Often making errors helps to progress. It was my case with spark-submit and local/remote JAR pair. They helped me to understand the role of driver, closures, serialization and some configuration properties.
In general Spark's actions reflects logic implemented in a lot of equivalent methods in programming languages. As an example we can consider isEmpty() that in Spark checks the existence of only 1 element and similarly in Java's List. But it can often lead to troubles, especially when more than 1 action is invoked.
Every distributed computation is divided in small parts called jobs, stages and tasks. It's useful to know them especially during monitoring because it helps to detect bottlenecks.
