Question 272894 in Azure Question posted in
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Azure – How to register a complex function as the below as UDF in PYSPARK?

I have a function that takes a dataframe as a parameter and calculates the NULL value counts and NULL value percentage and returns a dataframe with column_name, null value count and null percentages. How can I register it as UDF in pyspark so that I can use the spark’s processing advantage. I am using spark 3.3.0

This is my function:

enter image description here

I could’nt find any method or implementation for complex functions and functions that run on whole dataframe.

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2

Answers


  1. 0
    • I have tried to register the UDF while trying to do so I have
      encountered Serialization Issues,Missing GROUP BY
      Clause      ,Incompatible Data Types and Unsupported Functions
    • I tried to register the function using Pandas UDF in PySpark and apply it to the whole DataFrame
    • Spark UDFs might not handle complex objects or Spark-specific objects properly.

    I have tried below approach
    Created a function to get null count and percentage and called the function

    from pyspark.sql import SparkSession
from pyspark.sql.functions import col, round
spark = SparkSession.builder 
    .appName("example") 
    .getOrCreate()
data = [(1, "Alice", 30, None),
        (2, "Bob", None, 70),
        (3, "Carol", 25, 80)]
columns = ['id', 'name', 'age', 'score']
df = spark.createDataFrame(data, columns)
def get_null_count_and_percentage(df):
    global is_null_value_appended
    columnList = df.columns
    total_count = df.count()
    null_counts = []
    for column_to_check in columnList:
        null_count = df.filter(col(column_to_check).isNull()).count()
        null_percentage = (null_count / total_count) * 100
        null_counts.append((column_to_check, null_count, null_percentage))
    result_df_count = spark.createDataFrame(null_counts, schema=['column_name', 'null_counts', 'null_percentage'])
    result_df_count = result_df_count.withColumn("null_percentage", round(col("null_percentage"), 2))
    return result_df_count
result_df = get_null_count_and_percentage(df)
result_df.show()

    enter image description here

    • The above code is Defining Function – get_null_count_and_percentage that takes a DataFrame, df, as an argument.
    • This function calculates the count and percentage of null values in each column of the DataFrame.
    • The list of column names is extracted using df.columns.
    • The total count of rows in the DataFrame is obtained using
      df.count()
    • An empty list null_counts is initialized to store the null counts and percentages for each column.
    • loop iterates through each column, and the count of null values is obtained using the filter method combined with col(column_to_check).isNull().
    • The percentage of null values for each column is calculated and stored in the null_counts list as tuples.
    • A new DataFrame, result_df_count, is created using
      spark.createDataFrame by passing the null_counts list, and the schema is defined as ['column_name', 'null_counts', 'null_percentage'].
    • The null_percentage column in the DataFrame is rounded to two decimal places using the withColumn and round functions.
    • The main purpose of this script is to calculate the count and
      percentage of null values for each column in the DataFrame.
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  2. 0

    The function you made is not UDF. You are using spark methods inside the function so it is not UDF. Spark methods are already optimized for datasets.

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