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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
//! Stream and channel implementations for window function expressions.
//! The executor given here uses bounded memory (does not maintain all
//! the input data seen so far), which makes it appropriate when processing
//! infinite inputs.
use crate::physical_plan::expressions::PhysicalSortExpr;
use crate::physical_plan::metrics::{
BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet,
};
use crate::physical_plan::windows::{
calc_requirements, get_ordered_partition_by_indices, window_ordering_equivalence,
};
use crate::physical_plan::{
ColumnStatistics, DisplayFormatType, Distribution, ExecutionPlan, Partitioning,
RecordBatchStream, SendableRecordBatchStream, Statistics, WindowExpr,
};
use datafusion_common::Result;
use datafusion_execution::TaskContext;
use ahash::RandomState;
use arrow::{
array::{Array, ArrayRef, UInt32Builder},
compute::{concat, concat_batches, sort_to_indices},
datatypes::{Schema, SchemaBuilder, SchemaRef},
record_batch::RecordBatch,
};
use datafusion_expr::window_state::{PartitionBatchState, WindowAggState};
use futures::stream::Stream;
use futures::{ready, StreamExt};
use hashbrown::raw::RawTable;
use indexmap::IndexMap;
use log::debug;
use std::any::Any;
use std::cmp::{min, Ordering};
use std::collections::{HashMap, VecDeque};
use std::pin::Pin;
use std::sync::Arc;
use std::task::{Context, Poll};
use datafusion_common::utils::{
evaluate_partition_ranges, get_arrayref_at_indices, get_at_indices,
get_record_batch_at_indices, get_row_at_idx,
};
use datafusion_common::DataFusionError;
use datafusion_expr::ColumnarValue;
use datafusion_physical_expr::hash_utils::create_hashes;
use datafusion_physical_expr::window::{
PartitionBatches, PartitionKey, PartitionWindowAggStates, WindowState,
};
use datafusion_physical_expr::{
EquivalenceProperties, OrderingEquivalenceProperties, PhysicalExpr,
PhysicalSortRequirement,
};
#[derive(Debug, Clone, PartialEq)]
/// Specifies partition column properties in terms of input ordering
pub enum PartitionSearchMode {
/// None of the columns among the partition columns is ordered.
Linear,
/// Some columns of the partition columns are ordered but not all
PartiallySorted(Vec<usize>),
/// All Partition columns are ordered (Also empty case)
Sorted,
}
/// Window execution plan
#[derive(Debug)]
pub struct BoundedWindowAggExec {
/// Input plan
input: Arc<dyn ExecutionPlan>,
/// Window function expression
window_expr: Vec<Arc<dyn WindowExpr>>,
/// Schema after the window is run
schema: SchemaRef,
/// Schema before the window
input_schema: SchemaRef,
/// Partition Keys
pub partition_keys: Vec<Arc<dyn PhysicalExpr>>,
/// Execution metrics
metrics: ExecutionPlanMetricsSet,
/// Partition by search mode
pub partition_search_mode: PartitionSearchMode,
/// Partition by indices that define ordering
// For example, if input ordering is ORDER BY a, b and window expression
// contains PARTITION BY b, a; `ordered_partition_by_indices` would be 1, 0.
// Similarly, if window expression contains PARTITION BY a, b; then
// `ordered_partition_by_indices` would be 0, 1.
// See `get_ordered_partition_by_indices` for more details.
ordered_partition_by_indices: Vec<usize>,
}
impl BoundedWindowAggExec {
/// Create a new execution plan for window aggregates
pub fn try_new(
window_expr: Vec<Arc<dyn WindowExpr>>,
input: Arc<dyn ExecutionPlan>,
input_schema: SchemaRef,
partition_keys: Vec<Arc<dyn PhysicalExpr>>,
partition_search_mode: PartitionSearchMode,
) -> Result<Self> {
let schema = create_schema(&input_schema, &window_expr)?;
let schema = Arc::new(schema);
let partition_by_exprs = window_expr[0].partition_by();
let ordered_partition_by_indices = match &partition_search_mode {
PartitionSearchMode::Sorted => {
let indices = get_ordered_partition_by_indices(
window_expr[0].partition_by(),
&input,
);
if indices.len() == partition_by_exprs.len() {
indices
} else {
(0..partition_by_exprs.len()).collect::<Vec<_>>()
}
}
PartitionSearchMode::PartiallySorted(ordered_indices) => {
ordered_indices.clone()
}
PartitionSearchMode::Linear => {
vec![]
}
};
Ok(Self {
input,
window_expr,
schema,
input_schema,
partition_keys,
metrics: ExecutionPlanMetricsSet::new(),
partition_search_mode,
ordered_partition_by_indices,
})
}
/// Window expressions
pub fn window_expr(&self) -> &[Arc<dyn WindowExpr>] {
&self.window_expr
}
/// Input plan
pub fn input(&self) -> &Arc<dyn ExecutionPlan> {
&self.input
}
/// Get the input schema before any window functions are applied
pub fn input_schema(&self) -> SchemaRef {
self.input_schema.clone()
}
/// Return the output sort order of partition keys: For example
/// OVER(PARTITION BY a, ORDER BY b) -> would give sorting of the column a
// We are sure that partition by columns are always at the beginning of sort_keys
// Hence returned `PhysicalSortExpr` corresponding to `PARTITION BY` columns can be used safely
// to calculate partition separation points
pub fn partition_by_sort_keys(&self) -> Result<Vec<PhysicalSortExpr>> {
// Partition by sort keys indices are stored in self.ordered_partition_by_indices.
