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Usage of UDFs?

Spark/Big Datahard0.6 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
Citi
Key Concepts Tested
optimizationpythonsql
Expert AnswerPremium
114 wordsInterview-ready
**When to Use UDFs**: Built-in functions cannot express custom business logic (e.g., proprietary scoring, external API calls, complex parsing). UDFs extend SQL/DataFrame. **Why Python UDFs Are Slow**: Each row is serialized JVM->Python->JVM; no Catalyst optimization; no vectorization. 10–100x slower than built-in. **Alternatives**: (1) **Pandas UDF (vectorized)**: Processes batches; 5–20x faster than row UDF. (2) **Scala/Java UDF**: No serialization overhead....
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 Citi. The answer also includes follow-up discussion points that interviewers commonly explore.

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