Snowflake User Defined Functions (UDFs) - These are Powerful

In this article, we will discuss UDFs, also known as User Defined Functions that can be created and utilised in workloads within Snowflake.

User Defined Functions (UDFs) are a powerful feature in Snowflake that allow users to encapsulate and reuse logic within SQL, JavaScript, Java, or Python code. Whether you’re transforming data, encapsulating business logic, or customizing behaviour beyond Snowflake’s built-in capabilities, UDFs enable flexibility and cleaner, modular development. For engineers and analysts working with Snowflake, UDFs can significantly enhance productivity and maintainability, offering a powerful way to extend native functionality.

Let’s do an in-depth look at how Snowflake UDFs work, their benefits, practical implementation strategies, and best practices for using them effectively in real-world engineering contexts.

Table of Contents

Core Concepts: Understanding Snowflake UDFs

What Are User Defined Functions?

Features change from time to time with new features being added regularly, it is recommended that you review the documentation for the latest on what specific features are included with any of the Editions.

User Defined Functions are custom functions that you can define within Snowflake to perform specific operations that are not natively available or to simplify complex logic by wrapping it into reusable components. UDFs execute within Snowflake’s compute layer and can be invoked just like built-in SQL functions.

Types of UDFs in Snowflake

Snowflake supports several types of UDFs:

• Scalar UDFs: Return a single value for each input row.

• Table UDFs (UDTFs): Return a set of rows (a table) from input parameters.

• External Functions: Call out to external services (e.g., AWS Lambda) but are not considered traditional UDFs.

Supported Languages

Snowflake allows UDFs to be written in several languages:

• SQL: Ideal for basic logic and transformations.

• JavaScript: Enables conditional logic, loops, and control structures.

• Java: For performance-intensive or library-dependent logic.

• Python: Via Snowpark, great for data science workloads.

UDF in Practice

Creating a Scalar SQL UDF

CREATE OR REPLACE FUNCTION multiply_by_ten(x NUMBER)
RETURNS NUMBER
AS $$
  x * 10
$$;

using multiply_by_ten in practice:

SELECT multiply_by_ten(7); -- Returns 70

Examples and Use Cases

1. Data Transformation

Applying transformations to clean and standardize data during ingestion.

CREATE OR REPLACE FUNCTION clean_whitespace(input STRING)
RETURNS STRING
AS $$
  TRIM(REGEXP_REPLACE(input, '\\s+', ' '))
$$;

2. Business Logic Encapsulation

Reusable financial calculations, such as tax computation:

CREATE OR REPLACE FUNCTION calculate_tax(amount NUMBER, rate NUMBER)
RETURNS NUMBER
AS $$
  amount * rate / 100
$$;

3. Custom Scoring Systems

Generating a risk score based on various inputs:

CREATE OR REPLACE FUNCTION compute_risk(age NUMBER, income NUMBER)
RETURNS STRING
AS $$
  CASE
    WHEN age > 50 AND income < 30000 THEN 'HIGH'
    WHEN age <= 50 AND income >= 30000 THEN 'LOW'
    ELSE 'MEDIUM'
  END
$$;

Best Practices and Optimization Tips

1. Keep It Simple

Avoid overly complex logic in UDFs. Split logic into multiple smaller UDFs if needed.

2. Watch Performance

Scalar UDFs can be slower than native functions. Use them only when necessary and test performance on large datasets.

3. Avoid State

UDFs should be deterministic and stateless. Do not attempt to track session variables or external states.

4. Language Choice Matters

• Use SQL for simple transformations.

• Use JavaScript for flexible logic or string manipulation.

• Use Python for analytics or ML.

• Use Java for high-performance routines.

5. Version Control and Documentation

Track UDF definitions in version-controlled SQL files and document them clearly for team-wide understanding.

6. Secure Usage

Restrict UDF creation privileges. Avoid embedding sensitive logic that could expose business rules unnecessarily.

Conclusion

Snowflake UDFs offer a powerful way to extend the platform’s capabilities while maintaining clean, modular, and maintainable code. From simple string formatting to complex risk models, UDFs support reusable logic in a secure and scalable way. With multiple language options and integration with Snowpark, UDFs are indispensable tools for data engineers and analysts alike.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation for the latest on what specific features are included with any of the Editions.