Understanding the Consistency Models

Consistency defines the rules under which distributed data is available to users. What this means is that when new data is available (i.e., new, or updated data) in a distributed database, the consistency model determines when the data is available to users for reads. Besides strong and eventual, there are three additional consistency models. These are the bounded staleness, session, and consistent prefix.

Strong Consistency Model

Guarantees that any read of an item (such as a customer record) will return the most recent version of such item.

Eventual Consistency Model

When using the eventual consistency model, it is guaranteed that all the replicas will eventually converge to reflect the most recent write.

Bounded Staleness Consistency Model

With bounded staleness, reads may lag writes by at most K operations or a t time interval. For an account with only one region, K must be between 10 and 1,000,000 operations, and between 100,000 and 1,000,000 operations if the account is globally distributed.

Session Consistency Model

The session consistency model is named so because the consistency level is scoped at the client session. What this means is that any reads or writes are always current within the same session, and they are monotonic.

Consistent Prefix Consistency Model

The last consistency model is consistent prefix. This model is like the eventual consistency model; however, it guarantees that reads never see out-of-order writes.

Consistency for Queries

By default, any user-defined resource would have the same consistency level for queries as was defined for reads.