let sort_keys = self.input.output_ordering().unwrap_or(&[]);
get_at_indices(sort_keys, &self.ordered_partition_by_indices)
}
/// Initializes the appropriate [`PartitionSearcher`] implementation from
/// the state.
fn get_search_algo(&self) -> Result<Box<dyn PartitionSearcher>> {
let partition_by_sort_keys = self.partition_by_sort_keys()?;
let ordered_partition_by_indices = self.ordered_partition_by_indices.clone();
Ok(match &self.partition_search_mode {
PartitionSearchMode::Sorted => {
// In Sorted mode, all partition by columns should be ordered.
if self.window_expr()[0].partition_by().len()
!= ordered_partition_by_indices.len()
{
return Err(DataFusionError::Execution("All partition by columns should have an ordering in Sorted mode.".to_string()));
}
Box::new(SortedSearch {
partition_by_sort_keys,
ordered_partition_by_indices,
})
}
PartitionSearchMode::Linear | PartitionSearchMode::PartiallySorted(_) => {
Box::new(LinearSearch::new(ordered_partition_by_indices))
}
})
}
}
impl ExecutionPlan for BoundedWindowAggExec {
/// Return a reference to Any that can be used for downcasting
fn as_any(&self) -> &dyn Any {
self
}
fn schema(&self) -> SchemaRef {
self.schema.clone()
}
fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> {
vec![self.input.clone()]
}
/// Get the output partitioning of this plan
fn output_partitioning(&self) -> Partitioning {
// As we can have repartitioning using the partition keys, this can
// be either one or more than one, depending on the presence of
// repartitioning.
self.input.output_partitioning()
}
fn unbounded_output(&self, children: &[bool]) -> Result<bool> {
Ok(children[0])
}
fn output_ordering(&self) -> Option<&[PhysicalSortExpr]> {
self.input().output_ordering()
}
fn required_input_ordering(&self) -> Vec<Option<Vec<PhysicalSortRequirement>>> {
let partition_bys = self.window_expr()[0].partition_by();
let order_keys = self.window_expr()[0].order_by();
if self.partition_search_mode != PartitionSearchMode::Sorted
|| self.ordered_partition_by_indices.len() >= partition_bys.len()
{
let partition_bys = self
.ordered_partition_by_indices
.iter()
.map(|idx| &partition_bys[*idx]);
vec![calc_requirements(partition_bys, order_keys)]
} else {
vec![calc_requirements(partition_bys, order_keys)]
}
}
fn required_input_distribution(&self) -> Vec<Distribution> {
if self.partition_keys.is_empty() {
debug!("No partition defined for BoundedWindowAggExec!!!");
vec![Distribution::SinglePartition]
} else {
vec![Distribution::HashPartitioned(self.partition_keys.clone())]
}
}
fn equivalence_properties(&self) -> EquivalenceProperties {
self.input().equivalence_properties()
}
/// Get the OrderingEquivalenceProperties within the plan
fn ordering_equivalence_properties(&self) -> OrderingEquivalenceProperties {
window_ordering_equivalence(&self.schema, &self.input, &self.window_expr)
}
fn maintains_input_order(&self) -> Vec<bool> {
vec![true]
}
fn with_new_children(
self: Arc<Self>,
children: Vec<Arc<dyn ExecutionPlan>>,
) -> Result<Arc<dyn ExecutionPlan>> {
Ok(Arc::new(BoundedWindowAggExec::try_new(
self.window_expr.clone(),
children[0].clone(),
self.input_schema.clone(),
self.partition_keys.clone(),
self.partition_search_mode.clone(),
)?))
