Event Sourcing
Event sourcing is an architectural pattern where system state changes are captured as an immutable sequence of events rather than just storing the current state. Each event represents a fact that occurred at a specific point in time, providing a complete audit trail and enabling system state reconstruction at any historical point.
How event sourcing works
Event sourcing fundamentally changes how applications store and manage data. Instead of storing just the current state and updating it directly, systems store a sequence of state-changing events. For example, in a trading system, rather than just maintaining current positions, each trade, adjustment, and reconciliation would be stored as discrete events.
The event store becomes the primary source of truth, with events stored in chronological order. Each event contains:
- A unique identifier
- Timestamp
- Event type
- Event data payload
- Metadata (actor, version, etc.)
Key benefits
Complete audit trail
Event sourcing provides perfect auditability by preserving every state change. This is particularly valuable for:
- Trade surveillance systems
- Regulatory compliance
- Dispute resolution
Temporal query capabilities
The event log enables powerful temporal analysis:
- State reconstruction at any point in time
- Historical trend analysis
- What-if scenario modeling
Event replay
Systems can replay events to:
- Rebuild state after failures
- Test system behavior
- Analyze historical scenarios
- Create new views of existing data
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Applications in financial systems
Event sourcing is particularly valuable in financial contexts:
Trading systems
- Capture every order, modification, and cancellation
- Enable exact trade reconstruction
- Support regulatory reporting requirements
Risk management
- Track position changes over time
- Analyze historical risk scenarios
- Maintain detailed audit trails
Market data systems
- Record price changes and market events
- Enable market replay functionality
- Support backtesting systems
Implementation considerations
Event schema design
- Events should be immutable
- Include all necessary context
- Version event schemas for evolution
- Consider storage efficiency
Performance optimization
- Use efficient serialization formats
- Implement snapshotting for faster rebuilds
- Consider event partitioning strategies
- Optimize read/write patterns
Consistency and ordering
- Maintain strict event ordering
- Consider causality between events
- Handle concurrent events properly
- Ensure reliable event delivery
Integration with time-series systems
Event sourcing naturally complements time-series databases by:
- Providing temporal context for analytics
- Supporting point-in-time reconstruction
- Enabling complex event processing
- Facilitating audit and compliance
Event sourcing combines particularly well with Complex Event Processing (CEP) systems to enable sophisticated real-time analytics and decision-making capabilities.