This queue is read by a separate thread at some point later and the distributed cache takes care of submitting the data to the backing store such as the relational database. The distributed cache places the data in a queue. With write-behind, the service or application submits the data to the distributed cache and it returns an acknowledgment once the data arrives but before it is committed to the backing store. Write-behind solves the problem of writing to a slow backing store, for example, an overloaded relational database. Think of it as an extension to the write-through pattern. This is a caching pattern that is available in Hazelcast. Note the extra network hop and also the extra cache/DB sync logic required in the cache-aside pattern. These patterns make it much easier to implement a Cache as a Service Hazelcast Cache-Aside vs. Instead, it is much simpler operationally to let the distributed cache handle this. Imagine many different applications all having to handle access to the database and the cache, this could get very complicated and hard to handle in production. The cache process itself handles this logic and the connections to the database with read-through and write-through, whereas cache-aside means your service has to handle this. The important thing to note is that the amount of logic and libraries required in your service accessing the cache is greatly reduced with read-through and write-through. The diagram below shows the request flow using a cache-aside pattern versus that of read-through and write-through. Hazelcast is capable of handling cache-aside if required, but can also handle read-through, write-through, and write-behind caching patterns. For Hazelcast, only the update/read logic is required, which makes the code base much cleaner and easier to understand. With Redis, the responsibility is on the developer to write the cache/database synchronization logic and also the code to update/read the database. Hazelcast, by comparison, can be configured to handle read-through on a cache miss and write-through on updates. Using Redis as a cache over another store like a database forces the use of the cache-aside pattern this introduces extra network hops. The biggest difference between Hazelcast and Redis for caching use cases is that Redis forces the use of one caching pattern, whilst Hazelcast provides a number of patterns. Because we would like to speed up access to this data we cache the items once we have read them from the slower store. This could be a Relational Database, Mainframe, NoSQL Database or another Application API. When we talk about caching, we usually mean we are holding data in-memory that comes from a slower store which is usually disk-bound. The most common use case for Hazelcast IMDG and Redis is caching.
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