My question is if the number of execution contexts is more than the cores available in the driver of the cluster, then how does Databricks schedules the execution since one execution context will require at least one core to run on. Is it round-robin or something else. To make things simple let's assume no spark code is getting executed so there won't be any offloading to workers.