datafusion_physical_plan/sorts/

sort.rs

// 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.

//! Sort that deals with an arbitrary size of the input.
//! It will do in-memory sorting if it has enough memory budget
//! but spills to disk if needed.

use std::any::Any;
use std::fmt;
use std::fmt::{Debug, Formatter};
use std::sync::Arc;

use crate::common::spawn_buffered;
use crate::execution_plan::{Boundedness, CardinalityEffect, EmissionType};
use crate::expressions::PhysicalSortExpr;
use crate::limit::LimitStream;
use crate::metrics::{
    BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricBuilder, MetricsSet,
};
use crate::projection::{make_with_child, update_expr, ProjectionExec};
use crate::sorts::streaming_merge::StreamingMergeBuilder;
use crate::spill::{
    get_record_batch_memory_size, read_spill_as_stream, spill_record_batches,
};
use crate::stream::RecordBatchStreamAdapter;
use crate::topk::TopK;
use crate::{
    DisplayAs, DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan,
    ExecutionPlanProperties, Partitioning, PlanProperties, SendableRecordBatchStream,
    Statistics,
};

use arrow::array::{
    Array, RecordBatch, RecordBatchOptions, StringViewArray, UInt32Array,
};
use arrow::compute::{concat_batches, lexsort_to_indices, take_arrays, SortColumn};
use arrow::datatypes::{DataType, SchemaRef};
use arrow::row::{RowConverter, SortField};
use datafusion_common::{internal_err, Result};
use datafusion_execution::disk_manager::RefCountedTempFile;
use datafusion_execution::memory_pool::{MemoryConsumer, MemoryReservation};
use datafusion_execution::runtime_env::RuntimeEnv;
use datafusion_execution::TaskContext;
use datafusion_physical_expr::LexOrdering;
use datafusion_physical_expr_common::sort_expr::LexRequirement;

use futures::{StreamExt, TryStreamExt};
use log::{debug, trace};

struct ExternalSorterMetrics {
    /// metrics
    baseline: BaselineMetrics,

    /// count of spills during the execution of the operator
    spill_count: Count,

    /// total spilled bytes during the execution of the operator
    spilled_bytes: Count,

    /// total spilled rows during the execution of the operator
    spilled_rows: Count,
}

impl ExternalSorterMetrics {
    fn new(metrics: &ExecutionPlanMetricsSet, partition: usize) -> Self {
        Self {
            baseline: BaselineMetrics::new(metrics, partition),
            spill_count: MetricBuilder::new(metrics).spill_count(partition),
            spilled_bytes: MetricBuilder::new(metrics).spilled_bytes(partition),
            spilled_rows: MetricBuilder::new(metrics).spilled_rows(partition),
        }
    }
}

/// Sorts an arbitrary sized, unsorted, stream of [`RecordBatch`]es to
/// a total order. Depending on the input size and memory manager
/// configuration, writes intermediate results to disk ("spills")
/// using Arrow IPC format.
///
/// # Algorithm
///
/// 1. get a non-empty new batch from input
///
/// 2. check with the memory manager there is sufficient space to
///    buffer the batch in memory 2.1 if memory sufficient, buffer
///    batch in memory, go to 1.
///
/// 2.2 if no more memory is available, sort all buffered batches and
///     spill to file.  buffer the next batch in memory, go to 1.
///
/// 3. when input is exhausted, merge all in memory batches and spills
///    to get a total order.
///
/// # When data fits in available memory
///
/// If there is sufficient memory, data is sorted in memory to produce the output
///
/// ```text
///    ┌─────┐
///    │  2  │
///    │  3  │
///    │  1  │─ ─ ─ ─ ─ ─ ─ ─ ─ ┐
///    │  4  │
///    │  2  │                  │
///    └─────┘                  ▼
///    ┌─────┐
///    │  1  │              In memory
///    │  4  │─ ─ ─ ─ ─ ─▶ sort/merge  ─ ─ ─ ─ ─▶  total sorted output
///    │  1  │
///    └─────┘                  ▲
///      ...                    │
///
///    ┌─────┐                  │
///    │  4  │
///    │  3  │─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
///    └─────┘
///
/// in_mem_batches
///
/// ```
///
/// # When data does not fit in available memory
///
///  When memory is exhausted, data is first sorted and written to one
///  or more spill files on disk:
///
/// ```text
///    ┌─────┐                               .─────────────────.
///    │  2  │                              (                   )
///    │  3  │                              │`─────────────────'│
///    │  1  │─ ─ ─ ─ ─ ─ ─                 │  ┌────┐           │
///    │  4  │             │                │  │ 1  │░          │
///    │  2  │                              │  │... │░          │
///    └─────┘             ▼                │  │ 4  │░  ┌ ─ ─   │
///    ┌─────┐                              │  └────┘░    1  │░ │
///    │  1  │         In memory            │   ░░░░░░  │    ░░ │
///    │  4  │─ ─ ▶   sort/merge    ─ ─ ─ ─ ┼ ─ ─ ─ ─ ─▶ ... │░ │
///    │  1  │     and write to file        │           │    ░░ │
///    └─────┘                              │             4  │░ │
///      ...               ▲                │           └░─░─░░ │
///                        │                │            ░░░░░░ │
///    ┌─────┐                              │.─────────────────.│
///    │  4  │             │                (                   )
///    │  3  │─ ─ ─ ─ ─ ─ ─                  `─────────────────'
///    └─────┘
///
/// in_mem_batches                                  spills
///                                         (file on disk in Arrow
///                                               IPC format)
/// ```
///
/// Once the input is completely read, the spill files are read and
/// merged with any in memory batches to produce a single total sorted
/// output:
///
/// ```text
///   .─────────────────.
///  (                   )
///  │`─────────────────'│
///  │  ┌────┐           │
///  │  │ 1  │░          │
///  │  │... │─ ─ ─ ─ ─ ─│─ ─ ─ ─ ─ ─
///  │  │ 4  │░ ┌────┐   │           │
///  │  └────┘░ │ 1  │░  │           ▼
///  │   ░░░░░░ │    │░  │
///  │          │... │─ ─│─ ─ ─ ▶ merge  ─ ─ ─▶  total sorted output
///  │          │    │░  │
///  │          │ 4  │░  │           ▲
///  │          └────┘░  │           │
///  │           ░░░░░░  │
///  │.─────────────────.│           │
///  (                   )
///   `─────────────────'            │
///         spills
///                                  │
///
///                                  │
///
///     ┌─────┐                      │
///     │  1  │
///     │  4  │─ ─ ─ ─               │
///     └─────┘       │
///       ...                   In memory
///                   └ ─ ─ ─▶  sort/merge
///     ┌─────┐
///     │  4  │                      ▲
///     │  3  │─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
///     └─────┘
///
///  in_mem_batches
/// ```
struct ExternalSorter {
    // ========================================================================
    // PROPERTIES:
    // Fields that define the sorter's configuration and remain constant
    // ========================================================================
    /// Schema of the output (and the input)
    schema: SchemaRef,
    /// Sort expressions
    expr: Arc<[PhysicalSortExpr]>,
    /// If Some, the maximum number of output rows that will be produced
    fetch: Option<usize>,
    /// The target number of rows for output batches
    batch_size: usize,
    /// If the in size of buffered memory batches is below this size,
    /// the data will be concatenated and sorted in place rather than
    /// sort/merged.
    sort_in_place_threshold_bytes: usize,

    // ========================================================================
    // STATE BUFFERS:
    // Fields that hold intermediate data during sorting
    // ========================================================================
    /// Potentially unsorted in memory buffer
    in_mem_batches: Vec<RecordBatch>,
    /// if `Self::in_mem_batches` are sorted
    in_mem_batches_sorted: bool,

