Struct RankEvaluator 

struct RankEvaluator {
    state: RankState,
    rank_type: RankType,
}

State for the rank built-in window function.

Fields

state: RankState

rank_type: RankType

Trait Implementations

impl Debug for RankEvaluator

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

Formats the value using the given formatter.

impl PartitionEvaluator for RankEvaluator

fn is_causal(&self) -> bool

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

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 evaluate_all_with_rank( &self, num_rows: usize, ranks_in_partition: &[Range<usize>], ) -> Result<ArrayRef>

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

fn supports_bounded_execution(&self) -> bool

Can the window function be incrementally computed using bounded memory?

fn include_rank(&self) -> bool

Can this function be evaluated with (only) rank

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

When the window frame has a fixed beginning (e.g UNBOUNDED PRECEDING), some functions such as FIRST_VALUE, LAST_VALUE and NTH_VALUE do not need the (unbounded) input once they have seen a certain amount of input.

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 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 uses_window_frame(&self) -> bool

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