When preparing for a data engineering interview, you may be asked how a PySpark stored procedure would work compared to traditional MySQL stored procedures in Apache Spark environments. While MySQL has built-in stored procedures to encapsulate business logic at the database level, PySpark doesn’t support stored procedures in the traditional sense. Instead, similar behavior in PySpark is typically achieved using tools like a PySpark UDF, Spark SQL queries, or by calling existing database stored procedures through JDBC connections. In this article, we’ll walk through MySQL stored procedure examples, practical PySpark equivalents, and clarify the differences—so you’ll be ready if this topic comes up in your next interview.

1. What is a Stored Procedure in MySQL?

• Definition: Precompiled SQL code stored in the database.
• Benefits: Reusability, performance, access control.
• Example:

DELIMITER $$
CREATE PROCEDURE GetHighSalaryEmployees(IN min_salary INT)
BEGIN
    SELECT emp_id, emp_name, salary
    FROM employees
    WHERE salary > min_salary;
END$$
DELIMITER ;

Call it:

CALL GetHighSalaryEmployees(50000);

🔹 2. Does PySpark Support Stored Procedures?

• PySpark is not a database → it has no native stored procedure mechanism.
• Equivalent concepts:
  • UDFs (User Defined Functions) for custom logic.
  • Reusable ETL pipelines.
  • Calling external DB stored procedures via JDBC.

🔙 3. PySpark Equivalent Using UDFs

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
spark = SparkSession.builder.appName("UDFExample").getOrCreate()
# Example function
def add_prefix(name):
    return "EMP_" + name
# Register UDF
spark.udf.register("add_prefix_udf", add_prefix, StringType())
# Use in SQL
df = spark.sql("SELECT emp_id, add_prefix_udf(emp_name) AS emp_code FROM employees")
df.show()

🔹 4. Calling a MySQL Stored Procedure from PySpark (via JDBC)

query = "CALL GetHighSalaryEmployees(50000)"
df = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:mysql://localhost:3306/mydb") \
    .option("dbtable", f"({query}) as t") \
    .option("user", "root") \
    .option("password", "mypassword") \
    .load()
df.show()

🔹 5. Key Differences

Conclusion

Stored procedures are a database concept (like in MySQL), while PySpark relies on UDFs, SQL queries, or external stored procedure calls via JDBC to achieve similar functionality. Understanding the distinction is crucial for interviews and for designing ETL pipelines that balance performance, maintainability, and scalability.