    /// If data has previously been spilled, the locations of the
    /// spill files (in Arrow IPC format)
    spills: Vec<RefCountedTempFile>,

    // ========================================================================
    // EXECUTION RESOURCES:
    // Fields related to managing execution resources and monitoring performance.
    // ========================================================================
    /// Runtime metrics
    metrics: ExternalSorterMetrics,
    /// A handle to the runtime to get spill files
    runtime: Arc<RuntimeEnv>,
    /// Reservation for in_mem_batches
    reservation: MemoryReservation,

    /// Reservation for the merging of in-memory batches. If the sort
    /// might spill, `sort_spill_reservation_bytes` will be
    /// pre-reserved to ensure there is some space for this sort/merge.
    merge_reservation: MemoryReservation,
    /// How much memory to reserve for performing in-memory sort/merges
    /// prior to spilling.
    sort_spill_reservation_bytes: usize,
}

impl ExternalSorter {
    // TODO: make a builder or some other nicer API to avoid the
    // clippy warning
    #[allow(clippy::too_many_arguments)]
    pub fn new(
        partition_id: usize,
        schema: SchemaRef,
        expr: LexOrdering,
        batch_size: usize,
        fetch: Option<usize>,
        sort_spill_reservation_bytes: usize,
        sort_in_place_threshold_bytes: usize,
        metrics: &ExecutionPlanMetricsSet,
        runtime: Arc<RuntimeEnv>,
    ) -> Self {
        let metrics = ExternalSorterMetrics::new(metrics, partition_id);
        let reservation = MemoryConsumer::new(format!("ExternalSorter[{partition_id}]"))
            .with_can_spill(true)
            .register(&runtime.memory_pool);

        let merge_reservation =
            MemoryConsumer::new(format!("ExternalSorterMerge[{partition_id}]"))
                .register(&runtime.memory_pool);

        Self {
            schema,
            in_mem_batches: vec![],
            in_mem_batches_sorted: false,
            spills: vec![],
            expr: expr.into(),
            metrics,
            fetch,
            reservation,
            merge_reservation,
            runtime,
            batch_size,
            sort_spill_reservation_bytes,
            sort_in_place_threshold_bytes,
        }
    }

    /// Appends an unsorted [`RecordBatch`] to `in_mem_batches`
    ///
    /// Updates memory usage metrics, and possibly triggers spilling to disk
    async fn insert_batch(&mut self, input: RecordBatch) -> Result<()> {
        if input.num_rows() == 0 {
            return Ok(());
        }

        self.reserve_memory_for_merge()?;

        let size = get_reserved_byte_for_record_batch(&input);
        if self.reservation.try_grow(size).is_err() {
            self.sort_or_spill_in_mem_batches().await?;
            // We've already freed more than half of reserved memory,
            // so we can grow the reservation again. There's nothing we can do
            // if this try_grow fails.
            self.reservation.try_grow(size)?;
        }

        self.in_mem_batches.push(input);
        self.in_mem_batches_sorted = false;
        Ok(())
    }

    fn spilled_before(&self) -> bool {
        !self.spills.is_empty()
    }

    /// Returns the final sorted output of all batches inserted via
    /// [`Self::insert_batch`] as a stream of [`RecordBatch`]es.
    ///
    /// This process could either be:
    ///
    /// 1. An in-memory sort/merge (if the input fit in memory)
    ///
    /// 2. A combined streaming merge incorporating both in-memory
    ///    batches and data from spill files on disk.
    fn sort(&mut self) -> Result<SendableRecordBatchStream> {
        // Release the memory reserved for merge back to the pool so
        // there is some left when `in_mem_sort_stream` requests an
        // allocation.
        self.merge_reservation.free();

        if self.spilled_before() {
            let mut streams = vec![];
            if !self.in_mem_batches.is_empty() {
                let in_mem_stream =
                    self.in_mem_sort_stream(self.metrics.baseline.intermediate())?;
                streams.push(in_mem_stream);
            }

            for spill in self.spills.drain(..) {
                if !spill.path().exists() {
                    return internal_err!("Spill file {:?} does not exist", spill.path());
                }
                let stream = read_spill_as_stream(spill, Arc::clone(&self.schema), 2)?;
                streams.push(stream);
            }

            let expressions: LexOrdering = self.expr.iter().cloned().collect();

            StreamingMergeBuilder::new()
                .with_streams(streams)
                .with_schema(Arc::clone(&self.schema))
                .with_expressions(expressions.as_ref())
                .with_metrics(self.metrics.baseline.clone())
                .with_batch_size(self.batch_size)
                .with_fetch(self.fetch)
                .with_reservation(self.merge_reservation.new_empty())
                .build()
        } else {
            self.in_mem_sort_stream(self.metrics.baseline.clone())
        }
    }

    /// How much memory is buffered in this `ExternalSorter`?
    fn used(&self) -> usize {
        self.reservation.size()
    }

    /// How many bytes have been spilled to disk?
    fn spilled_bytes(&self) -> usize {
        self.metrics.spilled_bytes.value()
    }

    /// How many rows have been spilled to disk?
    fn spilled_rows(&self) -> usize {
        self.metrics.spilled_rows.value()
    }

    /// How many spill files have been created?
    fn spill_count(&self) -> usize {
        self.metrics.spill_count.value()
    }

    /// Writes any `in_memory_batches` to a spill file and clears
    /// the batches. The contents of the spill file are sorted.
    ///
    /// Returns the amount of memory freed.
    async fn spill(&mut self) -> Result<usize> {
        // we could always get a chance to free some memory as long as we are holding some
        if self.in_mem_batches.is_empty() {
            return Ok(0);
        }

        self.organize_stringview_arrays()?;

        debug!("Spilling sort data of ExternalSorter to disk whilst inserting");

        let spill_file = self.runtime.disk_manager.create_tmp_file("Sorting")?;
        let batches = std::mem::take(&mut self.in_mem_batches);
        let (spilled_rows, spilled_bytes) = spill_record_batches(
            &batches,
            spill_file.path().into(),
            Arc::clone(&self.schema),
        )?;
        let used = self.reservation.free();
        self.metrics.spill_count.add(1);
        self.metrics.spilled_bytes.add(spilled_bytes);
        self.metrics.spilled_rows.add(spilled_rows);
        self.spills.push(spill_file);
        Ok(used)
    }

    /// Reconstruct `self.in_mem_batches` to organize the payload buffers of each
    /// `StringViewArray` in sequential order by calling `gc()` on them.
    ///
    /// Note this is a workaround until <https://github.com/apache/arrow-rs/issues/7185> is
    /// available
    ///
    /// # Rationale
    /// After (merge-based) sorting, all batches will be sorted into a single run,
    /// but physically this sorted run is chunked into many small batches. For
    /// `StringViewArray`s inside each sorted run, their inner buffers are not
    /// re-constructed by default, leading to non-sequential payload locations
    /// (permutated by `interleave()` Arrow kernel). A single payload buffer might
    /// be shared by multiple `RecordBatch`es.
    /// When writing each batch to disk, the writer has to write all referenced buffers,
    /// because they have to be read back one by one to reduce memory usage. This
    /// causes extra disk reads and writes, and potentially execution failure.
    ///
    /// # Example
    /// Before sorting:
    /// batch1 -> buffer1
    /// batch2 -> buffer2
    ///
    /// sorted_batch1 -> buffer1
    ///               -> buffer2
    /// sorted_batch2 -> buffer1
    ///               -> buffer2
    ///
    /// Then when spilling each batch, the writer has to write all referenced buffers
    /// repeatedly.
    fn organize_stringview_arrays(&mut self) -> Result<()> {
        let mut organized_batches = Vec::with_capacity(self.in_mem_batches.len());

        for batch in self.in_mem_batches.drain(..) {
            let mut new_columns: Vec<Arc<dyn Array>> =
                Vec::with_capacity(batch.num_columns());

            let mut arr_mutated = false;
            for array in batch.columns() {
                if let Some(string_view_array) =
                    array.as_any().downcast_ref::<StringViewArray>()
                {
                    let new_array = string_view_array.gc();
                    new_columns.push(Arc::new(new_array));
                    arr_mutated = true;
                } else {
                    new_columns.push(Arc::clone(array));
                }
            }

            let organized_batch = if arr_mutated {
                RecordBatch::try_new(batch.schema(), new_columns)?
            } else {
                batch
            };

            organized_batches.push(organized_batch);
        }

        self.in_mem_batches = organized_batches;

