Invalidating query cache entries
Invalidating query cache entries - liquidating a trustee company
This causes the item cache in the primary node to be modified with the new value; that value will then be replicated to all of the other nodes in the cluster.
To achieve high availability for your application, we recommend that you provision your DAX cluster with at least two nodes (ideally three or more), and place those nodes in multiple availability zones within a region.
In this scenario, it is possible for two clients to read the same key from the same DAX cluster but receive different values, depending on the node that each client accessed.
The nodes will all be consistent when the update has been fully replicated throughout all of the nodes in the cluster.
DAX is a write-through caching service, designed to simplify the process of adding a cache to Amazon Dynamo DB tables.
Because DAX operates separately from Dynamo DB, it is important that you understand the consistency models of both DAX and Dynamo DB to ensure that your application behaves as you expect.
DAX is intended for applications that require high-performance reads.
As a write-through cache, DAX allows you to issue writes directly, so that your writes are immediately reflected in the item cache.When you use The item will remain in the DAX item cache, subject to the TTL setting and LRU algorithm for the cache (see Concepts).However, during this period, DAX will not re-read the item from Dynamo DB.To illustrate, consider the following scenario where an application is working with a table named expires.Your application should consider the TTL value for the query cache, and how long your application is able to tolerate inconsistent results between the query cache and the item cache.This ensures that data is not written to the DAX cache unless it is first written successfully to Dynamo DB.