Event Sourcing & CQRS
Master the architectural pattern that stores events as the source of truth with separated command and query models
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📚 Event Sourcing System Calculator
📊 Storage Metrics
Events per Day: 86400K
Total Events: 31536.0M
Event Storage: 45112.6 GB
Snapshot Storage: 0.8 GB
Network Usage: 1.5 MB/s
⚡ Performance Metrics
Query Latency: 10.8 ms
Replay Time: 10.0 ms
Projection Lag: 100 ms
Write Utilization: 10.0%
Consistency Delay: 130 ms
💰 Cost Analysis
Monthly Cost: $4561
⚠️ High storage costs - consider event archival strategy
Event Sourcing Fundamentals
Event Sourcing is an architectural pattern where we store the sequence of events that led to the current state, rather than storing just the current state. The current state is derived by replaying events.
🎯 Core Principles
- Events as Source of Truth: Events are immutable facts
- Temporal Modeling: Time-based view of system changes
- Auditability: Complete history preserved forever
- Replayability: State can be reconstructed at any point
- Append-Only: Events are never modified or deleted
⚡ Key Benefits
- Complete audit trail and history
- Temporal queries (state at any point in time)
- Easy integration with external systems
- Natural fits with event-driven architectures
- Debugging and analytics capabilities
- Future requirements flexibility
CQRS (Command Query Responsibility Segregation)
🔄 Separating Commands and Queries
📝 Command Side (Write)
Purpose: Handle business operations and generate events
Components:
- Command handlers
- Domain aggregates
- Event store
- Business logic validation
Optimized For:
- Consistency and validation
- Transaction integrity
- Business rule enforcement
📖 Query Side (Read)
Purpose: Provide optimized views for queries
Components:
- Projections/View models
- Read databases
- Event handlers
- Query processors
Optimized For:
- Query performance
- Specific UI requirements
- Reporting and analytics
🏗️ CQRS Architecture Flow
1. Command
User Action
→
2. Handler
Business Logic
→
3. Events
Event Store
↓
4. Projections
Event Handlers
→
5. Read Models
Optimized Views
→
6. Queries
UI/API Response
Event Store Implementation
🗄️ Storage Requirements
Append-Only Architecture:
Events are never modified, only appended
Optimistic Concurrency:
Version numbers prevent conflicting writes
Stream-Based Organization:
Events grouped by aggregate ID
Metadata Support:
Timestamps, correlation IDs, causation IDs
📦 Event Structure
{
"eventId": "uuid",
"streamId": "aggregate-123",
"eventType": "OrderPlaced",
"eventVersion": 1,
"streamVersion": 5,
"timestamp": "2024-01-15T10:30:00Z",
"data": {
"orderId": "ord-456",
"customerId": "cust-789",
"amount": 99.99
},
"metadata": {
"correlationId": "corr-123",
"causationId": "cmd-456"
}
}
Projections and Read Models
🔄 Projection Types
Live Projections:
Real-time updates as events arrive
Batch Projections:
Periodic rebuilds from event history
On-Demand Projections:
Built when requested, cached temporarily
Persistent Projections:
Saved read models with checkpointing
📊 Read Model Strategies
Denormalized Views:
Optimized for specific query patterns
Materialized Aggregates:
Pre-computed summary statistics
Temporal Views:
State at specific points in time
Cross-Aggregate Views:
Join data from multiple streams
🔄 Projection Example: Order Summary
Events
OrderPlaced { orderId, customerId, items }
ItemAdded { orderId, itemId, quantity }
OrderShipped { orderId, trackingId }
OrderDelivered { orderId, deliveryDate }
Projection Handler
on OrderPlaced:
createOrderSummary()
on ItemAdded:
updateTotalAmount()
on OrderShipped:
setStatus("Shipped")
on OrderDelivered:
setStatus("Delivered")
Snapshotting Strategy
🎯 Why Snapshots?
Performance: Avoid replaying thousands of events
Scalability: Bounded replay time as system grows
Efficiency: Reduce CPU and memory usage
Availability: Faster system startup and recovery
📈 Snapshot Triggers
- Every N events (e.g., every 100 events)
- Time-based (e.g., daily snapshots)
- On-demand before major operations
- Memory pressure thresholds
⚡ Snapshot Process
1. Trigger Condition Met
Event count or time threshold reached
2. Serialize State
Convert aggregate state to storable format
3. Store Snapshot
Save with version number and timestamp
4. Update Metadata
Record latest snapshot location
5. Cleanup (Optional)
Remove old snapshots beyond retention
Common Challenges & Solutions
⚠️ Challenges
Eventual Consistency:
Read models lag behind write models
Event Schema Evolution:
Handling changes to event structure over time
Projection Failures:
Handling errors in event processing
Storage Growth:
Events accumulate indefinitely
Complexity:
More complex than traditional CRUD
✅ Solutions
Consistency Boundaries:
Design UI to handle eventual consistency gracefully
Event Versioning:
Use event versioning and upcasting strategies
Error Handling:
Implement retry policies and poison message handling
Archival Strategy:
Archive old events to cheaper storage
Start Simple:
Begin with bounded contexts where benefits are clear
Event Sourcing Technology Stack
🗄️ Event Stores
EventStore:
Purpose-built event database with projections
Apache Kafka:
Distributed streaming platform
PostgreSQL:
Traditional DB with JSONB events
Amazon DynamoDB:
NoSQL with streams for events
🔧 Frameworks
Axon Framework (Java):
Complete CQRS/ES framework
NEventStore (.NET):
Event persistence for .NET
EventFlow (.NET):
CQRS and Event Sourcing framework
Akka Persistence (Scala):
Actor-based event sourcing
📊 Read Models
MongoDB:
Document-based projections
Elasticsearch:
Search and analytics projections
Redis:
Fast cached read models
InfluxDB:
Time-series projections
When to Use Event Sourcing
✅ Great Fit For
- • Financial Systems: Complete audit trails required
- • Domain-Rich Applications: Complex business rules
- • Temporal Requirements: Need historical state queries
- • Event-Driven Architectures: Natural integration
- • Analytics Heavy: Rich data for business intelligence
- • Collaboration Tools: Track all user interactions
- • Regulatory Compliance: Immutable audit logs
- • Debugging Complex Flows: Full event history
❌ Poor Fit For
- • Simple CRUD Applications: Unnecessary complexity
- • Report-Heavy Systems: Complex queries difficult
- • Real-time Requirements: Eventual consistency issues
- • Small Teams: Learning curve and maintenance
- • Short-lived Applications: Infrastructure overhead
- • High Update Frequency: Single entities updated often
- • Privacy Requirements: Hard to "forget" data
- • Legacy Integration: Existing systems expect current state
Event Sourcing Best Practices
🎯 Design Guidelines
- • Design events as business facts, not technical artifacts
- • Keep events immutable and focused on single concerns
- • Use meaningful event names that business understands
- • Include sufficient context in events for projections
- • Version events from the start using semantic versioning
- • Implement idempotent event handlers for safety
- • Plan for event schema evolution early
- • Design aggregates around consistency boundaries
🛠️ Implementation Tips
- • Start with a single bounded context
- • Implement comprehensive testing strategies
- • Monitor projection lag and system health
- • Use correlation IDs for request tracing
- • Implement proper error handling and retries
- • Design UI to handle eventual consistency
- • Plan for operational concerns (backups, monitoring)
- • Document event schemas and business rules
📝 Event Sourcing & CQRS Knowledge Quiz
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