Ali Zaidi has a walkthrough on using sparklyr on HDInsight:
The majority of Spark is written in Scala (~80% of Spark core), which is a functional programming language. Functional programming languages emphasize functional purity (the output only depends on the inputs) and strive to avoid side-effects. One important component of most functional programming languages is their lazy evaluation. While it might seem odd that we would appreciate laziness from our computing tools, lazy evaluation is an effective way of ensuring computations are evaluated in the most efficient manner possible.
Lazy evaluation allows Spark SQL to highly optimize the queries. When a user submits a query to Spark SQL, Spark composes the components of the SQL query into a logical plan. The logical plan is basically a recipe Spark SQL creates in order to evaluate the desired query. Spark SQL then submits the logical plan to its highly optimized engine called Catalyst, which optimizes this plan into a physical plan of action that is executed inside Spark computation engine (a series of coordinating JVMs).
Read on for more description and code.