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Refactor window aggregation, simplify batch processing logic #516

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merged 2 commits into from
Jun 7, 2021
Merged

Refactor window aggregation, simplify batch processing logic #516

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9ce29c9
refactor sort exec stream and combine batches
Jun 6, 2021
d1baa63
refactor async function
Jun 6, 2021
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1 change: 0 additions & 1 deletion datafusion/src/physical_plan/sort.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -241,7 +241,6 @@ impl SortStream {
sort_time: Arc<SQLMetric>,
) -> Self {
let (tx, rx) = futures::channel::oneshot::channel();

let schema = input.schema();
tokio::spawn(async move {
let schema = input.schema();
Expand Down
149 changes: 71 additions & 78 deletions datafusion/src/physical_plan/windows.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@

use crate::error::{DataFusionError, Result};
use crate::physical_plan::{
aggregates,
aggregates, common,
expressions::{Literal, NthValue, RowNumber},
type_coercion::coerce,
window_functions::signature_for_built_in,
Expand All @@ -29,20 +29,18 @@ use crate::physical_plan::{
RecordBatchStream, SendableRecordBatchStream, WindowAccumulator, WindowExpr,
};
use crate::scalar::ScalarValue;
use arrow::compute::concat;
use arrow::{
array::{Array, ArrayRef},
array::ArrayRef,
datatypes::{Field, Schema, SchemaRef},
error::{ArrowError, Result as ArrowResult},
record_batch::RecordBatch,
};
use async_trait::async_trait;
use futures::stream::{Stream, StreamExt};
use futures::stream::Stream;
use futures::Future;
use pin_project_lite::pin_project;
use std::any::Any;
use std::convert::TryInto;
use std::iter;
use std::pin::Pin;
use std::sync::Arc;
use std::task::{Context, Poll};
Expand Down Expand Up @@ -339,22 +337,15 @@ fn window_aggregate_batch(
window_accumulators: &mut [WindowAccumulatorItem],
expressions: &[Vec<Arc<dyn PhysicalExpr>>],
) -> Result<Vec<Option<ArrayRef>>> {
// 1.1 iterate accumulators and respective expressions together
// 1.2 evaluate expressions
// 1.3 update / merge window accumulators with the expressions' values

// 1.1
window_accumulators
.iter_mut()
.zip(expressions)
.map(|(window_acc, expr)| {
// 1.2
let values = &expr
.iter()
.map(|e| e.evaluate(batch))
.map(|e| e.evaluate(&batch))
.map(|r| r.map(|v| v.into_array(batch.num_rows())))
.collect::<Result<Vec<_>>>()?;

window_acc.scan_batch(batch.num_rows(), values)
})
.into_iter()
Expand All @@ -380,81 +371,58 @@ fn create_window_accumulators(
.collect::<Result<Vec<_>>>()
}

async fn compute_window_aggregate(
schema: SchemaRef,
/// Compute the window aggregate columns
///
/// 1. get a list of window accumulators
/// 2. evaluate the args
/// 3. scan args with window functions
/// 4. concat with final aggregations
///
/// FIXME so far this fn does not support:
/// 1. partition by
/// 2. order by
/// 3. window frame
///
/// which will require further work:
/// 1. inter-partition order by using vec partition-point (https://github.com/apache/arrow-datafusion/issues/360)
/// 2. inter-partition parallelism using one-shot channel (https://github.com/apache/arrow-datafusion/issues/299)
/// 3. convert aggregation based window functions to be self-contain so that: (https://github.com/apache/arrow-datafusion/issues/361)
/// a. some can be grow-only window-accumulating
/// b. some can be grow-and-shrink window-accumulating
/// c. some can be based on segment tree
fn compute_window_aggregates(
window_expr: Vec<Arc<dyn WindowExpr>>,
mut input: SendableRecordBatchStream,
) -> ArrowResult<RecordBatch> {
let mut window_accumulators = create_window_accumulators(&window_expr)
.map_err(DataFusionError::into_arrow_external_error)?;

let expressions = window_expressions(&window_expr)
.map_err(DataFusionError::into_arrow_external_error)?;

let expressions = Arc::new(expressions);

