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Handling custom data types in Spark

Spark/Big Datahard0.4 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
JP Morgan
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
88 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Custom types in Spark: (1) StructType for nested; (2) ArrayType, MapType; (3) UDF for complex logic; (4) Encoder for Dataset (Scala). PySpark: use StructType, or JSON/string plus UDF. Example: StructType([StructField('name', StringType()), StructField('age', IntegerType())])....
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 JP Morgan. The answer also includes follow-up discussion points that interviewers commonly explore.

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