DecimalAddNoOverflowCheck¶

Overview¶

DecimalAddNoOverflowCheck is a binary arithmetic expression that performs addition of two decimal values without checking for overflow conditions. This expression is specifically designed for use in aggregation operations like Sum and Avg where overflow checking is handled at the UnsafeRowWriter level.

Syntax¶

This expression is used internally by Spark's aggregation framework and is not directly accessible through SQL syntax. It is generated during the optimization phase for decimal addition in aggregation contexts.

Arguments¶

Argument	Type	Description
left	Expression	Left operand expression that evaluates to a decimal value
right	Expression	Right operand expression that evaluates to a decimal value
dataType	DataType	The result decimal data type (must be DecimalType)

Return Type¶

Returns a DecimalType with precision and scale determined by the provided dataType parameter.

Supported Data Types¶

Input types: DecimalType only
Both left and right expressions must evaluate to decimal values
Output type: DecimalType

Algorithm¶

Evaluates both left and right child expressions to obtain decimal values
Uses the TypeUtils.getNumeric() method to get the appropriate numeric handler for the result data type
Performs addition using the numeric handler's plus() method without overflow validation
For code generation, uses the decimal's $plus method directly
Null safety is handled at the BinaryOperator level

Partitioning Behavior¶

This expression does not affect partitioning behavior:

Preserves existing partitioning as it operates on individual row values
Does not require shuffle operations
Can be evaluated locally within each partition

Edge Cases¶

Null handling: If either operand is null, the result is null (inherited from BinaryOperator)
Overflow behavior: No overflow checking is performed, which may result in incorrect results for values that exceed the target decimal precision
Precision handling: Relies on the provided dataType parameter to determine result precision and scale
Type validation: Requires that the dataType parameter is an instance of DecimalType

Code Generation¶

This expression supports Spark's Tungsten code generation through the doGenCode method, which generates optimized Java code using the decimal's native $plus method for better performance.

Examples¶

-- This expression is not directly accessible in SQL
-- It's used internally during aggregation optimization
SELECT SUM(decimal_column) FROM table_name;

// Not directly constructible via DataFrame API
// Used internally by Spark's Catalyst optimizer
// Example of where it might be generated:
df.agg(sum($"decimal_column")).collect()