Context and Problem
Traditional CRUD-based data access can lead to scalability and performance bottlenecks:
- High contention on a single data store for reads and writes
- Different performance needs for read and write operations
- Difficulty scaling data storage efficiently
Solution
CQRS separates read and write operations into different models:
- Implement a command model for handling write operations
- Implement a query model optimized for read operations
- Use event sourcing or synchronization mechanisms to keep data consistent
Benefits
- Scalability
- Improves system performance by optimizing reads and writes separately
- Performance Optimization
- Enables database tuning specific to queries and commands
- Flexibility
- Supports independent scaling of read and write workloads
Trade-offs
- Increased Complexity
- Requires managing separate models and synchronization logic
- Eventual Consistency
- Writes and reads may not always be immediately consistent
- Additional Infrastructure
- Needs separate storage and processing layers for queries and commands
Issues and Considerations
- Data Synchronization
- Ensuring read models stay updated with write operations
- Implementation Overhead
- Requires additional development effort compared to CRUD models
- Query Model Design
- Designing efficient read models for different query patterns
When to Use This Pattern
- Your system has high read and write contention
- You need to optimize performance for large-scale applications
- Different components require specialized read and write handling