        Ok(())
    }

    /// Sorts the in_mem_batches in place
    ///
    /// Sorting may have freed memory, especially if fetch is `Some`. If
    /// the memory usage has dropped by a factor of 2, then we don't have
    /// to spill. Otherwise, we spill to free up memory for inserting
    /// more batches.
    ///
    /// The factor of 2 aims to avoid a degenerate case where the
    /// memory required for `fetch` is just under the memory available,
    // causing repeated re-sorting of data
    async fn sort_or_spill_in_mem_batches(&mut self) -> Result<()> {
        // Release the memory reserved for merge back to the pool so
        // there is some left when `in_mem_sort_stream` requests an
        // allocation. At the end of this function, memory will be
        // reserved again for the next spill.
        self.merge_reservation.free();

        let before = self.reservation.size();

        let mut sorted_stream =
            self.in_mem_sort_stream(self.metrics.baseline.intermediate())?;

        // `self.in_mem_batches` is already taken away by the sort_stream, now it is empty.
        // We'll gradually collect the sorted stream into self.in_mem_batches, or directly
        // write sorted batches to disk when the memory is insufficient.
        while let Some(batch) = sorted_stream.next().await {
            let batch = batch?;
            let sorted_size = get_reserved_byte_for_record_batch(&batch);
            if self.reservation.try_grow(sorted_size).is_err() {
                // Although the reservation is not enough, the batch is
                // already in memory, so it's okay to combine it with previously
                // sorted batches, and spill together.
                self.in_mem_batches.push(batch);
                self.spill().await?; // reservation is freed in spill()
            } else {
                self.in_mem_batches.push(batch);
                self.in_mem_batches_sorted = true;
            }
        }

        // Drop early to free up memory reserved by the sorted stream, otherwise the
        // upcoming `self.reserve_memory_for_merge()` may fail due to insufficient memory.
        drop(sorted_stream);

        // Sorting may free up some memory especially when fetch is `Some`. If we have
        // not freed more than 50% of the memory, then we have to spill to free up more
        // memory for inserting more batches.
        if self.reservation.size() > before / 2 {
            // We have not freed more than 50% of the memory, so we have to spill to
            // free up more memory
            self.spill().await?;
        }

        // Reserve headroom for next sort/merge
        self.reserve_memory_for_merge()?;

        Ok(())
    }

    /// Consumes in_mem_batches returning a sorted stream of
    /// batches. This proceeds in one of two ways:
    ///
    /// # Small Datasets
    ///
    /// For "smaller" datasets, the data is first concatenated into a
    /// single batch and then sorted. This is often faster than
    /// sorting and then merging.
    ///
    /// ```text
    ///        ┌─────┐
    ///        │  2  │
    ///        │  3  │
    ///        │  1  │─ ─ ─ ─ ┐            ┌─────┐
    ///        │  4  │                     │  2  │
    ///        │  2  │        │            │  3  │
    ///        └─────┘                     │  1  │             sorted output
    ///        ┌─────┐        ▼            │  4  │                stream
    ///        │  1  │                     │  2  │
    ///        │  4  │─ ─▶ concat ─ ─ ─ ─ ▶│  1  │─ ─ ▶  sort  ─ ─ ─ ─ ─▶
    ///        │  1  │                     │  4  │
    ///        └─────┘        ▲            │  1  │
    ///          ...          │            │ ... │
    ///                                    │  4  │
    ///        ┌─────┐        │            │  3  │
    ///        │  4  │                     └─────┘
    ///        │  3  │─ ─ ─ ─ ┘
    ///        └─────┘
    ///     in_mem_batches
    /// ```
    ///
    /// # Larger datasets
    ///
    /// For larger datasets, the batches are first sorted individually
    /// and then merged together.
    ///
    /// ```text
    ///      ┌─────┐                ┌─────┐
    ///      │  2  │                │  1  │
    ///      │  3  │                │  2  │
    ///      │  1  │─ ─▶  sort  ─ ─▶│  2  │─ ─ ─ ─ ─ ┐
    ///      │  4  │                │  3  │
    ///      │  2  │                │  4  │          │
    ///      └─────┘                └─────┘               sorted output
    ///      ┌─────┐                ┌─────┐          ▼       stream
    ///      │  1  │                │  1  │
    ///      │  4  │─ ▶  sort  ─ ─ ▶│  1  ├ ─ ─ ▶ merge  ─ ─ ─ ─▶
    ///      │  1  │                │  4  │
    ///      └─────┘                └─────┘          ▲
    ///        ...       ...         ...             │
    ///
    ///      ┌─────┐                ┌─────┐          │
    ///      │  4  │                │  3  │
    ///      │  3  │─ ▶  sort  ─ ─ ▶│  4  │─ ─ ─ ─ ─ ┘
    ///      └─────┘                └─────┘
    ///
    ///   in_mem_batches
    /// ```
    fn in_mem_sort_stream(
        &mut self,
        metrics: BaselineMetrics,
    ) -> Result<SendableRecordBatchStream> {
        if self.in_mem_batches.is_empty() {
            return Ok(Box::pin(EmptyRecordBatchStream::new(Arc::clone(
                &self.schema,
            ))));
        }

        // The elapsed compute timer is updated when the value is dropped.
        // There is no need for an explicit call to drop.
        let elapsed_compute = metrics.elapsed_compute().clone();
        let _timer = elapsed_compute.timer();

        // Please pay attention that any operation inside of `in_mem_sort_stream` will
        // not perform any memory reservation. This is for avoiding the need of handling
        // reservation failure and spilling in the middle of the sort/merge. The memory
        // space for batches produced by the resulting stream will be reserved by the
        // consumer of the stream.

        if self.in_mem_batches.len() == 1 {
            let batch = self.in_mem_batches.swap_remove(0);
            let reservation = self.reservation.take();
            return self.sort_batch_stream(batch, metrics, reservation);
        }

        // If less than sort_in_place_threshold_bytes, concatenate and sort in place
        if self.reservation.size() < self.sort_in_place_threshold_bytes {
            // Concatenate memory batches together and sort
            let batch = concat_batches(&self.schema, &self.in_mem_batches)?;
            self.in_mem_batches.clear();
            self.reservation
                .try_resize(get_reserved_byte_for_record_batch(&batch))?;
            let reservation = self.reservation.take();
            return self.sort_batch_stream(batch, metrics, reservation);
        }

        let streams = std::mem::take(&mut self.in_mem_batches)
            .into_iter()
            .map(|batch| {
                let metrics = self.metrics.baseline.intermediate();
                let reservation = self
                    .reservation
                    .split(get_reserved_byte_for_record_batch(&batch));
                let input = self.sort_batch_stream(batch, metrics, reservation)?;
                Ok(spawn_buffered(input, 1))
            })
            .collect::<Result<_>>()?;

        let expressions: LexOrdering = self.expr.iter().cloned().collect();

        StreamingMergeBuilder::new()
            .with_streams(streams)
            .with_schema(Arc::clone(&self.schema))
            .with_expressions(expressions.as_ref())
            .with_metrics(metrics)
            .with_batch_size(self.batch_size)
            .with_fetch(self.fetch)
            .with_reservation(self.merge_reservation.new_empty())
            .build()
    }

