Eventual Consistency
Eventual consistency is a consistency model that guarantees all database replicas will converge to the same value over time, after all writes cease. This model trades immediate consistency for improved availability and partition tolerance, making it particularly relevant for distributed time-series databases and high-throughput systems.
How eventual consistency works
In an eventually consistent system, different replicas may temporarily return different values for the same query. However, the system guarantees that:
- All replicas will eventually converge to the same state
- Once writes stop, all reads will return the same value
- The final state will reflect all successful writes
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Benefits and tradeoffs
Advantages
- Higher availability during network partitions
- Lower latency for write operations
- Better scalability across distributed systems
- Reduced coordination overhead between nodes
Disadvantages
- Temporary inconsistencies between replicas
- More complex programming model
- Challenging to reason about intermediate states
- May require application-level conflict resolution
Applications in time-series systems
Eventual consistency is particularly well-suited for time-series databases because:
- Time-series data is often append-only
- Historical data rarely changes
- Recent data modifications are typically additive
- Many time-series applications can tolerate temporary inconsistencies
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Implementation considerations
Conflict resolution
Systems must implement strategies to handle conflicting writes:
- Last-write-wins (based on timestamps)
- Vector clocks for causal ordering
- Custom merge functions
- Application-specific resolution logic
Monitoring consistency
Organizations should track:
- Replication lag metrics
- Convergence time
- Conflict rates
- Read/write latencies
Real-world examples
Eventual consistency is commonly used in:
- Metrics collection systems
- Sensor data aggregation
- Financial market data distribution
- Distributed monitoring platforms
The model excels in scenarios where:
- Write availability is critical
- Data has natural temporal ordering
- Applications can handle temporary inconsistencies
- Scale-out is more important than strict consistency
Remember that while eventual consistency provides excellent scaling properties, it requires careful consideration of application requirements and proper handling of potential inconsistencies during the convergence period.