The communication in distributed systems is an important element. The cluster members rarely share the hardware components and the single solution to communicate is the exchange of messages in the client-server model.
Defining the universal workload and associating corresponding resources is always difficult. Even if most of time expected resources will support the load, there always will be some interval in the year when data activity will grow (e.g. Black Friday). One of Spark's mechanisms helping to prevent processing failures in such situations is dynamic resource allocation.
Often a misconfiguration is the reason of all kinds of issues - performance, security or functional. Spark isn't an exception for this rule and it's the reason why this article focuses on configuration properties available for driver and executors.