    /// Sorts a single `RecordBatch` into a single stream.
    ///
    /// `reservation` accounts for the memory used by this batch and
    /// is released when the sort is complete
    fn sort_batch_stream(
        &self,
        batch: RecordBatch,
        metrics: BaselineMetrics,
        reservation: MemoryReservation,
    ) -> Result<SendableRecordBatchStream> {
        assert_eq!(
            get_reserved_byte_for_record_batch(&batch),
            reservation.size()
        );
        let schema = batch.schema();

        let fetch = self.fetch;
        let expressions: LexOrdering = self.expr.iter().cloned().collect();
        let stream = futures::stream::once(futures::future::lazy(move |_| {
            let timer = metrics.elapsed_compute().timer();
            let sorted = sort_batch(&batch, &expressions, fetch)?;
            timer.done();
            metrics.record_output(sorted.num_rows());
            drop(batch);
            drop(reservation);
            Ok(sorted)
        }));
        Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
    }

    /// If this sort may spill, pre-allocates
    /// `sort_spill_reservation_bytes` of memory to guarantee memory
    /// left for the in memory sort/merge.
    fn reserve_memory_for_merge(&mut self) -> Result<()> {
        // Reserve headroom for next merge sort
        if self.runtime.disk_manager.tmp_files_enabled() {
            let size = self.sort_spill_reservation_bytes;
            if self.merge_reservation.size() != size {
                self.merge_reservation.try_resize(size)?;
            }
        }

        Ok(())
    }
}

/// Estimate how much memory is needed to sort a `RecordBatch`.
///
/// This is used to pre-reserve memory for the sort/merge. The sort/merge process involves
/// creating sorted copies of sorted columns in record batches for speeding up comparison
/// in sorting and merging. The sorted copies are in either row format or array format.
/// Please refer to cursor.rs and stream.rs for more details. No matter what format the
/// sorted copies are, they will use more memory than the original record batch.
fn get_reserved_byte_for_record_batch(batch: &RecordBatch) -> usize {
    // 2x may not be enough for some cases, but it's a good start.
    // If 2x is not enough, user can set a larger value for `sort_spill_reservation_bytes`
    // to compensate for the extra memory needed.
    get_record_batch_memory_size(batch) * 2
}

impl Debug for ExternalSorter {
    fn fmt(&self, f: &mut Formatter) -> fmt::Result {
        f.debug_struct("ExternalSorter")
            .field("memory_used", &self.used())
            .field("spilled_bytes", &self.spilled_bytes())
            .field("spilled_rows", &self.spilled_rows())
            .field("spill_count", &self.spill_count())
            .finish()
    }
}

pub fn sort_batch(
    batch: &RecordBatch,
    expressions: &LexOrdering,
    fetch: Option<usize>,
) -> Result<RecordBatch> {
    let sort_columns = expressions
        .iter()
        .map(|expr| expr.evaluate_to_sort_column(batch))
        .collect::<Result<Vec<_>>>()?;

    let indices = if is_multi_column_with_lists(&sort_columns) {
        // lex_sort_to_indices doesn't support List with more than one column
        // https://github.com/apache/arrow-rs/issues/5454
        lexsort_to_indices_multi_columns(sort_columns, fetch)?
    } else {
        lexsort_to_indices(&sort_columns, fetch)?
    };

    let mut columns = take_arrays(batch.columns(), &indices, None)?;

    // The columns may be larger than the unsorted columns in `batch` especially for variable length
    // data types due to exponential growth when building the sort columns. We shrink the columns
    // to prevent memory reservation failures, as well as excessive memory allocation when running
    // merges in `SortPreservingMergeStream`.
    columns.iter_mut().for_each(|c| {
        c.shrink_to_fit();
    });

    let options = RecordBatchOptions::new().with_row_count(Some(indices.len()));
    Ok(RecordBatch::try_new_with_options(
        batch.schema(),
        columns,
        &options,
    )?)
}

#[inline]
fn is_multi_column_with_lists(sort_columns: &[SortColumn]) -> bool {
    sort_columns.iter().any(|c| {
        matches!(
            c.values.data_type(),
            DataType::List(_) | DataType::LargeList(_) | DataType::FixedSizeList(_, _)
        )
    })
}

pub(crate) fn lexsort_to_indices_multi_columns(
    sort_columns: Vec<SortColumn>,
    limit: Option<usize>,
) -> Result<UInt32Array> {
    let (fields, columns) = sort_columns.into_iter().fold(
        (vec![], vec![]),
        |(mut fields, mut columns), sort_column| {
            fields.push(SortField::new_with_options(
                sort_column.values.data_type().clone(),
                sort_column.options.unwrap_or_default(),
            ));
            columns.push(sort_column.values);
            (fields, columns)
        },
    );

    // TODO reuse converter and rows, refer to TopK.
    let converter = RowConverter::new(fields)?;
    let rows = converter.convert_columns(&columns)?;
    let mut sort: Vec<_> = rows.iter().enumerate().collect();
    sort.sort_unstable_by(|(_, a), (_, b)| a.cmp(b));

    let mut len = rows.num_rows();
    if let Some(limit) = limit {
        len = limit.min(len);
    }
    let indices =
        UInt32Array::from_iter_values(sort.iter().take(len).map(|(i, _)| *i as u32));

    Ok(indices)
}

/// Sort execution plan.
///
/// Support sorting datasets that are larger than the memory allotted
/// by the memory manager, by spilling to disk.
#[derive(Debug, Clone)]
pub struct SortExec {
    /// Input schema
    pub(crate) input: Arc<dyn ExecutionPlan>,
    /// Sort expressions
    expr: LexOrdering,
    /// Containing all metrics set created during sort
    metrics_set: ExecutionPlanMetricsSet,
    /// Preserve partitions of input plan. If false, the input partitions
    /// will be sorted and merged into a single output partition.
    preserve_partitioning: bool,
    /// Fetch highest/lowest n results
    fetch: Option<usize>,
    /// Cache holding plan properties like equivalences, output partitioning etc.
    cache: PlanProperties,
}

impl SortExec {
    /// Create a new sort execution plan that produces a single,
    /// sorted output partition.
    pub fn new(expr: LexOrdering, input: Arc<dyn ExecutionPlan>) -> Self {
        let preserve_partitioning = false;
        let cache = Self::compute_properties(&input, expr.clone(), preserve_partitioning);
        Self {
            expr,
            input,
            metrics_set: ExecutionPlanMetricsSet::new(),
            preserve_partitioning,
            fetch: None,
            cache,
        }
    }

    /// Whether this `SortExec` preserves partitioning of the children
    pub fn preserve_partitioning(&self) -> bool {
        self.preserve_partitioning
    }

    /// Specify the partitioning behavior of this sort exec
    ///
    /// If `preserve_partitioning` is true, sorts each partition
    /// individually, producing one sorted stream for each input partition.
    ///
    /// If `preserve_partitioning` is false, sorts and merges all
    /// input partitions producing a single, sorted partition.
    pub fn with_preserve_partitioning(mut self, preserve_partitioning: bool) -> Self {
        self.preserve_partitioning = preserve_partitioning;
        self.cache = self
            .cache
            .with_partitioning(Self::output_partitioning_helper(
                &self.input,
                self.preserve_partitioning,
            ));
        self
    }

