Are Spark SQL queries optimized? Are their execution plans optimized? Can you create an execution plan with Spark?

Spark SQL queries are automatically optimized by the Catalyst optimizer, which transforms the logical plan into an efficient physical plan. The optimizer applies a series of rule‑based transformations (e.g., predicate push‑down, column pruning, join reordering) and, if enabled, a cost‑based optimizer that uses statistics to choose the cheapest plan. The resulting physical plan is executed by the Tungsten engine, which performs code generation and memory management for speed. You can view the execution plan by calling explain() on a DataFrame or Dataset, which prints the logical plan, the optimized logical plan, the physical plan, and the physical execution plan. For example:

val df = spark.read.table("orders")
val plan = df.explain(true)

This prints a detailed plan that shows each optimization step. Thus, Spark SQL queries are indeed optimized, and you can inspect the execution plan directly within Spark.