👀 Views: 98 💬 Answers: 1 📅 Created: 2025-07-11

apache-spark pyspark udf python

I'm maintaining legacy code that I can't seem to get I'm working with a `NullPointerException` while applying a User Defined Function (UDF) to a DataFrame in Apache Spark 3.4.1. The DataFrame is created from a Parquet file that contains some nullable fields, and the behavior seems to occur when processing these nullable fields in the UDF. Here's a simplified version of my code: ```python from pyspark.sql import SparkSession from pyspark.sql.functions import udf from pyspark.sql.types import StringType spark = SparkSession.builder.appName('ExampleApp').getOrCreate() def my_udf(value): return value.upper() if value is not None else 'NULL' my_udf_func = udf(my_udf, StringType()) df = spark.read.parquet('path/to/parquet') df = df.withColumn('uppercase_column', my_udf_func(df['nullable_column'])) df.show() ``` When I run this code, I get the following behavior message: ``` java.lang.NullPointerException ``` I tried adding checks inside the UDF as shown, but it still fails with null values. I've also explored using the `when` function from `pyspark.sql.functions` to handle null cases, but I couldn't integrate it effectively with my UDF. Does anyone know how to properly handle nullable fields when using UDFs in Spark DataFrames to avoid this behavior? Any guidance or best practices would be greatly appreciated! Has anyone else encountered this? I'm coming from a different tech stack and learning Python.