    /// Modify how many rows to include in the result
    ///
    /// If None, then all rows will be returned, in sorted order.
    /// If Some, then only the top `fetch` rows will be returned.
    /// This can reduce the memory pressure required by the sort
    /// operation since rows that are not going to be included
    /// can be dropped.
    pub fn with_fetch(&self, fetch: Option<usize>) -> Self {
        let mut cache = self.cache.clone();
        // If the SortExec can emit incrementally (that means the sort requirements
        // and properties of the input match), the SortExec can generate its result
        // without scanning the entire input when a fetch value exists.
        let is_pipeline_friendly = matches!(
            self.cache.emission_type,
            EmissionType::Incremental | EmissionType::Both
        );
        if fetch.is_some() && is_pipeline_friendly {
            cache = cache.with_boundedness(Boundedness::Bounded);
        }
        SortExec {
            input: Arc::clone(&self.input),
            expr: self.expr.clone(),
            metrics_set: self.metrics_set.clone(),
            preserve_partitioning: self.preserve_partitioning,
            fetch,
            cache,
        }
    }

    /// Input schema
    pub fn input(&self) -> &Arc<dyn ExecutionPlan> {
        &self.input
    }

    /// Sort expressions
    pub fn expr(&self) -> &LexOrdering {
        &self.expr
    }

    /// If `Some(fetch)`, limits output to only the first "fetch" items
    pub fn fetch(&self) -> Option<usize> {
        self.fetch
    }

    fn output_partitioning_helper(
        input: &Arc<dyn ExecutionPlan>,
        preserve_partitioning: bool,
    ) -> Partitioning {
        // Get output partitioning:
        if preserve_partitioning {
            input.output_partitioning().clone()
        } else {
            Partitioning::UnknownPartitioning(1)
        }
    }

    /// This function creates the cache object that stores the plan properties such as schema, equivalence properties, ordering, partitioning, etc.
    fn compute_properties(
        input: &Arc<dyn ExecutionPlan>,
        sort_exprs: LexOrdering,
        preserve_partitioning: bool,
    ) -> PlanProperties {
        // Determine execution mode:
        let requirement = LexRequirement::from(sort_exprs);
        let sort_satisfied = input
            .equivalence_properties()
            .ordering_satisfy_requirement(&requirement);

        // The emission type depends on whether the input is already sorted:
        // - If already sorted, we can emit results in the same way as the input
        // - If not sorted, we must wait until all data is processed to emit results (Final)
        let emission_type = if sort_satisfied {
            input.pipeline_behavior()
        } else {
            EmissionType::Final
        };

        // The boundedness depends on whether the input is already sorted:
        // - If already sorted, we have the same property as the input
        // - If not sorted and input is unbounded, we require infinite memory and generates
        //   unbounded data (not practical).
        // - If not sorted and input is bounded, then the SortExec is bounded, too.
        let boundedness = if sort_satisfied {
            input.boundedness()
        } else {
            match input.boundedness() {
                Boundedness::Unbounded { .. } => Boundedness::Unbounded {
                    requires_infinite_memory: true,
                },
                bounded => bounded,
            }
        };

        // Calculate equivalence properties; i.e. reset the ordering equivalence
        // class with the new ordering:
        let sort_exprs = LexOrdering::from(requirement);
        let eq_properties = input
            .equivalence_properties()
            .clone()
            .with_reorder(sort_exprs);

        // Get output partitioning:
        let output_partitioning =
            Self::output_partitioning_helper(input, preserve_partitioning);

        PlanProperties::new(
            eq_properties,
            output_partitioning,
            emission_type,
            boundedness,
        )
    }
}

impl DisplayAs for SortExec {
    fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> fmt::Result {
        match t {
            DisplayFormatType::Default | DisplayFormatType::Verbose => {
                let preserve_partitioning = self.preserve_partitioning;
                match self.fetch {
                    Some(fetch) => {
                        write!(f, "SortExec: TopK(fetch={fetch}), expr=[{}], preserve_partitioning=[{preserve_partitioning}]", self.expr)
                    }
                    None => write!(f, "SortExec: expr=[{}], preserve_partitioning=[{preserve_partitioning}]", self.expr),
                }
            }
        }
    }
}

impl ExecutionPlan for SortExec {
    fn name(&self) -> &'static str {
        "SortExec"
    }

    fn as_any(&self) -> &dyn Any {
        self
    }

    fn properties(&self) -> &PlanProperties {
        &self.cache
    }

    fn required_input_distribution(&self) -> Vec<Distribution> {
        if self.preserve_partitioning {
            vec![Distribution::UnspecifiedDistribution]
        } else {
            // global sort
            // TODO support RangePartition and OrderedDistribution
            vec![Distribution::SinglePartition]
        }
    }

    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
        vec![&self.input]
    }

    fn benefits_from_input_partitioning(&self) -> Vec<bool> {
        vec![false]
    }

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        let new_sort = SortExec::new(self.expr.clone(), Arc::clone(&children[0]))
            .with_fetch(self.fetch)
            .with_preserve_partitioning(self.preserve_partitioning);

        Ok(Arc::new(new_sort))
    }

    fn execute(
        &self,
        partition: usize,
        context: Arc<TaskContext>,
    ) -> Result<SendableRecordBatchStream> {
        trace!("Start SortExec::execute for partition {} of context session_id {} and task_id {:?}", partition, context.session_id(), context.task_id());

        let mut input = self.input.execute(partition, Arc::clone(&context))?;

        let execution_options = &context.session_config().options().execution;

        trace!("End SortExec's input.execute for partition: {}", partition);

        let sort_satisfied = self
            .input
            .equivalence_properties()
            .ordering_satisfy_requirement(&LexRequirement::from(self.expr.clone()));

        match (sort_satisfied, self.fetch.as_ref()) {
            (true, Some(fetch)) => Ok(Box::pin(LimitStream::new(
                input,
                0,
                Some(*fetch),
                BaselineMetrics::new(&self.metrics_set, partition),
            ))),
            (true, None) => Ok(input),
            (false, Some(fetch)) => {
                let mut topk = TopK::try_new(
                    partition,
                    input.schema(),
                    self.expr.clone(),
                    *fetch,
                    context.session_config().batch_size(),
                    context.runtime_env(),
                    &self.metrics_set,
                )?;
                Ok(Box::pin(RecordBatchStreamAdapter::new(
                    self.schema(),
                    futures::stream::once(async move {
                        while let Some(batch) = input.next().await {
                            let batch = batch?;
                            topk.insert_batch(batch)?;
                        }
                        topk.emit()
                    })
                    .try_flatten(),
                )))
            }
            (false, None) => {
                let mut sorter = ExternalSorter::new(
                    partition,
                    input.schema(),
                    self.expr.clone(),
                    context.session_config().batch_size(),
                    self.fetch,
                    execution_options.sort_spill_reservation_bytes,
                    execution_options.sort_in_place_threshold_bytes,
                    &self.metrics_set,
                    context.runtime_env(),
                );
                Ok(Box::pin(RecordBatchStreamAdapter::new(
                    self.schema(),
                    futures::stream::once(async move {
                        while let Some(batch) = input.next().await {
                            let batch = batch?;
                            sorter.insert_batch(batch).await?;
                        }
                        sorter.sort()
                    })
                    .try_flatten(),
                )))
            }
        }
    }

    fn metrics(&self) -> Option<MetricsSet> {
        Some(self.metrics_set.clone_inner())
    }

    fn statistics(&self) -> Result<Statistics> {
        Statistics::with_fetch(self.input.statistics()?, self.schema(), self.fetch, 0, 1)
    }

    fn with_fetch(&self, limit: Option<usize>) -> Option<Arc<dyn ExecutionPlan>> {
        Some(Arc::new(SortExec::with_fetch(self, limit)))
    }

    fn fetch(&self) -> Option<usize> {
        self.fetch
    }

    fn cardinality_effect(&self) -> CardinalityEffect {
        if self.fetch.is_none() {
            CardinalityEffect::Equal
        } else {
            CardinalityEffect::LowerEqual
        }
    }