}
fn execute(
&self,
partition: usize,
context: Arc<TaskContext>,
) -> Result<SendableRecordBatchStream> {
let input = self.input.execute(partition, context)?;
let search_mode = self.get_search_algo()?;
let stream = Box::pin(BoundedWindowAggStream::new(
self.schema.clone(),
self.window_expr.clone(),
input,
BaselineMetrics::new(&self.metrics, partition),
search_mode,
)?);
Ok(stream)
}
fn fmt_as(
&self,
t: DisplayFormatType,
f: &mut std::fmt::Formatter,
) -> std::fmt::Result {
match t {
DisplayFormatType::Default | DisplayFormatType::Verbose => {
write!(f, "BoundedWindowAggExec: ")?;
let g: Vec<String> = self
.window_expr
.iter()
.map(|e| {
format!(
"{}: {:?}, frame: {:?}",
e.name().to_owned(),
e.field(),
e.get_window_frame()
)
})
.collect();
let mode = &self.partition_search_mode;
write!(f, "wdw=[{}], mode=[{:?}]", g.join(", "), mode)?;
}
}
Ok(())
}
fn metrics(&self) -> Option<MetricsSet> {
Some(self.metrics.clone_inner())
}
fn statistics(&self) -> Statistics {
let input_stat = self.input.statistics();
let win_cols = self.window_expr.len();
let input_cols = self.input_schema.fields().len();
// TODO stats: some windowing function will maintain invariants such as min, max...
let mut column_statistics = Vec::with_capacity(win_cols + input_cols);
if let Some(input_col_stats) = input_stat.column_statistics {
column_statistics.extend(input_col_stats);
} else {
column_statistics.extend(vec![ColumnStatistics::default(); input_cols]);
}
column_statistics.extend(vec![ColumnStatistics::default(); win_cols]);
Statistics {
is_exact: input_stat.is_exact,
num_rows: input_stat.num_rows,
column_statistics: Some(column_statistics),
total_byte_size: None,
}
}
}
/// Trait that specifies how we search for (or calculate) partitions. It has two
/// implementations: [`SortedSearch`] and [`LinearSearch`].
trait PartitionSearcher: Send {
/// This method constructs output columns using the result of each window expression
/// (each entry in the output vector comes from a window expression).
/// Executor when producing output concatenates `input_buffer` (corresponding section), and
/// result of this function to generate output `RecordBatch`. `input_buffer` is used to determine
/// which sections of the window expression results should be used to generate output.
/// `partition_buffers` contains corresponding section of the `RecordBatch` for each partition.
/// `window_agg_states` stores per partition state for each window expression.
/// None case means that no result is generated
/// `Some(Vec<ArrayRef>)` is the result of each window expression.
fn calculate_out_columns(
&mut self,
input_buffer: &RecordBatch,
window_agg_states: &[PartitionWindowAggStates],
partition_buffers: &mut PartitionBatches,
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Option<Vec<ArrayRef>>>;
// Constructs corresponding batches for each partition for the record_batch.
fn evaluate_partition_batches(
&mut self,
record_batch: &RecordBatch,
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Vec<(PartitionKey, RecordBatch)>>;
/// Prunes the state.
fn prune(&mut self, _n_out: usize) {}
/// Marks the partition as done if we are sure that corresponding partition
/// cannot receive any more values.
fn mark_partition_end(&self, partition_buffers: &mut PartitionBatches);
/// Updates `input_buffer` and `partition_buffers` with the new `record_batch`.
fn update_partition_batch(
&mut self,
input_buffer: &mut RecordBatch,
record_batch: RecordBatch,
window_expr: &[Arc<dyn WindowExpr>],
partition_buffers: &mut PartitionBatches,
) -> Result<()> {
if record_batch.num_rows() > 0 {
let partition_batches =
self.evaluate_partition_batches(&record_batch, window_expr)?;
for (partition_row, partition_batch) in partition_batches {
let partition_batch_state = partition_buffers
.entry(partition_row)
.or_insert_with(|| PartitionBatchState {
record_batch: RecordBatch::new_empty(partition_batch.schema()),
is_end: false,
n_out_row: 0,
});
partition_batch_state.record_batch = concat_batches(
&partition_batch.schema(),
[&partition_batch_state.record_batch, &partition_batch],
)?;
}
}
self.mark_partition_end(partition_buffers);
*input_buffer = if input_buffer.num_rows() == 0 {
record_batch
} else {
concat_batches(&input_buffer.schema(), [input_buffer, &record_batch])?
};
Ok(())
}
}
/// This object encapsulates the algorithm state for a simple linear scan
/// algorithm for computing partitions.
pub struct LinearSearch {
/// Keeps the hash of input buffer calculated from PARTITION BY columns.
/// Its length is equal to the `input_buffer` length.
input_buffer_hashes: VecDeque<u64>,
/// Used during hash value calculation.
random_state: RandomState,
/// Input ordering and partition by key ordering need not be the same, so
/// this vector stores the mapping between them. For instance, if the input
/// is ordered by a, b and the window expression contains a PARTITION BY b, a
/// clause, this attribute stores [1, 0].
ordered_partition_by_indices: Vec<usize>,
/// We use this [`RawTable`] to calculate unique partitions for each new
/// RecordBatch. First entry in the tuple is the hash value, the second
/// entry is the unique ID for each partition (increments from 0 to n).
row_map_batch: RawTable<(u64, usize)>,
/// We use this [`RawTable`] to calculate the output columns that we can
/// produce at each cycle. First entry in the tuple is the hash value, the
/// second entry is the unique ID for each partition (increments from 0 to n).
