BoolAnd¶

Overview¶

The BoolAnd expression is an aggregate function that returns true if all input boolean values are true, and false otherwise. It implements logical AND aggregation across multiple rows, internally using the Min function as its underlying implementation since false (0) is smaller than true (1) in boolean logic.

Syntax¶

bool_and(column)

// DataFrame API
df.agg(expr("bool_and(column)"))

Arguments¶

Argument	Type	Description
child	Expression	The boolean expression or column to aggregate

Return Type¶

BooleanType - Returns a single boolean value representing the logical AND of all input values.

Supported Data Types¶

BooleanType - Only boolean input types are supported due to ImplicitCastInputTypes with inputTypes set to Seq(BooleanType)

Algorithm¶

Internally delegates to the Min aggregate function through the replacement field
Leverages the fact that in boolean logic, false (0) < true (1)
The minimum of all boolean values will be false if any value is false
Returns true only when all input values are true (minimum would be true)
Follows standard aggregate function evaluation patterns through Spark Catalyst

Partitioning Behavior¶

Does not preserve partitioning as it is an aggregate function
Requires shuffle operation to collect all values for final aggregation
Partial aggregation can be performed within each partition before shuffling
Final result is computed after combining partial results from all partitions

Edge Cases¶

Null handling: Follows standard SQL null semantics - nulls are ignored in the aggregation
Empty input: Returns null for empty input sets (standard SQL aggregate behavior)
All nulls: Returns null when all input values are null
Mixed null/boolean: Ignores null values and aggregates only the non-null boolean values

Code Generation¶

This expression supports Tungsten code generation through its replacement with the Min function. The Min aggregate function has code generation support, so BoolAnd inherits this capability through the RuntimeReplaceableAggregate trait.

Examples¶

-- Basic usage
SELECT bool_and(col) FROM VALUES (true), (false), (true) AS tab(col);
-- Result: false

-- All true values
SELECT bool_and(col) FROM VALUES (true), (true), (true) AS tab(col);
-- Result: true

-- With nulls
SELECT bool_and(col) FROM VALUES (true), (null), (true) AS tab(col);
-- Result: true (nulls ignored)

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

val df = Seq((true), (false), (true)).toDF("col")
df.agg(expr("bool_and(col)")).show()

// Result: false