**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Logical plan workflow: (1) Unresolved Logical Plan—from DataFrame/SQL. (2) Analysis—resolve attributes, types (Analyzer). (3) Optimized Logical Plan—Catalyst rules (predicate pushdown, etc.). (4) Physical plans—Spark strategies (FileSource, Join). (5) Selected Physical Plan—cost-based. (6) RDD—execute. View: `df.explain(True)`....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Snowflake. The answer also includes follow-up discussion points that interviewers commonly explore.
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