The Who, Where, What, How, and When of Snowflake UDTFs

User-Defined Table Functions (UDTFs) in Snowflake enable advanced data transformations by returning a set of rows based on input parameters. This guide explores the who, where, what, how, and when of Snowflake UDTFs to help you leverage them effectively.

Who Uses Snowflake UDTFs?

Snowflake UDTFs are utilized by:

• Data Engineers: To perform complex transformations and enrich data pipelines.
• Data Analysts: For generating tabular outputs from structured or semi-structured data.
• Data Scientists: To preprocess or explode data for machine learning workflows.
• Developers: For building reusable functions that output multiple rows of data.

Where Are Snowflake UDTFs Used?

Snowflake UDTFs are applied in:

• Data Transformations: For breaking down or reshaping data.
• Analytics Pipelines: To compute derived metrics or generate datasets.
• Semi-Structured Data Processing: For parsing and normalizing JSON or XML data.
• Exploratory Data Analysis: To dynamically generate rows based on input values.

What Are Snowflake UDTFs?

Snowflake UDTFs are functions that accept input parameters and return a set of rows. They are typically implemented using Python (via Snowpark) or SQL.

Key Features of UDTFs

• Row-Generating: Produce multiple rows from a single function call.
• Dynamic Outputs: Adjust output rows based on input parameters.
• Flexible Logic: Implement custom row-generation logic in Python or SQL.

Example Use Cases

1. Exploding JSON Arrays: Splitting a JSON array into individual rows.
2. Generating Date Ranges: Producing rows for a range of dates based on input values.
3. Custom Aggregations: Creating tabular outputs for grouped data.

How Do Snowflake UDTFs Work?

1. Creating a UDTF

Define a UDTF using the CREATE FUNCTION statement. Specify the input parameters, return type, and logic.

SQL-Based UDTF Example

CREATE OR REPLACE FUNCTION generate_numbers(start INT, end INT)
RETURNS TABLE (number INT)
LANGUAGE SQL
AS
'
  SELECT seq AS number
  FROM TABLE(GENERATOR(ROWCOUNT => end - start + 1))
  QUALIFY ROW_NUMBER() OVER (ORDER BY seq) + start - 1 = seq;
';

Python-Based UDTF Example

CREATE OR REPLACE FUNCTION split_string(input STRING, delimiter STRING)
RETURNS TABLE (part STRING)
LANGUAGE PYTHON
RUNTIME_VERSION = '3.8'
HANDLER = 'split_string_udtf'
AS '
  def split_string_udtf(input, delimiter):
      for part in input.split(delimiter):
          yield (part,)
';

2. Invoking a UDTF

Use UDTFs in SQL queries just like tables:

SELECT * FROM TABLE(generate_numbers(1, 5));

SELECT * FROM TABLE(split_string('A,B,C', ','));

3. Managing UDTFs

• Show UDTFs: List all available UDTFs in the current schema.

  SHOW FUNCTIONS;
  

• Drop a UDTF:

  DROP FUNCTION generate_numbers(INT, INT);
  

When Should You Use Snowflake UDTFs?

1. Exploding Semi-Structured Data

• When: You need to parse JSON, XML, or arrays into rows.

2. Generating Dynamic Outputs

• When: Input parameters determine the number of output rows.

3. Custom Table Logic

• When: Advanced transformations require multi-row outputs.

Best Practices for Snowflake UDTFs

1. Optimize for Performance:
   • Use efficient logic to minimize execution time.
2. Validate Input Data:
   • Ensure input values are properly sanitized.
3. Use Python for Complex Logic:
   • Leverage Python UDTFs for advanced transformations and computations.
4. Test UDTFs Thoroughly:
   • Validate results with diverse input scenarios.
5. Monitor Usage:
   • Analyze query execution plans to identify potential bottlenecks.

Conclusion

Snowflake UDTFs provide powerful capabilities for generating rows dynamically, enabling complex data transformations directly within the platform. By understanding how to create and utilize UDTFs, you can simplify your data workflows and unlock new possibilities in Snowflake.

Start exploring Snowflake UDTFs today to transform your data like never before.