/// The third entry stores how many new outputs are calculated for the
/// corresponding partition.
row_map_out: RawTable<(u64, usize, usize)>,
}
impl PartitionSearcher for LinearSearch {
/// This method constructs output columns using the result of each window expression.
// Assume input buffer is | Partition Buffers would be (Where each partition and its data is seperated)
// a, 2 | a, 2
// b, 2 | a, 2
// a, 2 | a, 2
// b, 2 |
// a, 2 | b, 2
// b, 2 | b, 2
// b, 2 | b, 2
// | b, 2
// Also assume we happen to calculate 2 new values for a, and 3 for b (To be calculate missing values we may need to consider future values).
// Partition buffers effectively will be
// a, 2, 1
// a, 2, 2
// a, 2, (missing)
//
// b, 2, 1
// b, 2, 2
// b, 2, 3
// b, 2, (missing)
// When partition buffers are mapped back to the original record batch. Result becomes
// a, 2, 1
// b, 2, 1
// a, 2, 2
// b, 2, 2
// a, 2, (missing)
// b, 2, 3
// b, 2, (missing)
// This function calculates the column result of window expression(s) (First 4 entry of 3rd column in the above section.)
// 1
// 1
// 2
// 2
// Above section corresponds to calculated result which can be emitted without breaking input buffer ordering.
fn calculate_out_columns(
&mut self,
input_buffer: &RecordBatch,
window_agg_states: &[PartitionWindowAggStates],
partition_buffers: &mut PartitionBatches,
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Option<Vec<ArrayRef>>> {
let partition_output_indices = self.calc_partition_output_indices(
input_buffer,
window_agg_states,
window_expr,
)?;
let n_window_col = window_agg_states.len();
let mut new_columns = vec![vec![]; n_window_col];
// Size of all_indices can be at most input_buffer.num_rows():
let mut all_indices = UInt32Builder::with_capacity(input_buffer.num_rows());
for (row, indices) in partition_output_indices {
let length = indices.len();
for (idx, window_agg_state) in window_agg_states.iter().enumerate() {
let partition = &window_agg_state[&row];
let values = partition.state.out_col.slice(0, length).clone();
new_columns[idx].push(values);
}
let partition_batch_state = &mut partition_buffers[&row];
// Store how many rows are generated for each partition
partition_batch_state.n_out_row = length;
// For each row keep corresponding index in the input record batch
all_indices.append_slice(&indices);
}
let all_indices = all_indices.finish();
if all_indices.is_empty() {
// We couldn't generate any new value, return early:
return Ok(None);
}
// Concatenate results for each column by converting `Vec<Vec<ArrayRef>>`
// to Vec<ArrayRef> where inner `Vec<ArrayRef>`s are converted to `ArrayRef`s.
let new_columns = new_columns
.iter()
.map(|items| {
concat(&items.iter().map(|e| e.as_ref()).collect::<Vec<_>>())
.map_err(DataFusionError::ArrowError)
})
.collect::<Result<Vec<_>>>()?;
// We should emit columns according to row index ordering.
let sorted_indices = sort_to_indices(&all_indices, None, None)?;
// Construct new column according to row ordering. This fixes ordering
get_arrayref_at_indices(&new_columns, &sorted_indices).map(Some)
}
fn evaluate_partition_batches(
&mut self,
record_batch: &RecordBatch,
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Vec<(PartitionKey, RecordBatch)>> {
let partition_bys =
self.evaluate_partition_by_column_values(record_batch, window_expr)?;
// NOTE: In Linear or PartiallySorted modes, we are sure that
// `partition_bys` are not empty.
// Calculate indices for each partition and construct a new record
// batch from the rows at these indices for each partition:
self.get_per_partition_indices(&partition_bys, record_batch)?
.into_iter()
.map(|(row, indices)| {
let mut new_indices = UInt32Builder::with_capacity(indices.len());
new_indices.append_slice(&indices);
let indices = new_indices.finish();
Ok((row, get_record_batch_at_indices(record_batch, &indices)?))
})
.collect()
}
fn prune(&mut self, n_out: usize) {
// Delete hashes for the rows that are outputted.
self.input_buffer_hashes.drain(0..n_out);
}
fn mark_partition_end(&self, partition_buffers: &mut PartitionBatches) {
// We should be in the `PartiallySorted` case, otherwise we can not
// tell when we are at the end of a given partition.
if !self.ordered_partition_by_indices.is_empty() {
if let Some((last_row, _)) = partition_buffers.last() {
let last_sorted_cols = self
.ordered_partition_by_indices
.iter()
.map(|idx| last_row[*idx].clone())
.collect::<Vec<_>>();
for (row, partition_batch_state) in partition_buffers.iter_mut() {
let sorted_cols = self
.ordered_partition_by_indices
.iter()
.map(|idx| &row[*idx]);
// All the partitions other than `last_sorted_cols` are done.
