datafusion_physical_plan/joins/

sort_merge_join.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.

//! Defines the Sort-Merge join execution plan.
//! A Sort-Merge join plan consumes two sorted children plan and produces
//! joined output by given join type and other options.
//! Sort-Merge join feature is currently experimental.

use std::any::Any;
use std::cmp::Ordering;
use std::collections::{HashMap, VecDeque};
use std::fmt::Formatter;
use std::fs::File;
use std::io::BufReader;
use std::mem::size_of;
use std::ops::Range;
use std::pin::Pin;
use std::sync::atomic::AtomicUsize;
use std::sync::atomic::Ordering::Relaxed;
use std::sync::Arc;
use std::task::{Context, Poll};

use arrow::array::*;
use arrow::compute::{
    self, concat_batches, filter_record_batch, is_not_null, take, SortOptions,
};
use arrow::datatypes::{DataType, SchemaRef, TimeUnit};
use arrow::error::ArrowError;
use arrow::ipc::reader::FileReader;
use arrow_array::types::UInt64Type;
use datafusion_common::{
    exec_err, internal_err, not_impl_err, plan_err, DataFusionError, JoinSide, JoinType,
    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::equivalence::join_equivalence_properties;
use datafusion_physical_expr::{PhysicalExprRef, PhysicalSortRequirement};
use datafusion_physical_expr_common::sort_expr::{LexOrdering, LexRequirement};
use futures::{Stream, StreamExt};
use hashbrown::HashSet;

use crate::expressions::PhysicalSortExpr;
use crate::joins::utils::{
    build_join_schema, check_join_is_valid, estimate_join_statistics,
    symmetric_join_output_partitioning, JoinFilter, JoinOn, JoinOnRef,
};
use crate::metrics::{Count, ExecutionPlanMetricsSet, MetricBuilder, MetricsSet};
use crate::spill::spill_record_batches;
use crate::{
    execution_mode_from_children, metrics, DisplayAs, DisplayFormatType, Distribution,
    ExecutionPlan, ExecutionPlanProperties, PhysicalExpr, PlanProperties,
    RecordBatchStream, SendableRecordBatchStream, Statistics,
};

/// join execution plan executes partitions in parallel and combines them into a set of
/// partitions.
#[derive(Debug, Clone)]
pub struct SortMergeJoinExec {
    /// Left sorted joining execution plan
    pub left: Arc<dyn ExecutionPlan>,
    /// Right sorting joining execution plan
    pub right: Arc<dyn ExecutionPlan>,
    /// Set of common columns used to join on
    pub on: JoinOn,
    /// Filters which are applied while finding matching rows
    pub filter: Option<JoinFilter>,
    /// How the join is performed
    pub join_type: JoinType,
    /// The schema once the join is applied
    schema: SchemaRef,
    /// Execution metrics
    metrics: ExecutionPlanMetricsSet,
    /// The left SortExpr
    left_sort_exprs: LexOrdering,
    /// The right SortExpr
    right_sort_exprs: LexOrdering,
    /// Sort options of join columns used in sorting left and right execution plans
    pub sort_options: Vec<SortOptions>,
    /// If null_equals_null is true, null == null else null != null
    pub null_equals_null: bool,
    /// Cache holding plan properties like equivalences, output partitioning etc.
    cache: PlanProperties,
}

impl SortMergeJoinExec {
    /// Tries to create a new [SortMergeJoinExec].
    /// The inputs are sorted using `sort_options` are applied to the columns in the `on`
    /// # Error
    /// This function errors when it is not possible to join the left and right sides on keys `on`.
    pub fn try_new(
        left: Arc<dyn ExecutionPlan>,
        right: Arc<dyn ExecutionPlan>,
        on: JoinOn,
        filter: Option<JoinFilter>,
        join_type: JoinType,
        sort_options: Vec<SortOptions>,
        null_equals_null: bool,
    ) -> Result<Self> {
        let left_schema = left.schema();
        let right_schema = right.schema();

        if join_type == JoinType::RightSemi {
            return not_impl_err!(
                "SortMergeJoinExec does not support JoinType::RightSemi"
            );
        }

