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Code Keys/PySpark Academy/Module 19/Lesson 151
PYSPARK • LESSON 151

Vectorized & Native Alternatives to UDFs

How do we replace a Python email masking UDF with high-performance native Spark functions?

Expert⏱ 2 Minutes⭐ 840 XP
🤔 THE QUESTION

How do we replace a Python email masking UDF with high-performance native Spark functions?

💡 WHAT IS IT?

Combining substring() and concat() to achieve the exact same string masking without leaving the JVM.

🎯 WHAT IS IT USED FOR?

Refactoring slow UDFs to achieve 10x-100x speedups in high-throughput data lake pipelines.

💻 EXAMPLE
from pyspark.sql.functions import col, concat, lit, substring

df = df.withColumn("masked_email_native", concat(substring(col("email"), 1, 1), lit("***@domain.com")))

🎯 Mission Objectives

Practice typing production-grade PySpark code for Vectorized & Native Alternatives to UDFs.

  • ✓ Replace UDF with native functions
  • ✓ Use concat and substring for masking
  • ✓ Achieve whole-stage code generation performance
🚀 Start Mission
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