    /// Tries to swap the projection with its input [`SortExec`]. If it can be done,
    /// it returns the new swapped version having the [`SortExec`] as the top plan.
    /// Otherwise, it returns None.
    fn try_swapping_with_projection(
        &self,
        projection: &ProjectionExec,
    ) -> Result<Option<Arc<dyn ExecutionPlan>>> {
        // If the projection does not narrow the schema, we should not try to push it down.
        if projection.expr().len() >= projection.input().schema().fields().len() {
            return Ok(None);
        }

        let mut updated_exprs = LexOrdering::default();
        for sort in self.expr() {
            let Some(new_expr) = update_expr(&sort.expr, projection.expr(), false)?
            else {
                return Ok(None);
            };
            updated_exprs.push(PhysicalSortExpr {
                expr: new_expr,
                options: sort.options,
            });
        }

        Ok(Some(Arc::new(
            SortExec::new(updated_exprs, make_with_child(projection, self.input())?)
                .with_fetch(self.fetch())
                .with_preserve_partitioning(self.preserve_partitioning()),
        )))
    }
}

#[cfg(test)]
mod tests {
    use std::collections::HashMap;
    use std::pin::Pin;
    use std::task::{Context, Poll};

    use super::*;
    use crate::coalesce_partitions::CoalescePartitionsExec;
    use crate::collect;
    use crate::execution_plan::Boundedness;
    use crate::expressions::col;
    use crate::test;
    use crate::test::assert_is_pending;
    use crate::test::exec::{assert_strong_count_converges_to_zero, BlockingExec};
    use crate::test::TestMemoryExec;

    use arrow::array::*;
    use arrow::compute::SortOptions;
    use arrow::datatypes::*;
    use datafusion_common::cast::as_primitive_array;
    use datafusion_common::{assert_batches_eq, Result, ScalarValue};
    use datafusion_execution::config::SessionConfig;
    use datafusion_execution::runtime_env::RuntimeEnvBuilder;
    use datafusion_execution::RecordBatchStream;
    use datafusion_physical_expr::expressions::{Column, Literal};
    use datafusion_physical_expr::EquivalenceProperties;

    use futures::{FutureExt, Stream};

    #[derive(Debug, Clone)]
    pub struct SortedUnboundedExec {
        schema: Schema,
        batch_size: u64,
        cache: PlanProperties,
    }

    impl DisplayAs for SortedUnboundedExec {
        fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> fmt::Result {
            match t {
                DisplayFormatType::Default | DisplayFormatType::Verbose => {
                    write!(f, "UnboundableExec",).unwrap()
                }
            }
            Ok(())
        }
    }

    impl SortedUnboundedExec {
        fn compute_properties(schema: SchemaRef) -> PlanProperties {
            let mut eq_properties = EquivalenceProperties::new(schema);
            eq_properties.add_new_orderings(vec![LexOrdering::new(vec![
                PhysicalSortExpr::new_default(Arc::new(Column::new("c1", 0))),
            ])]);
            PlanProperties::new(
                eq_properties,
                Partitioning::UnknownPartitioning(1),
                EmissionType::Final,
                Boundedness::Unbounded {
                    requires_infinite_memory: false,
                },
            )
        }
    }

    impl ExecutionPlan for SortedUnboundedExec {
        fn name(&self) -> &'static str {
            Self::static_name()
        }

        fn as_any(&self) -> &dyn Any {
            self
        }

        fn properties(&self) -> &PlanProperties {
            &self.cache
        }

        fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
            vec![]
        }

        fn with_new_children(
            self: Arc<Self>,
            _: Vec<Arc<dyn ExecutionPlan>>,
        ) -> Result<Arc<dyn ExecutionPlan>> {
            Ok(self)
        }

        fn execute(
            &self,
            _partition: usize,
            _context: Arc<TaskContext>,
        ) -> Result<SendableRecordBatchStream> {
            Ok(Box::pin(SortedUnboundedStream {
                schema: Arc::new(self.schema.clone()),
                batch_size: self.batch_size,
                offset: 0,
            }))
        }
    }

    #[derive(Debug)]
    pub struct SortedUnboundedStream {
        schema: SchemaRef,
        batch_size: u64,
        offset: u64,
    }

    impl Stream for SortedUnboundedStream {
        type Item = Result<RecordBatch>;

        fn poll_next(
            mut self: Pin<&mut Self>,
            _cx: &mut Context<'_>,
        ) -> Poll<Option<Self::Item>> {
            let batch = SortedUnboundedStream::create_record_batch(
                Arc::clone(&self.schema),
                self.offset,
                self.batch_size,
            );
            self.offset += self.batch_size;
            Poll::Ready(Some(Ok(batch)))
        }
    }

    impl RecordBatchStream for SortedUnboundedStream {
        fn schema(&self) -> SchemaRef {
            Arc::clone(&self.schema)
        }
    }

    impl SortedUnboundedStream {
        fn create_record_batch(
            schema: SchemaRef,
            offset: u64,
            batch_size: u64,
        ) -> RecordBatch {
            let values = (0..batch_size).map(|i| offset + i).collect::<Vec<_>>();
            let array = UInt64Array::from(values);
            let array_ref: ArrayRef = Arc::new(array);
            RecordBatch::try_new(schema, vec![array_ref]).unwrap()
        }
    }

    #[tokio::test]
    async fn test_in_mem_sort() -> Result<()> {
        let task_ctx = Arc::new(TaskContext::default());
        let partitions = 4;
        let csv = test::scan_partitioned(partitions);
        let schema = csv.schema();

        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![PhysicalSortExpr {
                expr: col("i", &schema)?,
                options: SortOptions::default(),
            }]),
            Arc::new(CoalescePartitionsExec::new(csv)),
        ));

        let result = collect(sort_exec, Arc::clone(&task_ctx)).await?;

        assert_eq!(result.len(), 1);
        assert_eq!(result[0].num_rows(), 400);

        assert_eq!(
            task_ctx.runtime_env().memory_pool.reserved(),
            0,
            "The sort should have returned all memory used back to the memory manager"
        );

        Ok(())
    }

    #[tokio::test]
    async fn test_sort_spill() -> Result<()> {
        // trigger spill w/ 100 batches
        let session_config = SessionConfig::new();
        let sort_spill_reservation_bytes = session_config
            .options()
            .execution
            .sort_spill_reservation_bytes;
        let runtime = RuntimeEnvBuilder::new()
            .with_memory_limit(sort_spill_reservation_bytes + 12288, 1.0)
            .build_arc()?;
        let task_ctx = Arc::new(
            TaskContext::default()
                .with_session_config(session_config)
                .with_runtime(runtime),
        );

        // The input has 100 partitions, each partition has a batch containing 100 rows.
        // Each row has a single Int32 column with values 0..100. The total size of the
        // input is roughly 40000 bytes.
        let partitions = 100;
        let input = test::scan_partitioned(partitions);
        let schema = input.schema();

        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![PhysicalSortExpr {
                expr: col("i", &schema)?,
                options: SortOptions::default(),
            }]),
            Arc::new(CoalescePartitionsExec::new(input)),
        ));

        let result = collect(
            Arc::clone(&sort_exec) as Arc<dyn ExecutionPlan>,
            Arc::clone(&task_ctx),
        )
        .await?;

        assert_eq!(result.len(), 2);