        check_join_is_valid(&left_schema, &right_schema, &on)?;
        if sort_options.len() != on.len() {
            return plan_err!(
                "Expected number of sort options: {}, actual: {}",
                on.len(),
                sort_options.len()
            );
        }

        let (left_sort_exprs, right_sort_exprs): (Vec<_>, Vec<_>) = on
            .iter()
            .zip(sort_options.iter())
            .map(|((l, r), sort_op)| {
                let left = PhysicalSortExpr {
                    expr: Arc::clone(l),
                    options: *sort_op,
                };
                let right = PhysicalSortExpr {
                    expr: Arc::clone(r),
                    options: *sort_op,
                };
                (left, right)
            })
            .unzip();

        let schema =
            Arc::new(build_join_schema(&left_schema, &right_schema, &join_type).0);
        let cache =
            Self::compute_properties(&left, &right, Arc::clone(&schema), join_type, &on);
        Ok(Self {
            left,
            right,
            on,
            filter,
            join_type,
            schema,
            metrics: ExecutionPlanMetricsSet::new(),
            left_sort_exprs: LexOrdering::new(left_sort_exprs),
            right_sort_exprs: LexOrdering::new(right_sort_exprs),
            sort_options,
            null_equals_null,
            cache,
        })
    }

    /// Get probe side (e.g streaming side) information for this sort merge join.
    /// In current implementation, probe side is determined according to join type.
    pub fn probe_side(join_type: &JoinType) -> JoinSide {
        // When output schema contains only the right side, probe side is right.
        // Otherwise probe side is the left side.
        match join_type {
            JoinType::Right | JoinType::RightSemi | JoinType::RightAnti => {
                JoinSide::Right
            }
            JoinType::Inner
            | JoinType::Left
            | JoinType::Full
            | JoinType::LeftAnti
            | JoinType::LeftSemi
            | JoinType::LeftMark => JoinSide::Left,
        }
    }

    /// Calculate order preservation flags for this sort merge join.
    fn maintains_input_order(join_type: JoinType) -> Vec<bool> {
        match join_type {
            JoinType::Inner => vec![true, false],
            JoinType::Left
            | JoinType::LeftSemi
            | JoinType::LeftAnti
            | JoinType::LeftMark => vec![true, false],
            JoinType::Right | JoinType::RightSemi | JoinType::RightAnti => {
                vec![false, true]
            }
            _ => vec![false, false],
        }
    }

    /// Set of common columns used to join on
    pub fn on(&self) -> &[(PhysicalExprRef, PhysicalExprRef)] {
        &self.on
    }

    pub fn right(&self) -> &Arc<dyn ExecutionPlan> {
        &self.right
    }

    pub fn join_type(&self) -> JoinType {
        self.join_type
    }

    pub fn left(&self) -> &Arc<dyn ExecutionPlan> {
        &self.left
    }

    /// This function creates the cache object that stores the plan properties such as schema, equivalence properties, ordering, partitioning, etc.
    fn compute_properties(
        left: &Arc<dyn ExecutionPlan>,
        right: &Arc<dyn ExecutionPlan>,
        schema: SchemaRef,
        join_type: JoinType,
        join_on: JoinOnRef,
    ) -> PlanProperties {
        // Calculate equivalence properties:
        let eq_properties = join_equivalence_properties(
            left.equivalence_properties().clone(),
            right.equivalence_properties().clone(),
            &join_type,
            schema,
            &Self::maintains_input_order(join_type),
            Some(Self::probe_side(&join_type)),
            join_on,
        );

        let output_partitioning =
            symmetric_join_output_partitioning(left, right, &join_type);

        // Determine execution mode:
        let mode = execution_mode_from_children([left, right]);

        PlanProperties::new(eq_properties, output_partitioning, mode)
    }
}

impl DisplayAs for SortMergeJoinExec {
    fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> std::fmt::Result {
        match t {
            DisplayFormatType::Default | DisplayFormatType::Verbose => {
                let on = self
                    .on
                    .iter()
                    .map(|(c1, c2)| format!("({}, {})", c1, c2))
                    .collect::<Vec<String>>()
                    .join(", ");
                write!(
                    f,
                    "SortMergeJoin: join_type={:?}, on=[{}]{}",
                    self.join_type,
                    on,
                    self.filter.as_ref().map_or("".to_string(), |f| format!(
                        ", filter={}",
                        f.expression()
                    ))
                )
            }
        }
    }
}

