Our capability to run a task in a specific target location becomes much more useful when it comes to data affinity. This means that if we are going to be interacting with the distributed data held within the cluster, it would be optimal to co-locate the task execution close to where the required data is actually held. This will reduce the latency of a task and the networking cost of having to retrieve the dependency data from other nodes across the cluster before processing can actually occur. By making our task PartitionAware, we can return a key with which our task is going to interact. From this it is established which partition the key belongs to, and hence the member node that holds that data. Then the task will be automatically submitted to execute on that specific node to minimize the network latency for the task to obtain or manipulate the data.

We might also need to interact with multiple related entries, which might belong to different partitions,...