Data Engineer Interview Questions Set2

Great question! Understanding the difference between a UDF (User Defined Function) and built-in Spark SQL functions is crucial for writing performant PySpark code.

🔍 UDF vs In-built Spark Function

Feature	UDF (User Defined Function)	In-built Spark Function
Definition	A custom function defined by the user to extend Spark’s capabilities	Predefined, optimized functions provided by Spark (e.g., `col`, `lit`, `lower`)
Performance	❌ Slower – breaks Catalyst optimization	✅ Faster – fully optimized by Catalyst
Serialization Overhead	High – uses Python/Java serialization	Low – avoids serialization, uses internal JVM objects
Null Handling	Manual – you need to check for nulls explicitly	Handled internally and efficiently
Language Compatibility	Works across Python, Scala, Java, R	Works across all Spark APIs
Ease of Use	Flexible but verbose	Easy to use and concise
Vectorization	❌ Not supported	✅ Supported (e.g., for columnar formats)
Use Case	Complex logic not available in built-ins	Common operations (math, string, date, array, JSON, etc.)

🔧 Example

🔹 Using a Spark In-built Function (Fast & Optimized)

from pyspark.sql.functions import upper

df.select(upper(df.name)).show()

🔹 Using a UDF (Slower & Non-Optimized)

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

def custom_upper(name):
    return name.upper() if name else None

upper_udf = udf(custom_upper, StringType())
df.select(upper_udf(df.name)).show()

🧠 Key Takeaway

✅ Always prefer built-in Spark SQL functions over UDFs for better performance and optimization.
❌ Use UDFs only when the logic cannot be expressed with Spark functions.

No — UDFs (User Defined Functions) do not run on the driver side.
They run on the executor nodes — just like regular Spark transformations.

🔍 Detailed Explanation

✅ Where UDFs Run

When you define a UDF in PySpark (or Scala), it is serialized and shipped to each executor.
Then, during DataFrame transformations, each executor runs the UDF on the partitions of data it owns.

📌 So UDFs do NOT run on the driver, but rather on the worker (executor) nodes, just like built-in Spark operations.

⚠️ Why Confusion Happens

Some users think UDFs run on the driver because:

UDFs involve Python code, and PySpark has to serialize data between JVM (Spark) and Python (via Py4J).
This creates overhead and slower execution compared to Spark’s in-built JVM-optimized functions.

⚠️ But still — the execution happens on the executors, not on the driver.

🧪 Confirm with Example

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

def where_am_i(val):
    return socket.gethostname()  # Returns executor hostname

hostname_udf = udf(where_am_i, StringType())
df.withColumn("executor_host", hostname_udf(df["name"])).show()

👉 This will show the executor hostnames, proving that the UDF runs on executors, not the driver.

🧠 Summary

Statement	True/False
UDFs run only on driver	❌ False
UDFs run on executors (like other transformations)	✅ True
UDFs are slower than in-built functions	✅ True
UDFs cause serialization overhead in PySpark	✅ True

HintsToday

recent posts

about