Factorial¶

Overview¶

The Factorial expression computes the factorial of a given integer input. It returns the product of all positive integers less than or equal to the input value, with special handling for edge cases and range limitations.

Syntax¶

SELECT factorial(column_name) FROM table_name;
SELECT factorial(5);

import org.apache.spark.sql.functions._
df.select(factorial(col("column_name")))

Arguments¶

Argument	Type	Description
child	Integer	The input integer value for which to calculate the factorial

Return Type¶

LongType - Returns a 64-bit long integer containing the factorial result.

Supported Data Types¶

IntegerType (input is implicitly cast to integer if needed)

Algorithm¶

Accepts an integer input and validates it is within the supported range [0, 20]
Returns null for inputs outside the valid range (< 0 or > 20) to prevent overflow
Delegates the actual factorial computation to Factorial.factorial() helper method
Uses null-safe evaluation to handle null inputs appropriately
Supports both interpreted and code-generated execution paths

Partitioning Behavior¶

This expression preserves partitioning as it operates on individual rows without requiring data movement:

Does not require shuffle operations
Maintains existing data partitioning
Can be executed independently on each partition

Edge Cases¶

Returns null for negative integers (< 0)
Returns null for integers greater than 20 to prevent long integer overflow
Returns null when input is null (null-intolerant behavior)
Returns 1 for factorial(0) as per mathematical definition
Expression is marked as nullable due to range validation constraints

Code Generation¶

This expression supports Tungsten code generation through the doGenCode method, which generates optimized Java code for runtime execution rather than falling back to interpreted mode.

Examples¶

-- Basic factorial calculation
SELECT factorial(5);
-- Result: 120

-- Factorial with column reference
SELECT name, factorial(value) as fact_value FROM numbers;

-- Edge cases
SELECT factorial(0);   -- Result: 1
SELECT factorial(-1);  -- Result: null
SELECT factorial(21);  -- Result: null (overflow protection)

// DataFrame API usage
import org.apache.spark.sql.functions._

df.select(factorial(col("number_column")))

// With column alias
df.select(factorial(col("n")).alias("n_factorial"))

// Filtering out null results
df.select(factorial(col("n"))).filter(col("factorial(n)").isNotNull)