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Answer: Using Spark SQL's built-in functions wherever possible and reserving UDFs for operations that cannot be expressed with built-in functions
To ensure performance and maintainability when implementing custom UDFs in Spark, it's crucial to leverage Spark SQL's built-in functions whenever possible. These functions are optimized for performance and are more efficient than custom UDFs. Custom UDFs should be reserved for operations that cannot be achieved with built-in functions, keeping the codebase clean and maintainable. While broadcasting small datasets (Option A) can optimize access, and writing UDFs in Python (Option C) may ease development, these practices alone do not address the core need for optimization and maintainability as comprehensively as using built-in functions. Encapsulating all logic in a single UDF (Option D) can lead to monolithic structures that are hard to maintain and debug.
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When developing complex data transformation logic in Spark on Databricks using custom User Defined Functions (UDFs), what are the best practices to ensure they are both performant and maintainable?
A
Broadcasting small datasets used within UDFs to optimize their access across the cluster nodes
B
Using Spark SQL's built-in functions wherever possible and reserving UDFs for operations that cannot be expressed with built-in functions
C
Writing UDFs in Python for ease of development, disregarding the performance implications compared to Scala or Java UDFs
D
Encapsulating all transformation logic within a single UDF to minimize the invocation overhead
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