Struct CheckOverflow

pub struct CheckOverflow {
    pub child: Arc<dyn PhysicalExpr>,
    pub data_type: DataType,
    pub fail_on_error: bool,
}

This is from Spark CheckOverflow expression. Spark CheckOverflow expression rounds decimals to given scale and check if the decimals can fit in given precision. As cast kernel rounds decimals already, Comet CheckOverflow expression only checks if the decimals can fit in the precision.

Fields

child: Arc<dyn PhysicalExpr>

data_type: DataType

fail_on_error: bool

Implementations

impl CheckOverflow

pub fn new( child: Arc<dyn PhysicalExpr>, data_type: DataType, fail_on_error: bool, ) -> Self

Trait Implementations

impl Debug for CheckOverflow

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Display for CheckOverflow

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Hash for CheckOverflow

fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for CheckOverflow

fn eq(&self, other: &Self) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PhysicalExpr for CheckOverflow

fn as_any(&self) -> &dyn Any

Returns the physical expression as Any so that it can be downcast to a specific implementation.

fn fmt_sql(&self, _: &mut Formatter<'_>) -> Result

Format this PhysicalExpr in nice human readable “SQL” format

fn data_type(&self, _: &Schema) -> Result<DataType>

Get the data type of this expression, given the schema of the input

fn nullable(&self, _: &Schema) -> Result<bool>

Determine whether this expression is nullable, given the schema of the input

fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue>

Evaluate an expression against a RecordBatch

fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>>

Get a list of child PhysicalExpr that provide the input for this expr.

fn with_new_children( self: Arc<Self>, children: Vec<Arc<dyn PhysicalExpr>>, ) -> Result<Arc<dyn PhysicalExpr>>

Returns a new PhysicalExpr where all children were replaced by new exprs.

fn evaluate_selection( &self, batch: &RecordBatch, selection: &BooleanArray, ) -> Result<ColumnarValue, DataFusionError>

Evaluate an expression against a RecordBatch after first applying a validity array

fn evaluate_bounds( &self, _children: &[&Interval], ) -> Result<Interval, DataFusionError>

Computes the output interval for the expression, given the input intervals.

fn propagate_constraints( &self, _interval: &Interval, _children: &[&Interval], ) -> Result<Option<Vec<Interval>>, DataFusionError>

Updates bounds for child expressions, given a known interval for this expression.

fn evaluate_statistics( &self, children: &[&Distribution], ) -> Result<Distribution, DataFusionError>

Computes the output statistics for the expression, given the input statistics.

fn propagate_statistics( &self, parent: &Distribution, children: &[&Distribution], ) -> Result<Option<Vec<Distribution>>, DataFusionError>

Updates children statistics using the given parent statistic for this expression.

fn get_properties( &self, _children: &[ExprProperties], ) -> Result<ExprProperties, DataFusionError>

Calculates the properties of this PhysicalExpr based on its children’s properties (i.e. order and range), recursively aggregating the information from its children. In cases where the PhysicalExpr has no children (e.g., Literal or Column), these properties should be specified externally, as the function defaults to unknown properties.

fn snapshot(&self) -> Result<Option<Arc<dyn PhysicalExpr>>, DataFusionError>

Take a snapshot of this PhysicalExpr, if it is dynamic.

impl Eq for CheckOverflow