What Is a Variant Data Type in Snowflake?

A variant data type in Snowflake is a flexible data type that can store values of different types within a single column. This allows for storing heterogeneous data in a structured format, making it suitable for scenarios where the data type may vary across rows or even within a row.

Why use a variant data type?

The variant data type provides several advantages:

• Flexibility: With variant, you can store different types of values within the same column, eliminating the need for separate columns for each data type. This makes it easier to handle complex or changing data structures.
• Efficiency: Variant is highly compressed and optimized for storage, resulting in reduced storage requirements and improved query performance.
• Simplicity: Using variant simplifies the schema design by reducing the number of columns required to represent complex or evolving structures.

How does variant work?

In Snowflake, the variant data type is represented using JSON (JavaScript Object Notation) format. JSON is a lightweight and widely supported format for representing structured data.

The JSON-based variant stores values as nested key-value pairs. Each key represents a field name, and its corresponding value can be of any supported Snowflake data type (e.g., string, number, boolean, array, object).

Example:

{
  "name": "John Doe",
  "age": 30,
  "is_active": true,
  "interests": ["programming", "music", "sports"],
  "address": {
    "street": "123 Main St",
    "city": "New York",
    "country": "USA"
  }
}

Working with variant data type:

To work with variant data in Snowflake, you can use various functions and operators provided by Snowflake’s variant data type support. These allow you to extract, modify, and query the nested values within a variant column.

Querying:

You can query the values stored within a variant column using dot notation to access nested fields. For example:

SELECT data:age AS age,
       data:address.city AS city
FROM my_table;

This query retrieves the age and city values from the “data” variant column in the “my_table” table.

Modifying:

To modify values within a variant column, you can use the UPDATE statement with appropriate path expressions. For example:

UPDATE my_table
SET data:age = 31
WHERE id = 123;

This statement updates the age value within the “data” variant column for a specific row identified by its ID.

Conclusion:

The variant data type in Snowflake provides a powerful and flexible way to store heterogeneous data within a single column. Its ability to handle complex structures and changing data types makes it an ideal choice for scenarios where schema evolution is required. By leveraging JSON-based storage and various built-in functions, Snowflake makes it easy to work with variant columns efficiently.