UDFs (User Defined Functions) in Apache Spark - Mini Project: Build & Apply

Using UDFs (User Defined Functions) in Apache Spark

📖 Scenario: You work at a retail company. You have sales data with product names and prices. You want to add a new column that shows the price category: 'Cheap' if price is less than 20, 'Moderate' if price is between 20 and 50, and 'Expensive' if price is above 50.

🎯 Goal: Create a Spark DataFrame with product data, define a User Defined Function (UDF) to categorize prices, apply it to add a new column, and display the result.

📋 What You'll Learn

Create a Spark DataFrame with exact product and price data

Define a UDF named price_category that categorizes prices

Use the UDF to add a new column category to the DataFrame

Show the final DataFrame with the new column

💡 Why This Matters

🌍 Real World

In real companies, UDFs help add custom logic to big data processing pipelines when built-in functions are not enough.

💼 Career

Knowing how to write and use UDFs is important for data engineers and data scientists working with Apache Spark to transform and analyze large datasets.

1

Create the initial Spark DataFrame

Create a Spark DataFrame called df with these exact rows: ("Pen", 10), ("Notebook", 25), ("Backpack", 60). Use columns named product and price.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("UDF Example").getOrCreate()

# Create the DataFrame df with product and price columns
# Your code here

Hint

Use spark.createDataFrame with a list of tuples and specify column names.

2

Define the UDF function

Define a Python function called price_category that takes a single argument price and returns the string 'Cheap' if price is less than 20, 'Moderate' if price is between 20 and 50 (inclusive), and 'Expensive' if price is greater than 50.

Apache Spark

def price_category(price):
    if price < 20:
        return 'Cheap'
    elif 20 <= price <= 50:
        return 'Moderate'
    else:
        return 'Expensive'
# Register the UDF and add the category column

Hint

Use simple if-elif-else statements to return the correct category string.

3

Register the UDF and apply it to the DataFrame

Import udf and StringType from pyspark.sql.functions and pyspark.sql.types. Register the price_category function as a UDF named price_category_udf with return type StringType(). Use withColumn on df to add a new column called category by applying price_category_udf to the price column. Save the result back to df.

Apache Spark

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
price_category_udf = udf(price_category, StringType())
df = df.withColumn("category", price_category_udf(df["price"]))
# Show the DataFrame

Hint

Use udf(function, returnType) to register, then withColumn to add the new column.

4

Show the final DataFrame

Use print to display the result of df.show().

Apache Spark

Hint

Use df.show() inside a print statement to display the table.

Practice

1. What is the main purpose of a User Defined Function (UDF) in Apache Spark?

easy

A. To create new Spark sessions

B. To manage cluster resources

C. To optimize Spark SQL queries automatically

D. To apply custom logic to each row of a DataFrame

Solution

1. Step 1: Understand UDF role in Spark

   A UDF lets you add your own logic to process each row in a DataFrame, beyond built-in functions.

2. Step 2: Compare options

   The other options relate to Spark setup or optimization, not custom row logic.

3. Final Answer:

   To apply custom logic to each row of a DataFrame -> Option D

4. Quick Check:

   UDF = custom row logic [OK]

Hint: UDFs add your own row-wise logic in Spark [OK]

Common Mistakes:

• Confusing UDFs with Spark session management
• Thinking UDFs optimize queries automatically
• Assuming UDFs manage cluster resources

2. Which of the following is the correct way to register a UDF in PySpark with a return type of IntegerType?

easy

A. spark.udf.register('my_udf', func, 'IntegerType')

B. spark.udf.register('my_udf', func)

C. spark.udf.register('my_udf', func, IntegerType())

D. spark.udf.create('my_udf', func, IntegerType())

Solution

1. Step 1: Recall UDF registration syntax

   The correct method is spark.udf.register with function name, function, and return type as a DataType object.

2. Step 2: Evaluate options

   spark.udf.register('my_udf', func, IntegerType()) uses IntegerType() correctly. spark.udf.register('my_udf', func) misses return type. spark.udf.create('my_udf', func, IntegerType()) uses a non-existent method. spark.udf.register('my_udf', func, 'int') uses a string instead of IntegerType().