// We are sure that we will no longer receive values for these
// partitions (arrival of a new value would violate ordering).
partition_batch_state.is_end = !sorted_cols.eq(&last_sorted_cols);
}
}
}
}
}
impl LinearSearch {
/// Initialize a new [`LinearSearch`] partition searcher.
fn new(ordered_partition_by_indices: Vec<usize>) -> Self {
LinearSearch {
input_buffer_hashes: VecDeque::new(),
random_state: Default::default(),
ordered_partition_by_indices,
row_map_batch: RawTable::with_capacity(256),
row_map_out: RawTable::with_capacity(256),
}
}
/// Calculates partition by expression results for each window expression
/// on `record_batch`.
fn evaluate_partition_by_column_values(
&self,
record_batch: &RecordBatch,
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Vec<ArrayRef>> {
window_expr[0]
.partition_by()
.iter()
.map(|item| match item.evaluate(record_batch)? {
ColumnarValue::Array(array) => Ok(array),
ColumnarValue::Scalar(scalar) => Err(DataFusionError::Plan(format!(
"Sort operation is not applicable to scalar value {scalar}"
))),
})
.collect()
}
/// Calculate indices of each partition (according to PARTITION BY expression)
/// `columns` contain partition by expression results.
fn get_per_partition_indices(
&mut self,
columns: &[ArrayRef],
batch: &RecordBatch,
) -> Result<Vec<(PartitionKey, Vec<u32>)>> {
let mut batch_hashes = vec![0; batch.num_rows()];
create_hashes(columns, &self.random_state, &mut batch_hashes)?;
self.input_buffer_hashes.extend(&batch_hashes);
// reset row_map for new calculation
self.row_map_batch.clear();
// res stores PartitionKey and row indices (indices where these partition occurs in the `batch`) for each partition.
let mut result: Vec<(PartitionKey, Vec<u32>)> = vec![];
for (hash, row_idx) in batch_hashes.into_iter().zip(0u32..) {
let entry = self.row_map_batch.get_mut(hash, |(_, group_idx)| {
// We can safely get the first index of the partition indices
// since partition indices has one element during initialization.
let row = get_row_at_idx(columns, row_idx as usize).unwrap();
// Handle hash collusions with an equality check:
row.eq(&result[*group_idx].0)
});
if let Some((_, group_idx)) = entry {
result[*group_idx].1.push(row_idx)
} else {
self.row_map_batch
.insert(hash, (hash, result.len()), |(hash, _)| *hash);
let row = get_row_at_idx(columns, row_idx as usize)?;
// This is a new partition its only index is row_idx for now.
result.push((row, vec![row_idx]));
}
}
Ok(result)
}
/// Calculates partition keys and result indices for each partition.
/// The return value is a vector of tuples where the first entry stores
/// the partition key (unique for each partition) and the second entry
/// stores indices of the rows for which the partition is constructed.
fn calc_partition_output_indices(
&mut self,
input_buffer: &RecordBatch,
window_agg_states: &[PartitionWindowAggStates],
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Vec<(PartitionKey, Vec<u32>)>> {
let partition_by_columns =
self.evaluate_partition_by_column_values(input_buffer, window_expr)?;
// Reset the row_map state:
self.row_map_out.clear();
let mut partition_indices: Vec<(PartitionKey, Vec<u32>)> = vec![];
for (hash, row_idx) in self.input_buffer_hashes.iter().zip(0u32..) {
let entry = self.row_map_out.get_mut(*hash, |(_, group_idx, _)| {
let row =
get_row_at_idx(&partition_by_columns, row_idx as usize).unwrap();
row == partition_indices[*group_idx].0
});
if let Some((_, group_idx, n_out)) = entry {
let (_, indices) = &mut partition_indices[*group_idx];
if indices.len() >= *n_out {
break;
}
indices.push(row_idx);
} else {
let row = get_row_at_idx(&partition_by_columns, row_idx as usize)?;
let min_out = window_agg_states
.iter()
.map(|window_agg_state| {
window_agg_state
.get(&row)
.map(|partition| partition.state.out_col.len())
.unwrap_or(0)
})
.min()
.unwrap_or(0);
if min_out == 0 {
break;
}
self.row_map_out.insert(
*hash,
(*hash, partition_indices.len(), min_out),
|(hash, _, _)| *hash,
);
partition_indices.push((row, vec![row_idx]));
}
}
Ok(partition_indices)
}
}
/// This object encapsulates the algorithm state for sorted searching
/// when computing partitions.
