In terms of in-memory databases and caching, both replication and distribution are methods to manage data across multiple nodes. However, they function quite differently.
A replicated cache involves copying (or replicating) the same data across all nodes in the system. This means that each node holds an exact copy of the cache. The benefit of this approach is that it provides high availability and fault tolerance because if one node fails, the data can be served from another node. However, it can consume a lot of network bandwidth when the cache is updated because these updates need to be propagated to all nodes. Also, the total amount of data you can store is limited by the capacity of a single node.
Example using Ehcache:
In a distributed cache, the data is partitioned across all nodes in the system. Each piece of data is stored on one node only. This means that the system can support larger total volumes of data, as the storage capacity scales with the number of nodes. It also reduces the load on the network compared to a replicated cache because cache updates are sent to one node only. The downside is that if a node fails, any data stored on that node could be lost unless there's some form of redundancy or backup strategy in place.
Example using Redis:
In summary, the choice between a replicated cache and a distributed cache depends on your specific requirements regarding data volume, fault tolerance, and network load.