Context

Many data-processing APIs (including Spark, functional collections, and stream libraries) provide both map() and flatMap(). Confusing them often leads to nested outputs, incorrect counts, or unexpected downstream behavior.

Core Question

Explain the difference between map() and flatMap().

1. Output shape/cardinality: Given an input collection of size n, what can you say about the size and structure of the output from map() vs flatMap()?
2. Function signature intuition: What kind of function do you pass to each (element → element vs element → collection), and how does that affect the result?
3. Concrete example: Use a small input (e.g., list of sentences) to show how map(split) differs from flatMap(split).

Scope Guidance

Focus on semantics (nesting vs flattening), typical use cases (tokenization, exploding lists), and common mistakes. You may mention performance implications at a high level, but prioritize correctness and reasoning about output structure.