CAP Theorem
The CAP theorem describes the fundamental limitations of distributed databases regarding consistency, availability, and partition tolerance.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Potential data inconsistencies.
- System overload under high availability.
- Difficulties in troubleshooting.
- Regular review of system architecture.
- Use of monitoring tools.
- Documentation of all decisions.
I/O & resources
- System requirements
- Architectural decisions
- Data management strategies
- Decisions on consistency and availability
- Implementation guidelines
- Optimized system architecture
Description
The CAP theorem, formulated by Eric Brewer, states that in a distributed data system, it is impossible to guarantee all three properties – consistency, availability, and partition tolerance – simultaneously. In the event of a network partition, a system must choose between consistency or availability. This has far-reaching implications for the design and implementation of distributed systems.
✔Benefits
- Improved system availability.
- Flexibility in data architecture.
- Better fault tolerance.
✖Limitations
- Consistency may be compromised in certain scenarios.
- Requires careful planning and design.
- Can lead to complexity in implementation.
Trade-offs
Metrics
- System Availability
Measurement of system availability under various conditions.
- Data Consistency Rate
Percentage of consistent data queries.
- Response Time
Time taken by the system to respond to requests.
Examples & implementations
Cassandra Database
Cassandra is a distributed NoSQL database that offers high availability and partition tolerance but sacrifices consistency.
Amazon DynamoDB
DynamoDB is a managed NoSQL database service designed for high availability and partition tolerance.
Google Cloud Spanner
Cloud Spanner provides global consistency and high availability by combining distributed and relational database technologies.
Implementation steps
Assess system requirements.
Develop an architectural strategy.
Implement and test the solution.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated database technologies.
- Lack of documentation of decisions.
- Insufficient testing and validations.
Known bottlenecks
Misuse examples
- Using a system without considering partition tolerance.
- Focusing on consistency at the expense of availability.
- Neglecting user requirements.
Typical traps
- Assuming that consistency is always necessary.
- Believing that partitions never occur.
- Underestimating system complexity.
Required skills
Architectural drivers
Constraints
- • Compliance with data protection regulations.
- • Technical infrastructure must be in place.
- • Resource capacities must be considered.