What is Causal Consistency Guarantee?
Causal consistency guarantees that if one event causally precedes another event (i.e., the first event affects the outcome of the second event), all nodes in a distributed system will observe these events in the same causal order.
- In other words, if event A causes event B, all nodes will see event A before event B in a consistent order.
- This ensures that the causal relationships between events are preserved and provides a meaningful and consistent view of the system’s state across all nodes.
Causal Consistency Model in System Design
In distributed systems, ensuring consistency among replicas of data is a fundamental challenge. Traditional approaches to consistency, such as strong consistency, can impose significant performance overhead or limit the system’s availability. To address these challenges, researchers and practitioners have explored alternative consistency models, one of which is causal consistency.
Important Topics for the Causal Consistency Model in System Design
- What is the Importance of Data Consistency?
- What is Causal Consistency?
- Characteristics of Causal Consistency
- What is Causal Consistency Guarantee?
- Example of Causal Consistency
- Causal Relationships in Distributed Systems
- How does Causal Consistency work?
- Real-World Example of Causal Consistency
- Use-Cases and Applications of Causal Consistency
- Impact of Causal Consistency on (System Performance, Scalability, and Availability)
- Implementation of Causal Consistency
- Benefits of Causal Consistency
- Challenges of Causal Consistency