Strong Consistency
Strong consistency is a database consistency model that guarantees all readers see the most recent write, regardless of which node they access. Under strong consistency, once a write operation completes, all subsequent read operations will return the updated value, ensuring a single, current view of the data across all nodes in a distributed system.
How strong consistency works
Strong consistency maintains a strict ordering of operations by ensuring that writes are synchronized across all nodes before being considered complete. This model implements:
- Immediate propagation of updates
- Distributed consensus protocols
- Synchronous replication
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.
Tradeoffs and considerations
Strong consistency provides the highest level of data correctness but comes with significant tradeoffs:
Performance impact
- Higher latency due to synchronous operations
- Reduced write throughput
- Increased resource consumption
Availability challenges
- System may become unavailable if nodes can't reach consensus
- Network partitions can block operations
- Related to the CAP theorem tradeoffs
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 patterns
Two-Phase Commit (2PC)
The two-phase commit protocol ensures strong consistency through a coordinated commit process:
- Prepare phase: Coordinator asks all nodes to confirm they can commit
- Commit phase: After all confirmations, coordinator instructs nodes to commit
Consensus Algorithms
Distributed systems often implement consensus algorithms like:
- Paxos
- Raft
- Zab (ZooKeeper Atomic Broadcast)
These protocols ensure all nodes agree on the order and outcome of operations.
Applications in time-series data
In time-series databases, strong consistency is particularly important for:
- Financial transaction processing
- Industrial control systems
- Mission-critical sensor data
- Regulatory compliance systems
When working with time-series data, strong consistency ensures that:
- Timestamp ordering is preserved
- No data points are lost
- Aggregations reflect complete datasets
Best practices
- Evaluate whether strong consistency is truly needed
- Consider using snapshot isolation for read-heavy workloads
- Implement proper monitoring for consensus health
- Design for failure scenarios and network partitions
- Use appropriate replication strategies
Monitoring considerations
Key metrics to monitor in strongly consistent systems:
- Consensus round-trip time
- Replication lag (should be near zero)
- Node health and connectivity
- Write operation latency
- Conflict resolution events
The impact on query latency and write throughput should be carefully measured and balanced against consistency requirements.