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

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

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

    fn required_input_distribution(&self) -> Vec<Distribution> {
        let (left_expr, right_expr) = self
            .on
            .iter()
            .map(|(l, r)| (Arc::clone(l), Arc::clone(r)))
            .unzip();
        vec![
            Distribution::HashPartitioned(left_expr),
            Distribution::HashPartitioned(right_expr),
        ]
    }

    fn required_input_ordering(&self) -> Vec<Option<LexRequirement>> {
        vec![
            Some(PhysicalSortRequirement::from_sort_exprs(
                self.left_sort_exprs.iter(),
            )),
            Some(PhysicalSortRequirement::from_sort_exprs(
                self.right_sort_exprs.iter(),
            )),
        ]
    }

    fn maintains_input_order(&self) -> Vec<bool> {
        Self::maintains_input_order(self.join_type)
    }

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

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        match &children[..] {
            [left, right] => Ok(Arc::new(SortMergeJoinExec::try_new(
                Arc::clone(left),
                Arc::clone(right),
                self.on.clone(),
                self.filter.clone(),
                self.join_type,
                self.sort_options.clone(),
                self.null_equals_null,
            )?)),
            _ => internal_err!("SortMergeJoin wrong number of children"),
        }
    }

    fn execute(
        &self,
        partition: usize,
        context: Arc<TaskContext>,
    ) -> Result<SendableRecordBatchStream> {
        let left_partitions = self.left.output_partitioning().partition_count();
        let right_partitions = self.right.output_partitioning().partition_count();
        if left_partitions != right_partitions {
            return internal_err!(
                "Invalid SortMergeJoinExec, partition count mismatch {left_partitions}!={right_partitions},\
                 consider using RepartitionExec"
            );
        }
        let (on_left, on_right) = self.on.iter().cloned().unzip();
        let (streamed, buffered, on_streamed, on_buffered) =
            if SortMergeJoinExec::probe_side(&self.join_type) == JoinSide::Left {
                (
                    Arc::clone(&self.left),
                    Arc::clone(&self.right),
                    on_left,
                    on_right,
                )
            } else {
                (
                    Arc::clone(&self.right),
                    Arc::clone(&self.left),
                    on_right,
                    on_left,
                )
            };

        // execute children plans
        let streamed = streamed.execute(partition, Arc::clone(&context))?;
        let buffered = buffered.execute(partition, Arc::clone(&context))?;

        // create output buffer
        let batch_size = context.session_config().batch_size();

        // create memory reservation
        let reservation = MemoryConsumer::new(format!("SMJStream[{partition}]"))
            .register(context.memory_pool());

        // create join stream
        Ok(Box::pin(SMJStream::try_new(
            Arc::clone(&self.schema),
            self.sort_options.clone(),
            self.null_equals_null,
            streamed,
            buffered,
            on_streamed,
            on_buffered,
            self.filter.clone(),
            self.join_type,
            batch_size,
            SortMergeJoinMetrics::new(partition, &self.metrics),
            reservation,
            context.runtime_env(),
        )?))
    }

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

    fn statistics(&self) -> Result<Statistics> {
        // TODO stats: it is not possible in general to know the output size of joins
        // There are some special cases though, for example:
        // - `A LEFT JOIN B ON A.col=B.col` with `COUNT_DISTINCT(B.col)=COUNT(B.col)`
        estimate_join_statistics(
            Arc::clone(&self.left),
            Arc::clone(&self.right),
            self.on.clone(),
            &self.join_type,
            &self.schema,
        )
    }
}

