How does R handle distributed execution graphs in Spark vs local execution?

R executes locally in-memory, while Spark translates operations into distributed DAG execution plans.

Local R execution is eager and memory-bound. Spark builds a logical plan from transformations and optimizes it via Catalyst optimizer before executing across partitions. R expressions are translated into Spark SQL or serialized functions.