Best Practices in SQL Stored Procedures

Let us see some interesting use cases and best practices of SQL Stored Procedures using MySQL WorkBench in this week's article. It is not only famous in RDBMS but one of my favourite in getting the automated workflow of parameters for input, output help the procedures to get done through the unit test code.

If you are interested in ETL way of classifying large data into verifications (similar to Stored Procedures), please refer to,

Some Use Cases of My Choice

I want to call a stored procedures to list customers in ascending order-how would I do it?

Execution at Call step:

Similarly I want to list the quantity of paritcular product of my choice through a stored procedure-can I do it?

Now Sending the Input of particular item will help getting the output of quantity:

Learn more from tSQLt which uses Stored Procedures popularly for Unit Tests:

Introduction of Stored Procedures

In MySQL, a Stored Procedure is a set of SQL statements that are stored in the database and executed as a single unit. The general format of a stored procedure is:

Syntax

Explanation

• : Changes the statement delimiter to (to avoid conflicts with inside the procedure).

• : Defines the procedure with a name and optional parameters.

• : Marks the beginning and end of the procedure body.

• Inside the procedure, you can include SQL statements like , , , , loops, conditionals, etc.

• : Resets the delimiter back to .

Example 1: Stored Procedure Without Parameters

To call this procedure:

Example 2: Stored Procedure with Input Parameters

To call this procedure:

Example 3: Stored Procedure with Input and Output Parameters

To call this procedure:

Example 4: Stored Procedure with IF Statement

Example 5: Stored Procedure with Loop

To call:

Keyword Description Input parameter Output parameter Both input and output parameter Defines the procedure block Declares variables inside the procedure Assigns values to variables Stores query results into variables Conditional statement Looping statements.

USE CASES

Stored procedures in MySQL are widely used in real-world applications for performance optimization, code reuse, security, and automation. Here are some practical use cases:

1. Data Validation and Business Logic Enforcement

✅ Use Case: Ensuring only valid data gets inserted into the database.

📌 Example: A stored procedure to insert a new user while validating the email format.

🔹 Why? Reduces application-side validation logic, ensuring consistency.

2. Automating Reports Generation

✅ Use Case: Running complex queries for reports efficiently.

📌 Example: Generate a monthly sales report.

🔹 Why? Avoids recalculating data in applications, improving performance.

3. Batch Processing and Bulk Updates

✅ Use Case: Performing bulk updates efficiently.

📌 Example: Mark inactive users if they haven't logged in for a year.

🔹 Why? Improves efficiency instead of running individual update queries.

4. Logging and Auditing Changes

✅ Use Case: Keeping track of changes for compliance and debugging.

📌 Example: Log all deleted users into an archive table.

🔹 Why? Helps maintain historical records and prevent accidental deletions.

5. Role-Based Access Control (RBAC)

✅ Use Case: Restrict access based on user roles.

📌 Example: Allow only admins to delete users.

🔹 Why? Enhances security by enforcing role-based restrictions.

6. Data Synchronization Between Tables

✅ Use Case: Keeping data consistent across related tables.

📌 Example: When a product is deleted, remove it from the table.

🔹 Why? Prevents orphaned records and ensures data consistency.

7. Scheduled Maintenance Tasks

✅ Use Case: Automate database maintenance tasks.

📌 Example: Delete old log entries after 6 months.

🔹 Why? Helps manage database size and improve performance.

8. Real-time Notifications and Triggers

✅ Use Case: Sending notifications when an event occurs.

📌 Example: Notify admins when a high-value order is placed.

🔹 Why? Helps in proactive decision-making and fraud detection.

9. Improving Application Performance

✅ Use Case: Reduce database round trips by bundling queries.

📌 Example: Retrieve customer details along with their recent orders in a single call.

🔹 Why? Reduces network overhead and improves response time.

10. API Backend Optimization

✅ Use Case: Optimize database interactions in API backends.

📌 Example: A stored procedure for a user authentication API.

🔹 Why? Reduces backend processing time for authentication.

Using stored procedures for validating large index files in a stock exchange scenario can be useful when comparing digital assets, such as stock prices, trade volumes, and market data, against a reference dataset. However, for extremely large datasets, MySQL stored procedures might not be the most efficient approach compared to tools like Python (Pandas), ETL pipelines, or big data processing frameworks.

Use Case: Validating Large Index Files for Digital Assets in a Stock Exchange

✅ Scenario: A stock exchange receives a daily index file containing digital asset prices. This data needs to be validated against the existing database records to ensure consistency before updating the system.

✅ Requirements:

• Compare index file data with existing stock data.

• Identify missing, mismatched, or duplicate entries.

• Generate a report of discrepancies.

Step 1: Create a Table to Store Incoming Data

We assume the index file data is first imported into a temporary table ().

Step 2: Stored Procedure for Data Validation

This stored procedure compares the imported index file data () with the actual market data () and logs discrepancies.

Step 3: Create a Log Table for Discrepancies

Before running the procedure, create a validation log table to store errors.

Step 4: Execute the Stored Procedure

Run the validation:

Check validation results:

Step 5: Handle Discrepancies

After running the validation, you can take actions based on discrepancies:

• Send alerts for mismatches.

• Manually review and correct data.

• Automatically update records if needed.

For example, updating records where the price mismatch is within an acceptable threshold:

Performance Considerations

• Stored procedures are useful for structured validation but may not scale well for extremely large datasets.

• Indexes on and in both tables will improve performance.

• Batch processing or external tools (Python/Pandas, Apache Spark) may be better for big data.

Conclusion

📌 Stored procedures help validate large stock exchange files efficiently by detecting missing, mismatched, or duplicate records. 📌 This approach ensures data accuracy before updating the main trading system. 📌 For extremely large datasets, consider alternative big-data solutions.

🔹 Stored procedures are essential in MySQL for: ✅ Automating business processes ✅ Ensuring data consistency ✅ Enhancing security ✅ Reducing server load

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