MongoDB CQRS + Event Sourcing

CQRS + Event Sourcing Pattern Comparison

Command Query Responsibility Segregation Implementation

Compare how MongoDB and PostgreSQL handle CQRS + Event Sourcing patterns, examining event storage, read model optimization, and temporal querying capabilities.

🔍 Key Pattern Differences

MongoDB's document model naturally fits event storage with flexible schemas, while PostgreSQL requires careful schema design and additional tooling for effective CQRS implementation.

🍃 MongoDB CQRS + Event Sourcing

Document-Native Event Store: MongoDB's flexible document model naturally accommodates evolving event schemas and complex event data structures.

Flexible Event Schema: Events can have different structures without migrations
Rich Event Data: Store complex nested event payloads naturally
Aggregation Projections: Use MongoDB pipelines for read model building
Temporal Queries: Time-based queries with built-in indexing
Horizontal Scale: Shard events by aggregate ID for performance

Natural Fit

Document model aligns with event structure

🐘 PostgreSQL CQRS + Event Sourcing

Relational Event Store: PostgreSQL requires structured approach with JSON columns or normalized tables for event storage and complex joins for projections.

ACID Compliance: Strong consistency guarantees for events
JSON Support: JSONB columns for flexible event payloads
SQL Projections: Use complex SQL for read model generation
Mature Tooling: Well-established backup and monitoring
Transaction Safety: Reliable cross-table consistency

Event Store Table

event_id (UUID)
aggregate_id
event_type
event_data (JSONB)

version
timestamp
metadata (JSONB)
created_at

Read Models (Separate Tables)

User View

Order View

Analytics View

Complex SQL joins required for projections

Schema Complexity

Requires careful table design and joins

⚡ CQRS Implementation Comparison

MongoDB Advantages:

• Events stored as natural JSON documents
• Aggregation pipelines for projection building
• No schema migrations for event evolution
• Horizontal scaling by aggregate ID

PostgreSQL Challenges:

• Rigid schema for event table structure
• Complex SQL for projection generation
• Manual read model synchronization
• Vertical scaling limitations