Luhncheck¶

Overview¶

The Luhncheck expression validates whether a given string represents a valid number according to the Luhn algorithm (also known as the "modulus 10" algorithm). This is commonly used to validate credit card numbers, IMEI numbers, and other identification numbers that use Luhn checksum validation.

Syntax¶

luhn_check(input_string)

// DataFrame API
col("column_name").luhn_check()

Arguments¶

Argument	Type	Description
input	String	The string to be validated using the Luhn algorithm

Return Type¶

BooleanType - Returns true if the input string passes Luhn validation, false otherwise.

Supported Data Types¶

String types with collation support (supports trim collation)
Input values are implicitly cast to string type if not already strings

Algorithm¶

Delegates validation to the ExpressionImplUtils.isLuhnNumber static method via StaticInvoke
Implements the Luhn algorithm which processes digits from right to left
Doubles every second digit from the right, subtracting 9 if the result exceeds 9
Sums all digits and checks if the total is divisible by 10
Returns boolean result indicating whether the checksum validates

Partitioning Behavior¶

This expression preserves partitioning behavior:

Does not require data shuffling across partitions
Can be evaluated independently on each partition
Maintains existing partitioning scheme when used in transformations

Edge Cases¶

Null handling: Returns null if the input string is null
Empty string: Returns false for empty strings as they cannot contain valid Luhn numbers
Non-numeric characters: Behavior depends on the underlying isLuhnNumber implementation
Leading/trailing whitespace: Handled according to trim collation support
Single digit numbers: May return false as Luhn algorithm typically requires multiple digits

Code Generation¶

This expression uses RuntimeReplaceable pattern with StaticInvoke:

Does not generate custom code via Tungsten code generation
Falls back to interpreted mode by invoking the static method at runtime
The StaticInvoke provides some optimization by avoiding object creation

Examples¶

-- Valid credit card number
SELECT luhn_check('4532015112830366');
-- Returns: true

-- Invalid number
SELECT luhn_check('79927398714');
-- Returns: false

-- Using with table data
SELECT customer_id, luhn_check(credit_card_number) as is_valid
FROM customers;

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

df.select(col("credit_card_number"), 
         expr("luhn_check(credit_card_number)").as("is_valid"))

// Using in filter conditions
df.filter(expr("luhn_check(account_number) = true"))