// TODO each element shall have some size hint
let mut accumulator: Vec<Vec<ArrayRef>> =
iter::repeat(vec![]).take(window_expr.len()).collect();

let mut original_batches: Vec<RecordBatch> = vec![];

let mut total_num_rows = 0;

while let Some(batch) = input.next().await {
let batch = batch?;
total_num_rows += batch.num_rows();
original_batches.push(batch.clone());

let batch_aggregated =
window_aggregate_batch(&batch, &mut window_accumulators, &expressions)
.map_err(DataFusionError::into_arrow_external_error)?;
accumulator.iter_mut().zip(batch_aggregated).for_each(
|(acc_for_window, window_batch)| {
if let Some(data) = window_batch {
acc_for_window.push(data);
}
},
);
batch: &RecordBatch,
) -> Result<Vec<ArrayRef>> {
let mut window_accumulators = create_window_accumulators(&window_expr)?;
let expressions = Arc::new(window_expressions(&window_expr)?);
let num_rows = batch.num_rows();
let window_aggregates =
window_aggregate_batch(batch, &mut window_accumulators, &expressions)?;
let final_aggregates = finalize_window_aggregation(&window_accumulators)?;

// both must equal to window_expr.len()
if window_aggregates.len() != final_aggregates.len() {
return Err(DataFusionError::Internal(
"Impossibly got len mismatch".to_owned(),
));
}

let aggregated_mapped = finalize_window_aggregation(&window_accumulators)
.map_err(DataFusionError::into_arrow_external_error)?;

let mut columns: Vec<ArrayRef> = accumulator
window_aggregates
.iter()
.zip(aggregated_mapped)
.map(|(acc, agg)| {
Ok(match (acc, agg) {
(acc, Some(scalar_value)) if acc.is_empty() => {
scalar_value.to_array_of_size(total_num_rows)
}
(acc, None) if !acc.is_empty() => {
let vec_array: Vec<&dyn Array> =
acc.iter().map(|arc| arc.as_ref()).collect();
concat(&vec_array)?
}
.zip(final_aggregates)
.map(|(wa, fa)| {
Ok(match (wa, fa) {
(None, Some(fa)) => fa.to_array_of_size(num_rows),
(Some(wa), None) if wa.len() == num_rows => wa.clone(),
_ => {
return Err(DataFusionError::Execution(
"Invalid window function behavior".to_owned(),
))
}
})
})
.collect::<Result<Vec<ArrayRef>>>()
.map_err(DataFusionError::into_arrow_external_error)?;

for i in 0..(schema.fields().len() - window_expr.len()) {
let col = concat(
&original_batches
.iter()
.map(|batch| batch.column(i).as_ref())
.collect::<Vec<_>>(),
)?;
columns.push(col);
}

RecordBatch::try_new(schema.clone(), columns)
.collect()
}

impl WindowAggStream {
Expand All @@ -467,7 +435,8 @@ impl WindowAggStream {
let (tx, rx) = futures::channel::oneshot::channel();
let schema_clone = schema.clone();
tokio::spawn(async move {
let result = compute_window_aggregate(schema_clone, window_expr, input).await;
let schema = schema_clone.clone();
let result = WindowAggStream::process(input, window_expr, schema).await;
tx.send(result)
});

Expand All @@ -477,6 +446,30 @@ impl WindowAggStream {
schema,
}
}

async fn process(
input: SendableRecordBatchStream,
window_expr: Vec<Arc<dyn WindowExpr>>,
schema: SchemaRef,
) -> ArrowResult<RecordBatch> {
let input_schema = input.schema();
let batches = common::collect(input)
.await
.map_err(DataFusionError::into_arrow_external_error)?;
let batch = common::combine_batches(&batches, input_schema.clone())?;
if let Some(batch) = batch {
// calculate window cols
let mut columns = compute_window_aggregates(window_expr, &batch)
.map_err(DataFusionError::into_arrow_external_error)?;
// combine with the original cols
// note the setup of window aggregates is that they newly calculated window
// expressions are always prepended to the columns
columns.extend_from_slice(batch.columns());
RecordBatch::try_new(schema, columns)
} else {
Ok(RecordBatch::new_empty(schema))
}
}
}

impl Stream for WindowAggStream {
Expand Down