14 Jan 2019
Reflective consistency is a consistency model which exposes anomaly statistics to the user. The anomalies are defined by some underlying consistency model. For example, a system could run under eventual consistency but track anomalies as defined by linearizability. Statistics on these anomalies can then be used by the system or the user of such a system. One example of using the statistics is for modifying the underlying consistency model.
Example: Cassandra with Reflective Consistency
Cassandra offers many consistency levels1 under which every read and write can be performed. A user could request Cassandra to perform all writes under a strong consistency level, such as requiring a quorum of replicas to acknowledge every write, and all reads under a relatively weaker consistency level.
Using the anomaly statistics, a user could modify their use of Cassandra based on anomalies. A user could perform all writes under the same, weak consistency level as reads, and when anomalies start to increase more than the user wants they could direct Cassandra to use the stronger consistency level until anomalies being to decrease.
Statistics could even be tracked per key, per replica, or per anything. This gives the user fine or coarse grained control.