Abstract: Data services are often provided with consistency guarantees of either strong consistency models, comprising uniform wall-clock consistency, or eventual consistency models, where temporary logical inconsistency is guaranteed to be resolved only after full data propagation. However, the performance characteristics of contemporary services often require an intermediate consistency model, where some aspects of the service have specific consistency expectations and other aspects of the service are flexible, such as bounded staleness (e.g., a maximum delay in reaching consistency); session consistency (e.g., individual sessions remain logically consistent, but ordering may vary across sessions); and prefix consistency (e.g., each view during a session is logically consistent, but ordering may vary between session views).

Abstract: Data services are often provided with consistency guarantees of either strong consistency models, comprising uniform wall-clock consistency, or eventual consistency models, where temporary logical inconsistency is guaranteed to be resolved only after full data propagation. However, the performance characteristics of contemporary services often require an intermediate consistency model, where some aspects of the service have specific consistency expectations and other aspects of the service are flexible, such as bounded staleness (e.g., a maximum delay in reaching consistency); session consistency (e.g., individual sessions remain logically consistent, but ordering may vary across sessions); and prefix consistency (e.g., each view during a session is logically consistent, but ordering may vary between session views).