pub struct SortedSearch {
/// Stores partition by columns and their ordering information
partition_by_sort_keys: Vec<PhysicalSortExpr>,
/// Input ordering and partition by key ordering need not be the same, so
/// this vector stores the mapping between them. For instance, if the input
/// is ordered by a, b and the window expression contains a PARTITION BY b, a
/// clause, this attribute stores [1, 0].
ordered_partition_by_indices: Vec<usize>,
}
impl PartitionSearcher for SortedSearch {
/// This method constructs new output columns using the result of each window expression.
fn calculate_out_columns(
&mut self,
_input_buffer: &RecordBatch,
window_agg_states: &[PartitionWindowAggStates],
partition_buffers: &mut PartitionBatches,
_window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Option<Vec<ArrayRef>>> {
let n_out = self.calculate_n_out_row(window_agg_states, partition_buffers);
if n_out == 0 {
Ok(None)
} else {
window_agg_states
.iter()
.map(|map| get_aggregate_result_out_column(map, n_out).map(Some))
.collect()
}
}
fn evaluate_partition_batches(
&mut self,
record_batch: &RecordBatch,
_window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Vec<(PartitionKey, RecordBatch)>> {
let num_rows = record_batch.num_rows();
// Calculate result of partition by column expressions
let partition_columns = self
.partition_by_sort_keys
.iter()
.map(|elem| elem.evaluate_to_sort_column(record_batch))
.collect::<Result<Vec<_>>>()?;
// Reorder `partition_columns` such that its ordering matches input ordering.
let partition_columns_ordered =
get_at_indices(&partition_columns, &self.ordered_partition_by_indices)?;
let partition_points =
evaluate_partition_ranges(num_rows, &partition_columns_ordered)?;
let partition_bys = partition_columns
.into_iter()
.map(|arr| arr.values)
.collect::<Vec<ArrayRef>>();
partition_points
.iter()
.map(|range| {
let row = get_row_at_idx(&partition_bys, range.start)?;
let len = range.end - range.start;
let slice = record_batch.slice(range.start, len);
Ok((row, slice))
})
.collect::<Result<Vec<_>>>()
}
fn mark_partition_end(&self, partition_buffers: &mut PartitionBatches) {
// In Sorted case. We can mark all partitions besides last partition as ended.
// We are sure that those partitions will never receive any values.
// (Otherwise ordering invariant is violated.)
let n_partitions = partition_buffers.len();
for (idx, (_, partition_batch_state)) in partition_buffers.iter_mut().enumerate()
{
partition_batch_state.is_end |= idx < n_partitions - 1;
}
}
}
impl SortedSearch {
/// Calculates how many rows we can output.
fn calculate_n_out_row(
&mut self,
window_agg_states: &[PartitionWindowAggStates],
partition_buffers: &mut PartitionBatches,
) -> usize {
// Different window aggregators may produce results at different rates.
// We produce the overall batch result only as fast as the slowest one.
let mut counts = vec![];
let out_col_counts = window_agg_states.iter().map(|window_agg_state| {
// Store how many elements are generated for the current
// window expression:
let mut cur_window_expr_out_result_len = 0;
// We iterate over `window_agg_state`, which is an IndexMap.
// Iterations follow the insertion order, hence we preserve
// sorting when partition columns are sorted.
let mut per_partition_out_results = HashMap::new();
for (row, WindowState { state, .. }) in window_agg_state.iter() {
cur_window_expr_out_result_len += state.out_col.len();
let count = per_partition_out_results.entry(row).or_insert(0);
if *count < state.out_col.len() {
*count = state.out_col.len();
}
// If we do not generate all results for the current
// partition, we do not generate results for next
// partition -- otherwise we will lose input ordering.
if state.n_row_result_missing > 0 {
break;
}
}
counts.push(per_partition_out_results);
cur_window_expr_out_result_len
});
argmin(out_col_counts).map_or(0, |(min_idx, minima)| {
for (row, count) in counts.swap_remove(min_idx).into_iter() {
let partition_batch = &mut partition_buffers[row];
partition_batch.n_out_row = count;
}
minima
})
}
}
fn create_schema(
input_schema: &Schema,
window_expr: &[Arc<dyn WindowExpr>],
) -> Result<Schema> {
let capacity = input_schema.fields().len() + window_expr.len();
let mut builder = SchemaBuilder::with_capacity(capacity);
builder.extend(input_schema.fields.iter().cloned());
// append results to the schema
for expr in window_expr {
builder.push(expr.field()?);
}
Ok(builder.finish())
}
/// Stream for the bounded window aggregation plan.
pub struct BoundedWindowAggStream {
schema: SchemaRef,
input: SendableRecordBatchStream,
/// The record batch executor receives as input (i.e. the columns needed
/// while calculating aggregation results).
input_buffer: RecordBatch,
/// We separate `input_buffer` based on partitions (as
/// determined by PARTITION BY columns) and store them per partition
/// in `partition_batches`. We use this variable when calculating results
/// for each window expression. This enables us to use the same batch for
/// different window expressions without copying.