/// Metrics for SortMergeJoinExec
#[allow(dead_code)]
struct SortMergeJoinMetrics {
    /// Total time for joining probe-side batches to the build-side batches
    join_time: metrics::Time,
    /// Number of batches consumed by this operator
    input_batches: Count,
    /// Number of rows consumed by this operator
    input_rows: Count,
    /// Number of batches produced by this operator
    output_batches: Count,
    /// Number of rows produced by this operator
    output_rows: Count,
    /// Peak memory used for buffered data.
    /// Calculated as sum of peak memory values across partitions
    peak_mem_used: metrics::Gauge,
    /// 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 SortMergeJoinMetrics {
    #[allow(dead_code)]
    pub fn new(partition: usize, metrics: &ExecutionPlanMetricsSet) -> Self {
        let join_time = MetricBuilder::new(metrics).subset_time("join_time", partition);
        let input_batches =
            MetricBuilder::new(metrics).counter("input_batches", partition);
        let input_rows = MetricBuilder::new(metrics).counter("input_rows", partition);
        let output_batches =
            MetricBuilder::new(metrics).counter("output_batches", partition);
        let output_rows = MetricBuilder::new(metrics).output_rows(partition);
        let peak_mem_used = MetricBuilder::new(metrics).gauge("peak_mem_used", partition);
        let spill_count = MetricBuilder::new(metrics).spill_count(partition);
        let spilled_bytes = MetricBuilder::new(metrics).spilled_bytes(partition);
        let spilled_rows = MetricBuilder::new(metrics).spilled_rows(partition);

        Self {
            join_time,
            input_batches,
            input_rows,
            output_batches,
            output_rows,
            peak_mem_used,
            spill_count,
            spilled_bytes,
            spilled_rows,
        }
    }
}

/// State of SMJ stream
#[derive(Debug, PartialEq, Eq)]
enum SMJState {
    /// Init joining with a new streamed row or a new buffered batches
    Init,
    /// Polling one streamed row or one buffered batch, or both
    Polling,
    /// Joining polled data and making output
    JoinOutput,
    /// No more output
    Exhausted,
}

/// State of streamed data stream
#[derive(Debug, PartialEq, Eq)]
enum StreamedState {
    /// Init polling
    Init,
    /// Polling one streamed row
    Polling,
    /// Ready to produce one streamed row
    Ready,
    /// No more streamed row
    Exhausted,
}

/// State of buffered data stream
#[derive(Debug, PartialEq, Eq)]
enum BufferedState {
    /// Init polling
    Init,
    /// Polling first row in the next batch
    PollingFirst,
    /// Polling rest rows in the next batch
    PollingRest,
    /// Ready to produce one batch
    Ready,
    /// No more buffered batches
    Exhausted,
}

/// Represents a chunk of joined data from streamed and buffered side
struct StreamedJoinedChunk {
    /// Index of batch in buffered_data
    buffered_batch_idx: Option<usize>,
    /// Array builder for streamed indices
    streamed_indices: UInt64Builder,
    /// Array builder for buffered indices
    /// This could contain nulls if the join is null-joined
    buffered_indices: UInt64Builder,
}

struct StreamedBatch {
    /// The streamed record batch
    pub batch: RecordBatch,
    /// The index of row in the streamed batch to compare with buffered batches
    pub idx: usize,
    /// The join key arrays of streamed batch which are used to compare with buffered batches
    /// and to produce output. They are produced by evaluating `on` expressions.
    pub join_arrays: Vec<ArrayRef>,
    /// Chunks of indices from buffered side (may be nulls) joined to streamed
    pub output_indices: Vec<StreamedJoinedChunk>,
    /// Index of currently scanned batch from buffered data
    pub buffered_batch_idx: Option<usize>,
    /// Indices that found a match for the given join filter
    /// Used for semi joins to keep track the streaming index which got a join filter match
    /// and already emitted to the output.
    pub join_filter_matched_idxs: HashSet<u64>,
}

impl StreamedBatch {
    fn new(batch: RecordBatch, on_column: &[Arc<dyn PhysicalExpr>]) -> Self {
        let join_arrays = join_arrays(&batch, on_column);
        StreamedBatch {
            batch,
            idx: 0,
            join_arrays,
            output_indices: vec![],
            buffered_batch_idx: None,
            join_filter_matched_idxs: HashSet::new(),
        }
    }

    fn new_empty(schema: SchemaRef) -> Self {
        StreamedBatch {
            batch: RecordBatch::new_empty(schema),
            idx: 0,
            join_arrays: vec![],
            output_indices: vec![],
            buffered_batch_idx: None,
            join_filter_matched_idxs: HashSet::new(),
        }
    }

