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Have you worked with UDFs in Spark? When do you use them, and how do they differ from built-in functions?

Spark/Big Datahard0.5 min readPremium
Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Coforge
Key Concepts Tested
optimizationpartitionpythonsparksql
Expert AnswerPremium
97 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. UDFs extend Spark with custom logic. Use when: no built-in for logic (e.g., custom parse). Differ: UDFs are black-box to Catalyst—no pushdown, slower (Python UDF equals row-by-row via Py4J). Prefer: built-in, pandas_udf (vectorized). Example: @udf(StringType()) def my_udf(x): return x.upper()....
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 Coforge. The answer also includes follow-up discussion points that interviewers commonly explore.

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