        // Now, validate metrics
        let metrics = sort_exec.metrics().unwrap();

        assert_eq!(metrics.output_rows().unwrap(), 10000);
        assert!(metrics.elapsed_compute().unwrap() > 0);

        let spill_count = metrics.spill_count().unwrap();
        let spilled_rows = metrics.spilled_rows().unwrap();
        let spilled_bytes = metrics.spilled_bytes().unwrap();
        // Processing 40000 bytes of data using 12288 bytes of memory requires 3 spills
        // unless we do something really clever. It will spill roughly 9000+ rows and 36000
        // bytes. We leave a little wiggle room for the actual numbers.
        assert!((3..=10).contains(&spill_count));
        assert!((9000..=10000).contains(&spilled_rows));
        assert!((36000..=40000).contains(&spilled_bytes));

        let columns = result[0].columns();

        let i = as_primitive_array::<Int32Type>(&columns[0])?;
        assert_eq!(i.value(0), 0);
        assert_eq!(i.value(i.len() - 1), 81);

        assert_eq!(
            task_ctx.runtime_env().memory_pool.reserved(),
            0,
            "The sort should have returned all memory used back to the memory manager"
        );

        Ok(())
    }

    #[tokio::test]
    async fn test_sort_spill_utf8_strings() -> Result<()> {
        let session_config = SessionConfig::new()
            .with_batch_size(100)
            .with_sort_in_place_threshold_bytes(20 * 1024)
            .with_sort_spill_reservation_bytes(100 * 1024);
        let runtime = RuntimeEnvBuilder::new()
            .with_memory_limit(500 * 1024, 1.0)
            .build_arc()?;
        let task_ctx = Arc::new(
            TaskContext::default()
                .with_session_config(session_config)
                .with_runtime(runtime),
        );

        // The input has 200 partitions, each partition has a batch containing 100 rows.
        // Each row has a single Utf8 column, the Utf8 string values are roughly 42 bytes.
        // The total size of the input is roughly 8.4 KB.
        let input = test::scan_partitioned_utf8(200);
        let schema = input.schema();

        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![PhysicalSortExpr {
                expr: col("i", &schema)?,
                options: SortOptions::default(),
            }]),
            Arc::new(CoalescePartitionsExec::new(input)),
        ));

        let result = collect(
            Arc::clone(&sort_exec) as Arc<dyn ExecutionPlan>,
            Arc::clone(&task_ctx),
        )
        .await?;

        let num_rows = result.iter().map(|batch| batch.num_rows()).sum::<usize>();
        assert_eq!(num_rows, 20000);

        // Now, validate metrics
        let metrics = sort_exec.metrics().unwrap();

        assert_eq!(metrics.output_rows().unwrap(), 20000);
        assert!(metrics.elapsed_compute().unwrap() > 0);

        let spill_count = metrics.spill_count().unwrap();
        let spilled_rows = metrics.spilled_rows().unwrap();
        let spilled_bytes = metrics.spilled_bytes().unwrap();

        // This test case is processing 840KB of data using 400KB of memory. Note
        // that buffered batches can't be dropped until all sorted batches are
        // generated, so we can only buffer `sort_spill_reservation_bytes` of sorted
        // batches.
        // The number of spills is roughly calculated as:
        //  `number_of_batches / (sort_spill_reservation_bytes / batch_size)`
        assert!((12..=18).contains(&spill_count));
        assert!((15000..=20000).contains(&spilled_rows));
        assert!((700000..=900000).contains(&spilled_bytes));

        // Verify that the result is sorted
        let concated_result = concat_batches(&schema, &result)?;
        let columns = concated_result.columns();
        let string_array = as_string_array(&columns[0]);
        for i in 0..string_array.len() - 1 {
            assert!(string_array.value(i) <= string_array.value(i + 1));
        }

        assert_eq!(
            task_ctx.runtime_env().memory_pool.reserved(),
            0,
            "The sort should have returned all memory used back to the memory manager"
        );

        Ok(())
    }

    #[tokio::test]
    async fn test_sort_fetch_memory_calculation() -> Result<()> {
        // This test mirrors down the size from the example above.
        let avg_batch_size = 400;
        let partitions = 4;

        // A tuple of (fetch, expect_spillage)
        let test_options = vec![
            // Since we don't have a limit (and the memory is less than the total size of
            // all the batches we are processing, we expect it to spill.
            (None, true),
            // When we have a limit however, the buffered size of batches should fit in memory
            // since it is much lower than the total size of the input batch.
            (Some(1), false),
        ];

        for (fetch, expect_spillage) in test_options {
            let session_config = SessionConfig::new();
            let sort_spill_reservation_bytes = session_config
                .options()
                .execution
                .sort_spill_reservation_bytes;

            let runtime = RuntimeEnvBuilder::new()
                .with_memory_limit(
                    sort_spill_reservation_bytes + avg_batch_size * (partitions - 1),
                    1.0,
                )
                .build_arc()?;
            let task_ctx = Arc::new(
                TaskContext::default()
                    .with_runtime(runtime)
                    .with_session_config(session_config),
            );

            let csv = test::scan_partitioned(partitions);
            let schema = csv.schema();

            let sort_exec = Arc::new(
                SortExec::new(
                    LexOrdering::new(vec![PhysicalSortExpr {
                        expr: col("i", &schema)?,
                        options: SortOptions::default(),
                    }]),
                    Arc::new(CoalescePartitionsExec::new(csv)),
                )
                .with_fetch(fetch),
            );

            let result = collect(
                Arc::clone(&sort_exec) as Arc<dyn ExecutionPlan>,
                Arc::clone(&task_ctx),
            )
            .await?;
            assert_eq!(result.len(), 1);

            let metrics = sort_exec.metrics().unwrap();
            let did_it_spill = metrics.spill_count().unwrap_or(0) > 0;
            assert_eq!(did_it_spill, expect_spillage, "with fetch: {fetch:?}");
        }
        Ok(())
    }

    #[tokio::test]
    async fn test_sort_metadata() -> Result<()> {
        let task_ctx = Arc::new(TaskContext::default());
        let field_metadata: HashMap<String, String> =
            vec![("foo".to_string(), "bar".to_string())]
                .into_iter()
                .collect();
        let schema_metadata: HashMap<String, String> =
            vec![("baz".to_string(), "barf".to_string())]
                .into_iter()
                .collect();

        let mut field = Field::new("field_name", DataType::UInt64, true);
        field.set_metadata(field_metadata.clone());
        let schema = Schema::new_with_metadata(vec![field], schema_metadata.clone());
        let schema = Arc::new(schema);

        let data: ArrayRef =
            Arc::new(vec![3, 2, 1].into_iter().map(Some).collect::<UInt64Array>());

        let batch = RecordBatch::try_new(Arc::clone(&schema), vec![data]).unwrap();
        let input =
            TestMemoryExec::try_new_exec(&[vec![batch]], Arc::clone(&schema), None)
                .unwrap();

        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![PhysicalSortExpr {
                expr: col("field_name", &schema)?,
                options: SortOptions::default(),
            }]),
            input,
        ));

        let result: Vec<RecordBatch> = collect(sort_exec, task_ctx).await?;

        let expected_data: ArrayRef =
            Arc::new(vec![1, 2, 3].into_iter().map(Some).collect::<UInt64Array>());
        let expected_batch =
            RecordBatch::try_new(Arc::clone(&schema), vec![expected_data]).unwrap();

        // Data is correct
        assert_eq!(&vec![expected_batch], &result);

        // explicitly ensure the metadata is present
        assert_eq!(result[0].schema().fields()[0].metadata(), &field_metadata);
        assert_eq!(result[0].schema().metadata(), &schema_metadata);

        Ok(())
    }

    #[tokio::test]
    async fn test_lex_sort_by_mixed_types() -> Result<()> {
        let task_ctx = Arc::new(TaskContext::default());
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, true),
            Field::new(
                "b",
                DataType::List(Arc::new(Field::new_list_field(DataType::Int32, true))),
                true,
            ),
        ]));

