Event sourcing is an architectural pattern that persists the state of an application as an immutable, append-only sequence of state-changing events, rather than storing only the current state. Each event represents a fact that occurred in the domain, such as ItemAddedToCart or PriceCalculated, and the current state is derived by replaying this chronological event log.
Glossary
Event Sourcing
An architectural pattern that persists the state of an application as an immutable sequence of state-changing events, providing a complete audit trail and source of truth.
ARCHITECTURAL PATTERN
What is Event Sourcing?
Event sourcing is a foundational pattern for building systems with a complete, auditable history of all state changes, serving as the single source of truth for real-time decisioning engines.
This pattern provides a complete, auditable audit trail and a durable source of truth, enabling temporal queries to reconstruct past states. In a real-time decisioning engine, event sourcing decouples write operations from read models, often used alongside CQRS to optimize for high-throughput ingestion and low-latency querying simultaneously.
ARCHITECTURAL PRIMITIVES
Core Characteristics of Event Sourcing
Event Sourcing is defined by several non-negotiable characteristics that distinguish it from traditional state-oriented persistence. These principles ensure the system remains an auditable, rebuildable, and strictly causal source of truth.
01
Immutable Append-Only Log
The event store functions as an append-only ledger. Once an event is written, it can never be modified or deleted. This provides a cryptographically verifiable audit trail.
- Corrections are made by appending compensating events, not overwriting history.
- This immutability is the foundation for temporal queries, allowing the system to reconstruct state at any point in time.
02
Event as the Source of Truth
The canonical source of truth is the sequence of Domain Events, not the current state. The database is merely a derived view.
- An
OrderShippedevent is a fact. TheOrdertable row is just a cached projection of those facts. - This eliminates the object-relational impedance mismatch by storing intent (the event) rather than just the structural snapshot.
03
Complete Rebuildability
The entire application state can be discarded and rebuilt from scratch by replaying the event log. This is a powerful operational tool.
- Projections (read models) are disposable; they can be deleted and regenerated to adopt new query patterns.
- This enables retroactive debugging: replaying production events in a development environment to diagnose a bug deterministically.
04
Temporal Queries
Because the log is a complete history, the system can answer time-travel questions that are impossible in a CRUD system.
- Query the state of an aggregate as it was at
2024-03-15T14:30:00Z. - Analyze the time between events, such as the average delay between
PaymentAuthorizedandOrderFulfilled, to identify process bottlenecks.
05
Separation of Concerns (CQRS)
Event Sourcing is naturally paired with Command Query Responsibility Segregation (CQRS). The write side handles commands and produces events; the read side consumes events to build optimized query models.
- Commands are intent-based operations that validate business rules before emitting events.
- This separation allows the write model to be strict and domain-focused, while read models are denormalized for blazing-fast UI performance.
06
Causal Consistency
Events carry a strict order within an aggregate boundary, ensuring causal consistency. The system knows that ItemAdded happened before OrderPlaced.
- This eliminates race conditions common in multi-threaded CRUD updates.
- Optimistic concurrency control is used: when appending events, the system checks the expected version to prevent conflicts, ensuring no events are lost.
EVENT SOURCING
Frequently Asked Questions
Clear, technical answers to the most common questions about the event sourcing architectural pattern, its implementation, and its relationship to real-time decisioning engines.
Event sourcing is an architectural pattern that persists the state of an application as an immutable, append-only sequence of state-changing events, rather than storing only the current state. Instead of saving a final balance, the system records every transaction—AccountCredited, AccountDebited, FeeApplied—as a distinct event in an event store. To reconstruct the current state, the system replays all events for a given entity in chronological order. This provides a complete, auditable audit trail and a single source of truth. The event store acts as the system of record, while materialized views (often in a separate read database) provide optimized query representations. This pattern is the foundational persistence mechanism behind CQRS (Command Query Responsibility Segregation), where commands write events and queries read from projections.
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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