    /// Appends new pair consisting of current streamed index and `buffered_idx`
    /// index of buffered batch with `buffered_batch_idx` index.
    fn append_output_pair(
        &mut self,
        buffered_batch_idx: Option<usize>,
        buffered_idx: Option<usize>,
    ) {
        // If no current chunk exists or current chunk is not for current buffered batch,
        // create a new chunk
        if self.output_indices.is_empty() || self.buffered_batch_idx != buffered_batch_idx
        {
            self.output_indices.push(StreamedJoinedChunk {
                buffered_batch_idx,
                streamed_indices: UInt64Builder::with_capacity(1),
                buffered_indices: UInt64Builder::with_capacity(1),
            });
            self.buffered_batch_idx = buffered_batch_idx;
        };
        let current_chunk = self.output_indices.last_mut().unwrap();

        // Append index of streamed batch and index of buffered batch into current chunk
        current_chunk.streamed_indices.append_value(self.idx as u64);
        if let Some(idx) = buffered_idx {
            current_chunk.buffered_indices.append_value(idx as u64);
        } else {
            current_chunk.buffered_indices.append_null();
        }
    }
}

/// A buffered batch that contains contiguous rows with same join key
#[derive(Debug)]
struct BufferedBatch {
    /// The buffered record batch
    /// None if the batch spilled to disk th
    pub batch: Option<RecordBatch>,
    /// The range in which the rows share the same join key
    pub range: Range<usize>,
    /// Array refs of the join key
    pub join_arrays: Vec<ArrayRef>,
    /// Buffered joined index (null joining buffered)
    pub null_joined: Vec<usize>,
    /// Size estimation used for reserving / releasing memory
    pub size_estimation: usize,
    /// The indices of buffered batch that failed the join filter.
    /// This is a map between buffered row index and a boolean value indicating whether all joined row
    /// of the buffered row failed the join filter.
    /// When dequeuing the buffered batch, we need to produce null joined rows for these indices.
    pub join_filter_failed_map: HashMap<u64, bool>,
    /// Current buffered batch number of rows. Equal to batch.num_rows()
    /// but if batch is spilled to disk this property is preferable
    /// and less expensive
    pub num_rows: usize,
    /// An optional temp spill file name on the disk if the batch spilled
    /// None by default
    /// Some(fileName) if the batch spilled to the disk
    pub spill_file: Option<RefCountedTempFile>,
}

impl BufferedBatch {
    fn new(
        batch: RecordBatch,
        range: Range<usize>,
        on_column: &[PhysicalExprRef],
    ) -> Self {
        let join_arrays = join_arrays(&batch, on_column);

        // Estimation is calculated as
        //   inner batch size
        // + join keys size
        // + worst case null_joined (as vector capacity * element size)
        // + Range size
        // + size of this estimation
        let size_estimation = batch.get_array_memory_size()
            + join_arrays
                .iter()
                .map(|arr| arr.get_array_memory_size())
                .sum::<usize>()
            + batch.num_rows().next_power_of_two() * size_of::<usize>()
            + size_of::<Range<usize>>()
            + size_of::<usize>();

        let num_rows = batch.num_rows();
        BufferedBatch {
            batch: Some(batch),
            range,
            join_arrays,
            null_joined: vec![],
            size_estimation,
            join_filter_failed_map: HashMap::new(),
            num_rows,
            spill_file: None,
        }
    }
}

