When preparing for a data engineering or Python + Spark interview, one common question is the difference between stored procedures in databases (like MySQL) and how something similar is achieved in PySpark. 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, PySpark provides alternatives like User Defined Functions (UDFs), Spark SQL queries, or calling stored procedures from external databases via JDBC connections. In this article, we’ll explore MySQL stored procedure examples, PySpark equivalents, and explain the differences — so you’ll be ready if this 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
| Feature | MySQL Stored Procedure | PySpark Equivalent |
|---|---|---|
| Where it runs | Inside DB Engine | Spark cluster |
| Reusability | Stored in DB catalog | UDFs / Functions |
| Language | SQL / PL-SQL | Python / Scala |
| Performance tuning | DB optimizations | Spark parallelism |
| Access control | DB grants/roles | IAM + Spark roles |
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.
Srinimf 