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PySpark Code for Broadcast Join and Conditional Aggregation by Location

Spark/Big Datahard0.3 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
Nagarro
Key Concepts Tested
joinoptimizationpartitionsparksql
Expert AnswerPremium
65 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Broadcast join + conditional agg: `from pyspark.sql.functions import broadcast, sum, when`; `joined = df.join(broadcast(lookup_df), 'id')`; `joined.groupBy('location').agg(sum(when(col('status')=='A',1).otherwise(0)).alias('count_a'))`....
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 Nagarro. The answer also includes follow-up discussion points that interviewers commonly explore.

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