/// Sort-merge join stream that consumes streamed and buffered data stream
/// and produces joined output
struct SMJStream {
    /// Current state of the stream
    pub state: SMJState,
    /// Output schema
    pub schema: SchemaRef,
    /// Sort options of join columns used to sort streamed and buffered data stream
    pub sort_options: Vec<SortOptions>,
    /// null == null?
    pub null_equals_null: bool,
    /// Input schema of streamed
    pub streamed_schema: SchemaRef,
    /// Input schema of buffered
    pub buffered_schema: SchemaRef,
    /// Streamed data stream
    pub streamed: SendableRecordBatchStream,
    /// Buffered data stream
    pub buffered: SendableRecordBatchStream,
    /// Current processing record batch of streamed
    pub streamed_batch: StreamedBatch,
    /// Current buffered data
    pub buffered_data: BufferedData,
    /// (used in outer join) Is current streamed row joined at least once?
    pub streamed_joined: bool,
    /// (used in outer join) Is current buffered batches joined at least once?
    pub buffered_joined: bool,
    /// State of streamed
    pub streamed_state: StreamedState,
    /// State of buffered
    pub buffered_state: BufferedState,
    /// The comparison result of current streamed row and buffered batches
    pub current_ordering: Ordering,
    /// Join key columns of streamed
    pub on_streamed: Vec<PhysicalExprRef>,
    /// Join key columns of buffered
    pub on_buffered: Vec<PhysicalExprRef>,
    /// optional join filter
    pub filter: Option<JoinFilter>,
    /// Staging output array builders
    pub output_record_batches: JoinedRecordBatches,
    /// Staging output size, including output batches and staging joined results.
    /// Increased when we put rows into buffer and decreased after we actually output batches.
    /// Used to trigger output when sufficient rows are ready
    pub output_size: usize,
    /// Target output batch size
    pub batch_size: usize,
    /// How the join is performed
    pub join_type: JoinType,
    /// Metrics
    pub join_metrics: SortMergeJoinMetrics,
    /// Memory reservation
    pub reservation: MemoryReservation,
    /// Runtime env
    pub runtime_env: Arc<RuntimeEnv>,
    /// A unique number for each batch
    pub streamed_batch_counter: AtomicUsize,
}

/// Joined batches with attached join filter information
struct JoinedRecordBatches {
    /// Joined batches. Each batch is already joined columns from left and right sources
    pub batches: Vec<RecordBatch>,
    /// Filter match mask for each row(matched/non-matched)
    pub filter_mask: BooleanBuilder,
    /// Row indices to glue together rows in `batches` and `filter_mask`
    pub row_indices: UInt64Builder,
    /// Which unique batch id the row belongs to
    /// It is necessary to differentiate rows that are distributed the way when they point to the same
    /// row index but in not the same batches
    pub batch_ids: Vec<usize>,
}

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

/// True if next index refers to either:
/// - another batch id
/// - another row index within same batch id
/// - end of row indices
#[inline(always)]
fn last_index_for_row(
    row_index: usize,
    indices: &UInt64Array,
    batch_ids: &[usize],
    indices_len: usize,
) -> bool {
    row_index == indices_len - 1
        || batch_ids[row_index] != batch_ids[row_index + 1]
        || indices.value(row_index) != indices.value(row_index + 1)
}

// Returns a corrected boolean bitmask for the given join type
// Values in the corrected bitmask can be: true, false, null
// `true` - the row found its match and sent to the output
// `null` - the row ignored, no output
// `false` - the row sent as NULL joined row
fn get_corrected_filter_mask(
    join_type: JoinType,
    row_indices: &UInt64Array,
    batch_ids: &[usize],
    filter_mask: &BooleanArray,
    expected_size: usize,
) -> Option<BooleanArray> {
    let row_indices_length = row_indices.len();
    let mut corrected_mask: BooleanBuilder =
        BooleanBuilder::with_capacity(row_indices_length);
    let mut seen_true = false;

    match join_type {
        JoinType::Left | JoinType::Right => {
            for i in 0..row_indices_length {
                let last_index =
                    last_index_for_row(i, row_indices, batch_ids, row_indices_length);
                if filter_mask.value(i) {
                    seen_true = true;
                    corrected_mask.append_value(true);
                } else if seen_true || !filter_mask.value(i) && !last_index {
                    corrected_mask.append_null(); // to be ignored and not set to output
                } else {
                    corrected_mask.append_value(false); // to be converted to null joined row
                }

                if last_index {
                    seen_true = false;
                }
            }

