Snowflake's New Higher-Order Functions

Snowflake’s New Higher-Order Functions

In a competitive data analytics industry, it's necessary for efficiency and clarity. Snowflake cloud has introduced the Higher-order functions, a powerful feature to its already growing list of SQL capabilities to stand atop on the modern data warehouses platform to effectively process the arrays and semi-structure data.

Higher-Order functions?—?So, what are?they?

These are a new set of functions which accepts another function, mostly a Lambda expression as an argument. These are extremely valuable while working on semi-structured datasets making it easier to work on arrays and apply specific operations on its elements.

Key Higher-Order Functions in Snowflake

Currently Snowflake offers three core higher-order functions:

FILTER- As name implies, it filters the array elements on a given condition

TRANSFORM: Transforms array elements into a desired format or values.

REDUCE: Aggregates the array elements into single value through iterative processing.

What benefits do they?bring?

Traditionally, Snowflake Lateral Flatten is used to flatten semi structured data like JSON, XML formats.

Improved Efficiency

These higher-order functions often outperform the Lateral Flatten or UDF’s

Better Readability

These new functions bring more readability making the queries compact and expressive in nature

Enhanced Flexibility

Provides flexibility to manipulate the structure and semi-structure data

Reduced need for UDF

Overall, it eliminates the need for user-defined functions on many scenarios

Few Examples

Let's create a table named Orders and load few sample data.

CREATE OR REPLACE TABLE orders AS
  SELECT 1 AS order_id, '2025-01-01' AS order_date, [
    {'item':'Bose Soundbar', 'quantity':3, 'subtotal':1500},
    {'item':'Brother Printer', 'quantity':1, 'subtotal':1200}
  ] AS order_detail
  UNION SELECT 2 AS order_id, '2025-01-02' AS order_date, [
    {'item':'Lenovo Laptop', 'quantity':5, 'subtotal':7500},
    {'item':'Airpod Noise-canceling Headphones', 'quantity':5, 'subtotal':1000},
    {'item':'Samsung Noise-canceling EarBuds-3', 'quantity':4, 'subtotal':1505}
  ] AS order_detail;        

SELECT * FROM orders;        

Filter Function

Now I want to filter the data using higher-order function FILTER. Where i create the lambda function to check the subtotal greater than or equals to $1500 and Quantity greater than or equals to 4. I can do that all in one single function.

select order_id, order_date,
filter(order_detail, i->i:subtotal>=1500 and i:quantity >=4) as Order_Qty_gte_1500_4
from orders;        

Reduce

Let's try the second one on the list, the Reduce function. I am building a lambda function that calculates the subtotal and uses the acc variable to add subtotal calling a GrandTotal_sum. Note that, I am also initializing the acc variable to 0 to begin with.

SELECT order_id,  
       order_date,  
       REDUCE(order_detail,  
              0,  
              (acc, val) -> acc + val:subtotal) AS GrandTotal_sum
FROM orders;         

Transform

Final one on the current list is the TRANSFORM higher-order functions. As the name implies, we can perform transformation using this function. I want to write a simple conver the characters to upper case function on the order detail column.

select order_id, order_date,
transform(order_detail,x->upper(x)) as upper_nm
from orders;        

Finally, putting them all together in one single query

SELECT order_id,  
       order_date,  
       REDUCE(o.order_detail,  
              0,  
              (acc, val) -> acc + val:subtotal) AS GrandTotal_sum ,
            transform(order_detail,x->upper(x)) as Product_Details,
            filter(order_detail, y->y:subtotal>=1500 and y:quantity >=4) as Trans_OrdertotalQty_gte_1500_4
FROM orders o;         

Implementation Timeline

• FILTER and TRANSFORM: Released in May 2024.
• REDUCE: Introduced later, adding significant functionality for array processing.

Transforming Data Analytics to a new?high

The introduction of higher-order functions has reshaped how data professionals handle complex transformations, array manipulations, and advanced analytics. By integrating functional programming into SQL, Snowflake offers a modern, streamlined approach to data processing.

With these set of tools, users can perform sophisticated operations on arrays and semi-structured data, reducing reliance on inefficient workarounds.? The result is more efficient, readable, and performant workflows.

As data analytics evolves, Snowflake’s higher-order functions represent a pivotal innovation, empowering professionals to process complex data structures with greater ease and precision.