Struct datafusion_physical_expr::window::AggregateWindowExpr

pub struct AggregateWindowExpr { /* private fields */ }

A window expr that takes the form of an aggregate function

Implementations

impl AggregateWindowExpr

pub fn new(
    aggregate: Arc<dyn AggregateExpr>,
    partition_by: &[Arc<dyn PhysicalExpr>],
    order_by: &[PhysicalSortExpr],
    window_frame: Option<WindowFrame>
) -> Self

create a new aggregate window function expression

Trait Implementations

impl Debug for AggregateWindowExpr

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

Formats the value using the given formatter.

impl WindowExpr for AggregateWindowExpr

fn as_any(&self) -> &dyn Any

Return a reference to Any that can be used for downcasting

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

evaluate the window function values against the batch

fn name(&self) -> &str

Human readable name such as "MIN(c2)" or "RANK()". The default implementation returns placeholder text.

fn field(&self) -> Result<Field>

the field of the final result of this window function.

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

expressions that are passed to the WindowAccumulator. Functions which take a single input argument, such as sum, return a single datafusion_expr::expr::Expr, others (e.g. cov) return many.

fn partition_by(&self) -> &[Arc<dyn PhysicalExpr>]

expressions that’s from the window function’s partition by clause, empty if absent

fn order_by(&self) -> &[PhysicalSortExpr]

expressions that’s from the window function’s order by clause, empty if absent

fn evaluate_args(&self, batch: &RecordBatch) -> Result<Vec<ArrayRef>>

evaluate the window function arguments against the batch and return array ref, normally the resulting vec is a single element one.

fn evaluate_partition_points(
    &self,
    num_rows: usize,
    partition_columns: &[SortColumn]
) -> Result<Vec<Range<usize>>>

evaluate the partition points given the sort columns; if the sort columns are empty then the result will be a single element vec of the whole column rows.

fn partition_columns(&self, batch: &RecordBatch) -> Result<Vec<SortColumn>>

get partition columns that can be used for partitioning, empty if absent

fn sort_columns(&self, batch: &RecordBatch) -> Result<Vec<SortColumn>>

get sort columns that can be used for peer evaluation, empty if absent