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Explain aggregation functions in PySpark with examples and use cases.

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Hexaware
Key Concepts Tested
optimizationpartitionsparkwindow
Expert AnswerPremium
93 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. PySpark aggregation functions: sum(), count(), avg(), min(), max(), collect_list(), collect_set(). Use with groupBy: df.groupBy('dept').agg(sum('salary'), avg('age')). Window functions: row_number(), rank(), dense_rank(), sum() over partition. Example: df.withColumn('rank', rank().over(Window.partitionBy('dept').orderBy(desc('salary'))))....
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 Hexaware. The answer also includes follow-up discussion points that interviewers commonly explore.

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