Struct NthValueEvaluator 

pub(crate) struct NthValueEvaluator {
    state: NthValueState,
    ignore_nulls: bool,
    n: i64,
}

Fields

state: NthValueState

ignore_nulls: bool

n: i64

Trait Implementations

impl Debug for NthValueEvaluator

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

Formats the value using the given formatter.

impl PartitionEvaluator for NthValueEvaluator

fn memoize(&mut self, state: &mut WindowAggState) -> Result<()>

When the window frame has a fixed beginning (e.g UNBOUNDED PRECEDING), for some functions such as FIRST_VALUE, LAST_VALUE and NTH_VALUE, we can memoize the result. Once result is calculated, it will always stay same. Hence, we do not need to keep past data as we process the entire dataset.

fn evaluate( &mut self, values: &[ArrayRef], range: &Range<usize>, ) -> Result<ScalarValue>

Evaluate window function on a range of rows in an input partition.x

fn supports_bounded_execution(&self) -> bool

Can the window function be incrementally computed using bounded memory?

fn uses_window_frame(&self) -> bool

Does the window function use the values from the window frame, if one is specified?

fn get_range( &self, idx: usize, _n_rows: usize, ) -> Result<Range<usize>, DataFusionError>

If uses_window_frame flag is false. This method is used to calculate required range for the window function during stateful execution.

fn is_causal(&self) -> bool

Get whether evaluator needs future data for its result (if so returns false) or not

fn evaluate_all( &mut self, values: &[Arc<dyn Array>], num_rows: usize, ) -> Result<Arc<dyn Array>, DataFusionError>

Evaluate a window function on an entire input partition.

fn evaluate_all_with_rank( &self, _num_rows: usize, _ranks_in_partition: &[Range<usize>], ) -> Result<Arc<dyn Array>, DataFusionError>

PartitionEvaluator::evaluate_all_with_rank is called for window functions that only need the rank of a row within its window frame.

fn include_rank(&self) -> bool

Can this function be evaluated with (only) rank