// Note that we could keep record batches for each window expression in
// `PartitionWindowAggStates`. However, this would use more memory (as
// many times as the number of window expressions).
partition_buffers: PartitionBatches,
/// An executor can run multiple window expressions if the PARTITION BY
/// and ORDER BY sections are same. We keep state of the each window
/// expression inside `window_agg_states`.
window_agg_states: Vec<PartitionWindowAggStates>,
finished: bool,
window_expr: Vec<Arc<dyn WindowExpr>>,
baseline_metrics: BaselineMetrics,
/// Search mode for partition columns. This determines the algorithm with
/// which we group each partition.
search_mode: Box<dyn PartitionSearcher>,
}
impl BoundedWindowAggStream {
/// Prunes sections of the state that are no longer needed when calculating
/// results (as determined by window frame boundaries and number of results generated).
// For instance, if first `n` (not necessarily same with `n_out`) elements are no longer needed to
// calculate window expression result (outside the window frame boundary) we retract first `n` elements
// from `self.partition_batches` in corresponding partition.
// For instance, if `n_out` number of rows are calculated, we can remove
// first `n_out` rows from `self.input_buffer`.
fn prune_state(&mut self, n_out: usize) -> Result<()> {
// Prune `self.window_agg_states`:
self.prune_out_columns();
// Prune `self.partition_batches`:
self.prune_partition_batches();
// Prune `self.input_buffer`:
self.prune_input_batch(n_out)?;
// Prune internal state of search algorithm.
self.search_mode.prune(n_out);
Ok(())
}
}
impl Stream for BoundedWindowAggStream {
type Item = Result<RecordBatch>;
fn poll_next(
mut self: Pin<&mut Self>,
cx: &mut Context<'_>,
) -> Poll<Option<Self::Item>> {
let poll = self.poll_next_inner(cx);
self.baseline_metrics.record_poll(poll)
}
}
impl BoundedWindowAggStream {
/// Create a new BoundedWindowAggStream
fn new(
schema: SchemaRef,
window_expr: Vec<Arc<dyn WindowExpr>>,
input: SendableRecordBatchStream,
baseline_metrics: BaselineMetrics,
search_mode: Box<dyn PartitionSearcher>,
) -> Result<Self> {
let state = window_expr.iter().map(|_| IndexMap::new()).collect();
let empty_batch = RecordBatch::new_empty(schema.clone());
Ok(Self {
schema,
input,
input_buffer: empty_batch,
partition_buffers: IndexMap::new(),
window_agg_states: state,
finished: false,
window_expr,
baseline_metrics,
search_mode,
})
}
fn compute_aggregates(&mut self) -> Result<RecordBatch> {
// calculate window cols
for (cur_window_expr, state) in
self.window_expr.iter().zip(&mut self.window_agg_states)
{
cur_window_expr.evaluate_stateful(&self.partition_buffers, state)?;
}
let schema = self.schema.clone();
let window_expr_out = self.search_mode.calculate_out_columns(
&self.input_buffer,
&self.window_agg_states,
&mut self.partition_buffers,
&self.window_expr,
)?;
if let Some(window_expr_out) = window_expr_out {
let n_out = window_expr_out[0].len();
// right append new columns to corresponding section in the original input buffer.
let columns_to_show = self
.input_buffer
.columns()
.iter()
.map(|elem| elem.slice(0, n_out))
.chain(window_expr_out.into_iter())
.collect::<Vec<_>>();
let n_generated = columns_to_show[0].len();
self.prune_state(n_generated)?;
Ok(RecordBatch::try_new(schema, columns_to_show)?)
} else {
Ok(RecordBatch::new_empty(schema))
}
}
#[inline]
fn poll_next_inner(
&mut self,
cx: &mut Context<'_>,
) -> Poll<Option<Result<RecordBatch>>> {
if self.finished {
return Poll::Ready(None);
}
let result = match ready!(self.input.poll_next_unpin(cx)) {
Some(Ok(batch)) => {
self.search_mode.update_partition_batch(
&mut self.input_buffer,
batch,
&self.window_expr,
&mut self.partition_buffers,
)?;
self.compute_aggregates()
}
Some(Err(e)) => Err(e),
None => {
self.finished = true;
for (_, partition_batch_state) in self.partition_buffers.iter_mut() {
partition_batch_state.is_end = true;
}
self.compute_aggregates()
}
};
Poll::Ready(Some(result))
}
/// Prunes the sections of the record batch (for each partition)
/// that we no longer need to calculate the window function result.
fn prune_partition_batches(&mut self) {
// Remove partitions which we know already ended (is_end flag is true).