        // define data.
        let batch = RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(vec![Some(2), None, Some(1), Some(2)])),
                Arc::new(ListArray::from_iter_primitive::<Int32Type, _, _>(vec![
                    Some(vec![Some(3)]),
                    Some(vec![Some(1)]),
                    Some(vec![Some(6), None]),
                    Some(vec![Some(5)]),
                ])),
            ],
        )?;

        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![
                PhysicalSortExpr {
                    expr: col("a", &schema)?,
                    options: SortOptions {
                        descending: false,
                        nulls_first: true,
                    },
                },
                PhysicalSortExpr {
                    expr: col("b", &schema)?,
                    options: SortOptions {
                        descending: true,
                        nulls_first: false,
                    },
                },
            ]),
            TestMemoryExec::try_new_exec(&[vec![batch]], Arc::clone(&schema), None)?,
        ));

        assert_eq!(DataType::Int32, *sort_exec.schema().field(0).data_type());
        assert_eq!(
            DataType::List(Arc::new(Field::new_list_field(DataType::Int32, true))),
            *sort_exec.schema().field(1).data_type()
        );

        let result: Vec<RecordBatch> =
            collect(Arc::clone(&sort_exec) as Arc<dyn ExecutionPlan>, task_ctx).await?;
        let metrics = sort_exec.metrics().unwrap();
        assert!(metrics.elapsed_compute().unwrap() > 0);
        assert_eq!(metrics.output_rows().unwrap(), 4);
        assert_eq!(result.len(), 1);

        let expected = RecordBatch::try_new(
            schema,
            vec![
                Arc::new(Int32Array::from(vec![None, Some(1), Some(2), Some(2)])),
                Arc::new(ListArray::from_iter_primitive::<Int32Type, _, _>(vec![
                    Some(vec![Some(1)]),
                    Some(vec![Some(6), None]),
                    Some(vec![Some(5)]),
                    Some(vec![Some(3)]),
                ])),
            ],
        )?;

        assert_eq!(expected, result[0]);

        Ok(())
    }

    #[tokio::test]
    async fn test_lex_sort_by_float() -> Result<()> {
        let task_ctx = Arc::new(TaskContext::default());
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Float32, true),
            Field::new("b", DataType::Float64, true),
        ]));

        // define data.
        let batch = RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Float32Array::from(vec![
                    Some(f32::NAN),
                    None,
                    None,
                    Some(f32::NAN),
                    Some(1.0_f32),
                    Some(1.0_f32),
                    Some(2.0_f32),
                    Some(3.0_f32),
                ])),
                Arc::new(Float64Array::from(vec![
                    Some(200.0_f64),
                    Some(20.0_f64),
                    Some(10.0_f64),
                    Some(100.0_f64),
                    Some(f64::NAN),
                    None,
                    None,
                    Some(f64::NAN),
                ])),
            ],
        )?;

        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![
                PhysicalSortExpr {
                    expr: col("a", &schema)?,
                    options: SortOptions {
                        descending: true,
                        nulls_first: true,
                    },
                },
                PhysicalSortExpr {
                    expr: col("b", &schema)?,
                    options: SortOptions {
                        descending: false,
                        nulls_first: false,
                    },
                },
            ]),
            TestMemoryExec::try_new_exec(&[vec![batch]], schema, None)?,
        ));

        assert_eq!(DataType::Float32, *sort_exec.schema().field(0).data_type());
        assert_eq!(DataType::Float64, *sort_exec.schema().field(1).data_type());

        let result: Vec<RecordBatch> =
            collect(Arc::clone(&sort_exec) as Arc<dyn ExecutionPlan>, task_ctx).await?;
        let metrics = sort_exec.metrics().unwrap();
        assert!(metrics.elapsed_compute().unwrap() > 0);
        assert_eq!(metrics.output_rows().unwrap(), 8);
        assert_eq!(result.len(), 1);

        let columns = result[0].columns();

        assert_eq!(DataType::Float32, *columns[0].data_type());
        assert_eq!(DataType::Float64, *columns[1].data_type());

        let a = as_primitive_array::<Float32Type>(&columns[0])?;
        let b = as_primitive_array::<Float64Type>(&columns[1])?;

        // convert result to strings to allow comparing to expected result containing NaN
        let result: Vec<(Option<String>, Option<String>)> = (0..result[0].num_rows())
            .map(|i| {
                let aval = if a.is_valid(i) {
                    Some(a.value(i).to_string())
                } else {
                    None
                };
                let bval = if b.is_valid(i) {
                    Some(b.value(i).to_string())
                } else {
                    None
                };
                (aval, bval)
            })
            .collect();

        let expected: Vec<(Option<String>, Option<String>)> = vec![
            (None, Some("10".to_owned())),
            (None, Some("20".to_owned())),
            (Some("NaN".to_owned()), Some("100".to_owned())),
            (Some("NaN".to_owned()), Some("200".to_owned())),
            (Some("3".to_owned()), Some("NaN".to_owned())),
            (Some("2".to_owned()), None),
            (Some("1".to_owned()), Some("NaN".to_owned())),
            (Some("1".to_owned()), None),
        ];

        assert_eq!(expected, result);

        Ok(())
    }

    #[tokio::test]
    async fn test_drop_cancel() -> Result<()> {
        let task_ctx = Arc::new(TaskContext::default());
        let schema =
            Arc::new(Schema::new(vec![Field::new("a", DataType::Float32, true)]));

        let blocking_exec = Arc::new(BlockingExec::new(Arc::clone(&schema), 1));
        let refs = blocking_exec.refs();
        let sort_exec = Arc::new(SortExec::new(
            LexOrdering::new(vec![PhysicalSortExpr {
                expr: col("a", &schema)?,
                options: SortOptions::default(),
            }]),
            blocking_exec,
        ));

        let fut = collect(sort_exec, Arc::clone(&task_ctx));
        let mut fut = fut.boxed();

        assert_is_pending(&mut fut);
        drop(fut);
        assert_strong_count_converges_to_zero(refs).await;

        assert_eq!(
            task_ctx.runtime_env().memory_pool.reserved(),
            0,
            "The sort should have returned all memory used back to the memory manager"
        );

        Ok(())
    }

    #[test]
    fn test_empty_sort_batch() {
        let schema = Arc::new(Schema::empty());
        let options = RecordBatchOptions::new().with_row_count(Some(1));
        let batch =
            RecordBatch::try_new_with_options(Arc::clone(&schema), vec![], &options)
                .unwrap();

        let expressions = LexOrdering::new(vec![PhysicalSortExpr {
            expr: Arc::new(Literal::new(ScalarValue::Int64(Some(1)))),
            options: SortOptions::default(),
        }]);

        let result = sort_batch(&batch, expressions.as_ref(), None).unwrap();
        assert_eq!(result.num_rows(), 1);
    }

    #[tokio::test]
    async fn topk_unbounded_source() -> Result<()> {
        let task_ctx = Arc::new(TaskContext::default());
        let schema = Schema::new(vec![Field::new("c1", DataType::UInt64, false)]);
        let source = SortedUnboundedExec {
            schema: schema.clone(),
            batch_size: 2,
            cache: SortedUnboundedExec::compute_properties(Arc::new(schema.clone())),
        };
        let mut plan = SortExec::new(
            LexOrdering::new(vec![PhysicalSortExpr::new_default(Arc::new(Column::new(
                "c1", 0,
            )))]),
            Arc::new(source),
        );
        plan = plan.with_fetch(Some(9));

        let batches = collect(Arc::new(plan), task_ctx).await?;
        #[rustfmt::skip]
        let expected = [
            "+----+",
            "| c1 |",
            "+----+",
            "| 0  |",
            "| 1  |",
            "| 2  |",
            "| 3  |",
            "| 4  |",
            "| 5  |",
            "| 6  |",
            "| 7  |",
            "| 8  |",
            "+----+",];
        assert_batches_eq!(expected, &batches);
        Ok(())
    }
}