            // Generate null joined rows for records which have no matching join key
            let null_matched = expected_size - corrected_mask.len();
            corrected_mask.extend(vec![Some(false); null_matched]);
            Some(corrected_mask.finish())
        }
        JoinType::LeftMark => {
            for i in 0..row_indices_length {
                let last_index =
                    last_index_for_row(i, row_indices, batch_ids, row_indices_length);
                if filter_mask.value(i) && !seen_true {
                    seen_true = true;
                    corrected_mask.append_value(true);
                } else if seen_true || !filter_mask.value(i) && !last_index {
                    corrected_mask.append_null(); // to be ignored and not set to output
                } else {
                    corrected_mask.append_value(false); // to be converted to null joined row
                }

                if last_index {
                    seen_true = false;
                }
            }

            // Generate null joined rows for records which have no matching join key
            let null_matched = expected_size - corrected_mask.len();
            corrected_mask.extend(vec![Some(false); null_matched]);
            Some(corrected_mask.finish())
        }
        JoinType::LeftSemi => {
            for i in 0..row_indices_length {
                let last_index =
                    last_index_for_row(i, row_indices, batch_ids, row_indices_length);
                if filter_mask.value(i) && !seen_true {
                    seen_true = true;
                    corrected_mask.append_value(true);
                } else {
                    corrected_mask.append_null(); // to be ignored and not set to output
                }

                if last_index {
                    seen_true = false;
                }
            }

            Some(corrected_mask.finish())
        }
        JoinType::LeftAnti => {
            for i in 0..row_indices_length {
                let last_index =
                    last_index_for_row(i, row_indices, batch_ids, row_indices_length);

                if filter_mask.value(i) {
                    seen_true = true;
                }

                if last_index {
                    if !seen_true {
                        corrected_mask.append_value(true);
                    } else {
                        corrected_mask.append_null();
                    }

                    seen_true = false;
                } else {
                    corrected_mask.append_null();
                }
            }

            let null_matched = expected_size - corrected_mask.len();
            corrected_mask.extend(vec![Some(true); null_matched]);
            Some(corrected_mask.finish())
        }
        // Only outer joins needs to keep track of processed rows and apply corrected filter mask
        _ => None,
    }
}

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

    fn poll_next(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
    ) -> Poll<Option<Self::Item>> {
        let join_time = self.join_metrics.join_time.clone();
        let _timer = join_time.timer();
        loop {
            match &self.state {
                SMJState::Init => {
                    let streamed_exhausted =
                        self.streamed_state == StreamedState::Exhausted;
                    let buffered_exhausted =
                        self.buffered_state == BufferedState::Exhausted;
                    self.state = if streamed_exhausted && buffered_exhausted {
                        SMJState::Exhausted
                    } else {
                        match self.current_ordering {
                            Ordering::Less | Ordering::Equal => {
                                if !streamed_exhausted {
                                    if self.filter.is_some()
                                        && matches!(
                                            self.join_type,
                                            JoinType::Left
                                                | JoinType::LeftSemi
                                                | JoinType::LeftMark
                                                | JoinType::Right
                                                | JoinType::LeftAnti
                                        )
                                    {
                                        self.freeze_all()?;

                                        if !self.output_record_batches.batches.is_empty()
                                        {
                                            let out_filtered_batch =
                                                self.filter_joined_batch()?;
                                            return Poll::Ready(Some(Ok(
                                                out_filtered_batch,
                                            )));
                                        }
                                    }

                                    self.streamed_joined = false;
                                    self.streamed_state = StreamedState::Init;
                                }
                            }
                            Ordering::Greater => {
                                if !buffered_exhausted {
                                    self.buffered_joined = false;
                                    self.buffered_state = BufferedState::Init;
                                }
                            }
                        }
                        SMJState::Polling
                    };
                }
                SMJState::Polling => {
                    if ![StreamedState::Exhausted, StreamedState::Ready]
                        .contains(&self.streamed_state)
                    {
                        match self.poll_streamed_row(cx)? {
                            Poll::Ready(_) => {}
                            Poll::Pending => return Poll::Pending,
                        }
                    }