// Since the retain method preserves insertion order, we still have
// ordering in between partitions after removal.
self.partition_buffers
.retain(|_, partition_batch_state| !partition_batch_state.is_end);
// The data in `self.partition_batches` is used by all window expressions.
// Therefore, when removing from `self.partition_batches`, we need to remove
// from the earliest range boundary among all window expressions. Variable
// `n_prune_each_partition` fill the earliest range boundary information for
// each partition. This way, we can delete the no-longer-needed sections from
// `self.partition_batches`.
// For instance, if window frame one uses [10, 20] and window frame two uses
// [5, 15]; we only prune the first 5 elements from the corresponding record
// batch in `self.partition_batches`.
// Calculate how many elements to prune for each partition batch
let mut n_prune_each_partition = HashMap::new();
for window_agg_state in self.window_agg_states.iter_mut() {
window_agg_state.retain(|_, WindowState { state, .. }| !state.is_end);
for (partition_row, WindowState { state: value, .. }) in window_agg_state {
let n_prune =
min(value.window_frame_range.start, value.last_calculated_index);
if let Some(current) = n_prune_each_partition.get_mut(partition_row) {
if n_prune < *current {
*current = n_prune;
}
} else {
n_prune_each_partition.insert(partition_row.clone(), n_prune);
}
}
}
// Retract no longer needed parts during window calculations from partition batch:
for (partition_row, n_prune) in n_prune_each_partition.iter() {
let pb_state = &mut self.partition_buffers[partition_row];
let batch = &pb_state.record_batch;
pb_state.record_batch = batch.slice(*n_prune, batch.num_rows() - n_prune);
pb_state.n_out_row = 0;
// Update state indices since we have pruned some rows from the beginning:
for window_agg_state in self.window_agg_states.iter_mut() {
window_agg_state[partition_row].state.prune_state(*n_prune);
}
}
}
/// Prunes the section of the input batch whose aggregate results
/// are calculated and emitted.
fn prune_input_batch(&mut self, n_out: usize) -> Result<()> {
// Prune first n_out rows from the input_buffer
let n_to_keep = self.input_buffer.num_rows() - n_out;
let batch_to_keep = self
.input_buffer
.columns()
.iter()
.map(|elem| elem.slice(n_out, n_to_keep))
.collect::<Vec<_>>();
self.input_buffer =
RecordBatch::try_new(self.input_buffer.schema(), batch_to_keep)?;
Ok(())
}
/// Prunes emitted parts from WindowAggState `out_col` field.
fn prune_out_columns(&mut self) {
// We store generated columns for each window expression in the `out_col`
// field of `WindowAggState`. Given how many rows are emitted, we remove
// these sections from state.
for partition_window_agg_states in self.window_agg_states.iter_mut() {
// Remove `n_out` entries from the `out_col` field of `WindowAggState`.
// `n_out` is stored in `self.partition_buffers` for each partition.
// If `is_end` is set, directly remove them; this shrinks the hash map.
partition_window_agg_states
.retain(|_, partition_batch_state| !partition_batch_state.state.is_end);
for (
partition_key,
WindowState {
state: WindowAggState { out_col, .. },
..
},
) in partition_window_agg_states
{
let partition_batch = &mut self.partition_buffers[partition_key];
let n_to_del = partition_batch.n_out_row;
let n_to_keep = out_col.len() - n_to_del;
*out_col = out_col.slice(n_to_del, n_to_keep);
}
}
}
}
impl RecordBatchStream for BoundedWindowAggStream {
/// Get the schema
fn schema(&self) -> SchemaRef {
self.schema.clone()
}
}
// Gets the index of minimum entry, returns None if empty.
fn argmin<T: PartialOrd>(data: impl Iterator<Item = T>) -> Option<(usize, T)> {
data.enumerate()
.min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(Ordering::Equal))
}
/// Calculates the section we can show results for expression
fn get_aggregate_result_out_column(
partition_window_agg_states: &PartitionWindowAggStates,
len_to_show: usize,
) -> Result<ArrayRef> {
let mut result = None;
let mut running_length = 0;
// We assume that iteration order is according to insertion order
for (
_,
WindowState {
state: WindowAggState { out_col, .. },
..
},
) in partition_window_agg_states
{
if running_length < len_to_show {
let n_to_use = min(len_to_show - running_length, out_col.len());
let slice_to_use = out_col.slice(0, n_to_use);
result = Some(match result {
Some(arr) => concat(&[&arr, &slice_to_use])?,
None => slice_to_use,
});
running_length += n_to_use;
} else {
break;
}
}
if running_length != len_to_show {
return Err(DataFusionError::Execution(format!(
"Generated row number should be {len_to_show}, it is {running_length}"
)));
}
result
.ok_or_else(|| DataFusionError::Execution("Should contain something".to_string()))
}