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Discuss techniques such as partitioning, broadcast joins, and caching to enhance Spark job performance.

Spark/Big Datamedium0.4 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
Carelon
Key Concepts Tested
joinpartitionspark
Expert AnswerPremium
72 wordsInterview-ready
**Why techniques matter**: Combined effect = 5–50x improvement. **Partitioning**: Co-locate data; partition pruning reduces scan. Align partition key with filter. **Broadcast**: Small table to all executors; no shuffle. **Caching**: Reuse intermediates. **Scalability trade-offs**: Partition by high-cardinality = explosion; broadcast has size limit; cache competes with memory. **Cost implications**: Partition pruning = less scan; broadcast = less shuffle; cache = faster when reused....
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 Carelon. The answer also includes follow-up discussion points that interviewers commonly explore.

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