3. Final Answer:

   spark.udf.register('my_udf', func, IntegerType()) -> Option C

4. Quick Check:

   Register UDF with return type object [OK]

Hint: Always specify return type as DataType object when registering UDF [OK]

Common Mistakes:

• Omitting the return type parameter
• Using incorrect method names like create
• Passing return type as string instead of DataType

3. Given the following PySpark code, what will be the output of df.select('name', 'age_plus_one').show()?

from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType

def add_one(age):
    return age + 1

add_one_udf = udf(add_one, IntegerType())
df = spark.createDataFrame([('Alice', 30), ('Bob', 25)], ['name', 'age'])
df = df.withColumn('age_plus_one', add_one_udf('age'))

medium

A. [[Alice, 31], [Bob, 26]]

B. [[Alice, 30], [Bob, 25]]

C. Error: UDF missing return type

D. [[Alice, 1], [Bob, 1]]

Solution

1. Step 1: Understand UDF logic

   The UDF adds 1 to the 'age' column values: 30 + 1 = 31, 25 + 1 = 26.

2. Step 2: Check DataFrame transformation

   The new column 'age_plus_one' will have values 31 and 26 for Alice and Bob respectively.

3. Final Answer:

   [[Alice, 31], [Bob, 26]] -> Option A

4. Quick Check:

   age + 1 = 31, 26 [OK]

Hint: UDF applies function row-wise, transforming each value [OK]

Common Mistakes:

• Forgetting to specify return type causing errors
• Assuming original age remains unchanged
• Confusing UDF output with input data

4. Identify the error in the following PySpark UDF code snippet:

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

def greet(name):
    return 'Hello ' + name

greet_udf = udf(greet)
df = spark.createDataFrame([('John',), ('Jane',)], ['name'])
df = df.withColumn('greeting', greet_udf('name'))
df.show()

medium

A. UDF missing return type specification

B. Function greet has wrong parameter type

C. DataFrame creation syntax is incorrect

D. withColumn method is used incorrectly

Solution

1. Step 1: Check UDF registration

   The UDF is created without specifying the return type, which is required.

2. Step 2: Verify other code parts

   Function parameter and DataFrame creation are correct. withColumn usage is proper.

3. Final Answer:

   UDF missing return type specification -> Option A

4. Quick Check:

   Always specify return type when creating UDF [OK]

Hint: Always specify return type when defining UDF [OK]

Common Mistakes:

• Omitting return type causes runtime errors
• Assuming return type is optional
• Misunderstanding function parameter types

5. You want to create a UDF that returns the length of a string column but safely handles null values by returning 0 instead of failing. Which of the following UDF definitions correctly implements this behavior?

hard

A. def safe_length(s):
return len(s)
safe_length_udf = udf(safe_length, IntegerType())

B. def safe_length(s):
if s is None:
return 0
else:
return len(s)
safe_length_udf = udf(safe_length, IntegerType())

C. def safe_length(s):
if s == '':
return 0
return len(s)
safe_length_udf = udf(safe_length, StringType())

D. def safe_length(s):
return s.length()
safe_length_udf = udf(safe_length, IntegerType())

Solution

1. Step 1: Handle null values safely

   def safe_length(s): if s is None: return 0 else: return len(s) safe_length_udf = udf(safe_length, IntegerType()) checks if input is None and returns 0, preventing errors.

2. Step 2: Check return type and logic

   def safe_length(s): if s is None: return 0 else: return len(s) safe_length_udf = udf(safe_length, IntegerType()) returns IntegerType and uses len(s) correctly. Others either miss null check or use wrong return type or method.

3. Final Answer:

   def safe_length(s): if s is None: return 0 else: return len(s) safe_length_udf = udf(safe_length, IntegerType()) -> Option B

4. Quick Check:

   Check nulls first, then return length [OK]

Hint: Check for None before calling len() in UDF [OK]

Common Mistakes:

• Not handling None causing runtime errors
• Returning wrong data type from UDF
• Using string methods incorrectly in Python