fabric
• Support location-transparent event delivery
• Enable multi-protocol event communication
• Implement intelligent routing based on content
• Support cross-datacenter event distribution

Event Gateway

• Provide entry point for external event sources
• Implement protocol translation
• Validate and transform incoming events
• Apply security policies
• Support event throttling and rate limiting

Implementation Guidance

Event Design

Event Naming Conventions

• Use past tense verbs (e.g., OrderCreated, PaymentProcessed)
• Follow consistent naming pattern: [Entity][ActionInPastTense]
• Be specific about what changed
• Use domain language consistently
• Document naming conventions

Payload Design

• Include only necessary data
• Use well-defined data types
• Consider performance implications of payload size
• Include identifiers for related entities
• Avoid circular references

Event Versioning

• Include version information in events
• Define versioning strategy (semantic versioning)
• Handle multiple versions of events in consumers
• Document breaking vs. non-breaking changes
• Establish deprecation process for old versions

Consumer Implementation

Consumer Patterns

• Competing Consumer: Multiple instances process events from a shared queue
• Observer: Consumers are notified when events occur
• Event Router: Routes events to specific handlers based on type or content
• Event Processor: Transforms events or enriches them with additional data
• Event Store: Persists events for later retrieval or replay

Idempotent Processing

• Design consumers to handle duplicate events
• Implement deduplication mechanisms
• Use idempotent operations
• Track processed event IDs
• Define retry policies

Error Handling

• Classify errors (transient vs. permanent)
• Implement appropriate retry strategies
• Use dead letter queues for failed processing
• Log detailed error information
• Alert on persistent processing failures

Infrastructure Considerations

Event Store

• Optimize for append-only operations
• Consider partitioning strategies
• Implement appropriate retention policies
• Enable efficient event retrieval
• Scale for expected event volume

Message Brokers

• Select appropriate message delivery guarantees
• Configure message persistence settings
• Implement proper cluster sizing
• Define topic/queue structure
• Monitor broker performance

Scalability

• Implement horizontal scaling for producers and consumers
• Use partitioning for parallel processing
• Consider message ordering requirements
• Design for elasticity based on load
• Implement backpressure mechanisms

Monitoring

• Track event production and consumption rates
• Monitor processing latency
• Alert on queue depth thresholds
• Track failed deliveries and processing
• Implement distributed tracing

Technology Recommendations

Message Brokers

• Apache Kafka: High-throughput distributed streaming platform
• RabbitMQ: Feature-rich message broker supporting multiple protocols
• NATS: Simple, high-performance messaging system
• Amazon SQS/SNS: Managed messaging services
• Azure Service Bus/Event Hubs: Microsoft's managed messaging services
• Google Pub/Sub: Google Cloud's managed messaging service

Event Sourcing Platforms

• EventStoreDB: Purpose-built database for event sourcing
• Apache Kafka: Can serve as an event store with proper configuration
• Axon Server: Specialized event store for Axon Framework
• Chronicle Queue: Ultra-low latency, persisted queue
• PostgreSQL/MySQL: Can implement event sourcing with appropriate schema design

Stream Processing

• Apache Kafka Streams: Lightweight stream processing library
• Apache Flink: Distributed processing engine for streams and batch
• Apache Spark Streaming: Micro-batch processing
• Akka Streams: Reactive stream processing library
• Spring Cloud Stream: Framework for building stream applications

Common Challenges and Solutions

Eventual Consistency

• Set appropriate expectations about data consistency
• Design UIs to handle eventual consistency
• Implement optimistic updates
• Use version vectors or lamport timestamps when needed
• Consider CRDT (Conflict-free Replicated Data Types) for certain use cases

Event Schema Evolution

• Establish schema compatibility guidelines
• Implement schema registries
• Test schema changes against existing consumers
• Maintain event format documentation
• Use schema validation in development environments

Ordering and Causality

• Implement sequence numbers or timestamps
• Use consistent hashing for partitioning when order matters
• Consider causal consistency requirements
• Document ordering guarantees and limitations
• Design for out-of-order event processing when possible

Testing Strategies

• Unit Testing: Test event handlers in isolation
• Integration Testing: Verify producer-consumer interactions
• Event Sourcing Testing: Test state reconstruction
• Chaos Testing: Simulate broker failures and network partitions
• Performance Testing: Verify throughput and latency requirements

Implementation Checklist

• Define event schema standards and versioning strategy
• Select appropriate messaging infrastructure
• Implement event production with proper metadata
• Design consumer error handling and retry policies
• Implement monitoring and alerting
• Define disaster recovery procedures
• Establish testing strategies for event-driven components
• Document event catalog and consumption patterns
• Train team on event-driven architecture principles
• Implement security controls for event communication

Case Studies

E-Commerce Order Processing

Problem: Complex order fulfillment involving multiple systems and services

Solution: Event-driven architecture with the following events:

• OrderCreated
• PaymentProcessed
• InventoryReserved
• ShipmentCreated
• OrderCompleted

Benefits:

• Decoupled fulfillment process steps
• Improved resilience to component failures
• Enhanced visibility through event history
• Simplified scaling of individual components

IoT Sensor Data Processing

Problem: High-volume sensor data requiring real-time processing

Solution: Event streaming architecture with:

• Sensor data as events
• Stream processing for anomaly detection
• Event sourcing for historical analysis
• Multiple specialized projections for different analytics needs

Benefits:

• Scaled to handle millions of events per second
• Enabled real-time monitoring and alerting
• Provided complete audit history
• Supported multiple specialized analytical views

References and Resources

Books

• "Building Event-Driven Microservices" by Adam Bellemare
• "Designing Data-Intensive Applications" by Martin Kleppmann
• "Event Streams in Action" by Alexander Dean and Valentin Crettaz
• "Domain-Driven Design" by Eric Evans
• "Effective Kafka" by Emil Koutanov

Online Resources

• Enterprise Integration Patterns
• Confluent Kafka Documentation
• AWS Event-Driven Architecture
• Microsoft Azure Event-Driven Architecture
• CNCF Cloud Events Specification