Table of Contents
Consistency models define how data is synchronized across distributed systems. Understanding these models helps in designing systems that meet specific performance and reliability requirements. This article explores practical examples and calculations related to various consistency models.
Types of Consistency Models
Common consistency models include eventual consistency, strong consistency, and causal consistency. Each offers different guarantees about data visibility and synchronization across nodes.
Practical Example: Read and Write Operations
Consider a distributed database with three nodes. A user writes data to node A, and another user reads from node B. The consistency model determines whether the read reflects the latest write.
Calculations for Consistency Guarantees
Suppose the system has a network delay of 50 milliseconds. In eventual consistency, data may take up to this delay to synchronize across nodes. For strong consistency, the system must wait for acknowledgments from all nodes before confirming a write, increasing latency.
- Eventual Consistency: Data propagates asynchronously, with possible stale reads.
- Strong Consistency: Reads always reflect the latest write, with higher latency.
- Causal Consistency: Ensures causally related updates are seen in order.
- Latency Impact: Strong consistency can add delays proportional to the number of nodes.