The first type of cache that Coherence supported was the replicated cache, and it was an instant success due to its ability to handle data replication, concurrency control and failover in a cluster, all while delivering in-memory data access speeds. A clustered replicated cache is exactly what it says it is: a cache that replicates its data to all cluster nodes.
There are several challenges to building a reliable replicated cache. The first is how to get it to scale and perform well. Updates to the cache have to be sent to all cluster nodes, and all cluster nodes have to end up with the same data, even if multiple updates to the same piece of data occur at the same time. Also, if a cluster node requests a lock, it should not have to get all cluster nodes to agree on the lock, otherwise it will scale extremely poorly; yet in the case of cluster node failure, all of the data and lock information must be kept safely. Coherence handles all of these scenarios transparently, and provides the most scalable and highly available replicated cache implementation available for Java applications.
The best part of a replicated cache is its access speed. Since the data is replicated to each cluster node, it is available for use without any waiting. This is referred to as "zero latency access," and is perfect for situations in which an application requires the highest possible speed in its data access. Each cluster node (JVM) accesses the data from its own memory:
In contrast, updating a replicated cache requires pushing the new version of the data to all other cluster nodes:
Coherence implements its replicated cache service in such a way that all read-only operations occur locally, all concurrency control operations involve at most one other cluster node, and only update operations require communicating with all other cluster nodes. The result is excellent scalable performance, and as with all of the Coherence services, the replicated cache service provides transparent and complete failover and failback.
The limitations of the replicated cache service should also be carefully considered. First, however much data is managed by the replicated cache service is on each and every cluster node that has joined the service. That means that memory utilization (the Java heap size) is increased for each cluster node, which can impact performance. Secondly, replicated caches with a high incidence of updates will not scale linearly as the cluster grows; in other words, the cluster will suffer diminishing returns as cluster nodes are added.