Eventual Consistency
Eventual Consistency is a consistency model that ensures that all copies of a database will eventually converge, without requiring immediate consistency at all times.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Data loss due to inconsistent states.
- Misuse of data integrity.
- Delayed user experiences.
- Regular checks of data integrity.
- Use of load balancing techniques.
- Implementation of data synchronization protocols.
I/O & resources
- User Inputs
- System Configurations
- Data Patterns
- Updated Records
- Data Analytics
- Reports on Data Availability
Description
Eventual Consistency is a fundamental concept in distributed systems, where data changes are propagated across various nodes. It allows for greater availability and scalability since immediate consistency is not required. A common example includes NoSQL databases that offer flexible data storage.
✔Benefits
- Increased availability.
- Better scalability.
- Reduced latency.
✖Limitations
- Data can be temporarily inconsistent.
- Difficulties in troubleshooting.
- Can lead to confusing states.
Trade-offs
Metrics
- Response Times
Time taken by a system to respond to requests.
- Data Availability
Measure of how often data is available and can be utilized.
- System Load
Ratio of active requests to total system capacities.
Examples & implementations
NoSQL Database Implementation
An organization implements a NoSQL database that uses eventual consistency to store data.
Cassandra for Large Data Sets
Using Apache Cassandra in a large distributed system to manage user information.
Data Archiving Strategies
Developing data archiving approaches that support eventually consistent data.
Implementation steps
Planning the system architecture.
Implementation of tested protocols.
Monitoring system performance and adjustments.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated infrastructure for data management.
- Non-optimized data packets.
- Lack of resource resolution.
Known bottlenecks
Misuse examples
- Using eventual consistency in real-time applications.
- Ignoring locking mechanisms in critical transactions.
- Inadequate testing in applications with high data availability.
Typical traps
- Excessive reliance on client-side logic.
- Misunderstanding of eventual consistency concepts.
- Difficulties in managing distributed transactions.
Required skills
Architectural drivers
Constraints
- • Technical requirements for data integrity.
- • Resource constraints in data processing.
- • Minimum network speed requirements.