Event Sourcing Cqrs FAQ & Answers

Event sourcing stores all changes as immutable event sequence instead of current state. Events are append-only facts (UserRegistered, OrderPlaced, PaymentProcessed) with metadata (timestamp, user_id, correlation_id). Current state derived by replaying events through aggregate reducers: state = events.reduce(applyEvent, initialState). Benefits: (1) Complete audit trail (compliance, debugging - see every change ever made), (2) Time travel (replay to any point for investigation - what was state at 2PM yesterday?), (3) Natural event-driven architecture (publish events to consumers), (4) Reproduce bugs by replaying production events locally (exact same sequence). Storage: EventStoreDB 23.x (native event store), Kafka 3.x (high-throughput, 1M+ events/sec), PostgreSQL (event log table with partitioning for budget option). Events are immutable - never update or delete, only append new events.

CQRS separates read and write models into independent paths. Commands modify state via write model (domain logic, validations, business rules), queries read from optimized read models (denormalized views, different databases). Example: e-commerce - Commands: CreateOrder (validates inventory, payment), UpdateShippingAddress (business rules). Queries: GetOrderDetails (PostgreSQL with JOINs for admin), GetUserOrderHistory (MongoDB for fast customer lookups by user_id), SearchOrders (Elasticsearch for full-text search). Each query reads from database optimized for that access pattern. Benefits: (1) Independent scaling (write-heavy workload scales writes, read-heavy scales reads separately), (2) Optimized data models per query type (avoid one-size-fits-all compromise), (3) Security (commands require authentication/authorization, queries can be cached/public). Not event sourcing - can use traditional databases, just separates read/write concerns.

Event sourcing + CQRS combined pattern: (1) Commands validate and produce events → EventStore (append-only event log), (2) Event handlers consume events → update projections (materialized views optimized per query type), (3) Queries read from projections (eventual consistency, typically 10-100ms lag). Real-world example: e-commerce orders - Events: OrderPlaced, PaymentAuthorized, InventoryReserved, OrderShipped. Projections: (a) OrderSummary in PostgreSQL (admin dashboard queries with JOINs), (b) UserOrderHistory in MongoDB (fast customer lookups by user_id), (c) InventoryLevels in Redis (real-time stock counts), (d) OrderSearchIndex in Elasticsearch (full-text search). Each projection subscribes to relevant events only. Benefits together: (1) Independent scaling (writes to EventStore, reads from Elasticsearch), (2) Multiple views without duplicating write logic, (3) Add new features by replaying existing events into new projections (zero downtime). Projections built asynchronously from event stream.

Event sourcing and CQRS technology stack (2025): Event stores - EventStoreDB (native event store, projections built-in, .NET ecosystem), Kafka 3.x (high-throughput streaming, 1M+ events/sec, JVM ecosystem), PostgreSQL (event log table with partitioning, budget-friendly option), Axon Server (event store + message routing, enterprise). Projection frameworks - Axon Framework (Java, full CQRS+ES framework), Eventuous (.NET, lightweight ES library), Commanded (Elixir, distributed event sourcing), Marten (.NET + PostgreSQL event sourcing). Read databases - PostgreSQL (relational queries), MongoDB (document lookups), Elasticsearch (full-text search), Redis (real-time counters/caching). Message routing - Kafka, RabbitMQ, Azure Service Bus, AWS EventBridge. Snapshot optimization: store periodic state snapshots every 100-1000 events to avoid replaying millions (critical for high-event-count aggregates).

Use event sourcing + CQRS when: audit requirements (financial, healthcare - need complete history), complex domain with multiple views of same data (admin dashboard + customer portal + analytics), need temporal queries (state at specific time - what was balance on Dec 31?), event-driven microservices (events as integration contracts). Avoid for: simple CRUD apps (overhead not justified), tight consistency requirements (real-time trading - can't tolerate 50-200ms eventual consistency lag), small teams without event sourcing expertise (operational complexity high). Challenges: (1) Operational complexity (multiple databases, event version management, projection monitoring), (2) Eventual consistency (read-after-write may not see update for 50-200ms typical), (3) Event schema evolution (use upcasters/transformers for backward compatibility). Best practice: start with CQRS alone (separate read/write models using traditional DB), add event sourcing later when audit/temporal needs emerge. Monitor projection lag (<100ms production SLA typical).