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Worked with UDFs - share examples

Spark/Big Dataeasy0.3 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
LTIMindtree
Key Concepts Tested
python
Expert AnswerPremium
63 wordsInterview-ready
**Example 1 (Parse date)**: `@udf("date")`; `def parse_date(s): return datetime.strptime(s, "%Y-%m-%d").date()` **Example 2 (Pandas UDF, vectorized)**: `@pandas_udf("double")`; `def scale(s: pd.Series) -> pd.Series: return s * 2` **Example 3 (UDAF)**: Custom aggregate; GroupedData.agg(custom_udaf(col)). **Production**: Prefer built-in. Pandas UDF for performance. Register and reuse. Test null handling. **Scalability Trade-offs**: Python UDF slow; Pandas UDF 5–20x faster....
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 LTIMindtree. The answer also includes follow-up discussion points that interviewers commonly explore.

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