Struct datafusion_physical_expr::window::SlidingAggregateWindowExpr

pub struct SlidingAggregateWindowExpr { /* private fields */ }

A window expr that takes the form of an aggregate function Aggregate Window Expressions that have the form OVER({ROWS | RANGE| GROUPS} BETWEEN UNBOUNDED PRECEDING AND ...) e.g cumulative window frames uses PlainAggregateWindowExpr. Where as Aggregate Window Expressions that have the form OVER({ROWS | RANGE| GROUPS} BETWEEN M {PRECEDING| FOLLOWING} AND ...) e.g sliding window frames uses SlidingAggregateWindowExpr.

Implementations

impl SlidingAggregateWindowExpr

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

Create a new (sliding) aggregate window function expression.

pub fn get_aggregate_expr(&self) -> &Arc<dyn AggregateExpr>

Get the AggregateExpr of this object.

Trait Implementations

impl Debug for SlidingAggregateWindowExpr

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

Formats the value using the given formatter.

impl WindowExpr for SlidingAggregateWindowExpr

peer based evaluation based on the fact that batch is pre-sorted given the sort columns and then per partition point we’ll evaluate the peer group (e.g. SUM or MAX gives the same results for peers) and concatenate the results.

fn as_any(&self) -> &dyn Any

Return a reference to Any that can be used for downcasting

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

the field of the final result of this window function.

fn name(&self) -> &str

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

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

evaluate the window function values against the batch

fn evaluate_stateful(
    &self,
    partition_batches: &PartitionBatches,
    window_agg_state: &mut PartitionWindowAggStates
) -> Result<()>

Evaluate the window function against the batch. This function facilitates stateful, bounded-memory implementations.

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 get_window_frame(&self) -> &Arc<WindowFrame>

Get the window frame of this WindowExpr.

fn get_reverse_expr(&self) -> Option<Arc<dyn WindowExpr>>

Get the reverse expression of this WindowExpr.

fn uses_bounded_memory(&self) -> bool

Return a flag indicating whether this WindowExpr can run with bounded memory.

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

get order by columns, 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

fn get_values_orderbys(
    &self,
    record_batch: &RecordBatch
) -> Result<(Vec<ArrayRef>, Vec<ArrayRef>)>

Get values columns(argument of Window Function) and order by columns (columns of the ORDER BY expression)used in evaluators