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Usage of UDFs?

Spark/Big Datahard0.6 min read

**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...

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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

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Citi. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, python, sql) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
114 words

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. (3) Built-in + expr: Often expr() or when/otherwise can replace simple UDFs.

Scalability Trade-offs: UDF breaks predicate pushdown; filter after UDF can't be pushed to source. For 1B rows, Python UDF can add hours.

Cost Implications: UDF-heavy jobs need more executors and time; 2–3x cost vs. built-in equivalent. Prefer pandas_udf with StructType for structured output.

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