Strong Consistency vs Causal Consistency
Developers should use strong consistency when building systems that require strict data accuracy and cannot tolerate stale or conflicting reads, such as banking applications, e-commerce checkout processes, or healthcare records meets developers should learn and use causal consistency when building distributed applications that require high availability and low latency, such as social media feeds, collaborative editing tools, or real-time messaging systems, where strict serializability is too costly. Here's our take.
Strong Consistency
Developers should use strong consistency when building systems that require strict data accuracy and cannot tolerate stale or conflicting reads, such as banking applications, e-commerce checkout processes, or healthcare records
Strong Consistency
Nice PickDevelopers should use strong consistency when building systems that require strict data accuracy and cannot tolerate stale or conflicting reads, such as banking applications, e-commerce checkout processes, or healthcare records
Pros
- +It is essential in scenarios where concurrent operations must be serialized to prevent race conditions, ensuring data integrity and user trust
- +Related to: distributed-systems, database-consistency
Cons
- -Specific tradeoffs depend on your use case
Causal Consistency
Developers should learn and use causal consistency when building distributed applications that require high availability and low latency, such as social media feeds, collaborative editing tools, or real-time messaging systems, where strict serializability is too costly
Pros
- +It is particularly valuable in geo-replicated databases like Amazon DynamoDB or Cassandra, where it helps prevent anomalies like lost updates or stale reads without sacrificing scalability
- +Related to: distributed-systems, consistency-models
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Strong Consistency if: You want it is essential in scenarios where concurrent operations must be serialized to prevent race conditions, ensuring data integrity and user trust and can live with specific tradeoffs depend on your use case.
Use Causal Consistency if: You prioritize it is particularly valuable in geo-replicated databases like amazon dynamodb or cassandra, where it helps prevent anomalies like lost updates or stale reads without sacrificing scalability over what Strong Consistency offers.
Developers should use strong consistency when building systems that require strict data accuracy and cannot tolerate stale or conflicting reads, such as banking applications, e-commerce checkout processes, or healthcare records
Disagree with our pick? nice@nicepick.dev