                    if ![BufferedState::Exhausted, BufferedState::Ready]
                        .contains(&self.buffered_state)
                    {
                        match self.poll_buffered_batches(cx)? {
                            Poll::Ready(_) => {}
                            Poll::Pending => return Poll::Pending,
                        }
                    }
                    let streamed_exhausted =
                        self.streamed_state == StreamedState::Exhausted;
                    let buffered_exhausted =
                        self.buffered_state == BufferedState::Exhausted;
                    if streamed_exhausted && buffered_exhausted {
                        self.state = SMJState::Exhausted;
                        continue;
                    }
                    self.current_ordering = self.compare_streamed_buffered()?;
                    self.state = SMJState::JoinOutput;
                }
                SMJState::JoinOutput => {
                    self.join_partial()?;

                    if self.output_size < self.batch_size {
                        if self.buffered_data.scanning_finished() {
                            self.buffered_data.scanning_reset();
                            self.state = SMJState::Init;
                        }
                    } else {
                        self.freeze_all()?;
                        if !self.output_record_batches.batches.is_empty() {
                            let record_batch = self.output_record_batch_and_reset()?;
                            // For non-filtered join output whenever the target output batch size
                            // is hit. For filtered join its needed to output on later phase
                            // because target output batch size can be hit in the middle of
                            // filtering causing the filtering to be incomplete and causing
                            // correctness issues
                            if self.filter.is_some()
                                && matches!(
                                    self.join_type,
                                    JoinType::Left
                                        | JoinType::LeftSemi
                                        | JoinType::Right
                                        | JoinType::LeftAnti
                                        | JoinType::LeftMark
                                )
                            {
                                continue;
                            }

                            return Poll::Ready(Some(Ok(record_batch)));
                        }
                        return Poll::Pending;
                    }
                }
                SMJState::Exhausted => {
                    self.freeze_all()?;

                    if !self.output_record_batches.batches.is_empty() {
                        if self.filter.is_some()
                            && matches!(
                                self.join_type,
                                JoinType::Left
                                    | JoinType::LeftSemi
                                    | JoinType::Right
                                    | JoinType::LeftAnti
                                    | JoinType::LeftMark
                            )
                        {
                            let out = self.filter_joined_batch()?;
                            return Poll::Ready(Some(Ok(out)));
                        } else {
                            let record_batch = self.output_record_batch_and_reset()?;
                            return Poll::Ready(Some(Ok(record_batch)));
                        }
                    } else {
                        return Poll::Ready(None);
                    }
                }
            }
        }
    }
}

impl SMJStream {
    #[allow(clippy::too_many_arguments)]
    pub fn try_new(
        schema: SchemaRef,
        sort_options: Vec<SortOptions>,
        null_equals_null: bool,
        streamed: SendableRecordBatchStream,
        buffered: SendableRecordBatchStream,
        on_streamed: Vec<Arc<dyn PhysicalExpr>>,
        on_buffered: Vec<Arc<dyn PhysicalExpr>>,
        filter: Option<JoinFilter>,
        join_type: JoinType,
        batch_size: usize,
        join_metrics: SortMergeJoinMetrics,
        reservation: MemoryReservation,
        runtime_env: Arc<RuntimeEnv>,
    ) -> Result<Self> {
        let streamed_schema = streamed.schema();
        let buffered_schema = buffered.schema();
        Ok(Self {
            state: SMJState::Init,
            sort_options,
            null_equals_null,
            schema,
            streamed_schema: Arc::clone(&streamed_schema),
            buffered_schema,
            streamed,
            buffered,
            streamed_batch: StreamedBatch::new_empty(streamed_schema),
            buffered_data: BufferedData::default(),
            streamed_joined: false,
            buffered_joined: false,
            streamed_state: StreamedState::Init,
            buffered_state: BufferedState::Init,
            current_ordering: Ordering::Equal,
            on_streamed,
            on_buffered,
            filter,
            output_record_batches: JoinedRecordBatches {
                batches: vec![],
                filter_mask: BooleanBuilder::new(),
                row_indices: UInt64Builder::new(),
                batch_ids: vec![],
            },
            output_size: 0,
            batch_size,
            join_type,
            join_metrics,
            reservation,
            runtime_env,
            streamed_batch_counter: AtomicUsize::new(0),
        })
    }

    /// Poll next streamed row
    fn poll_streamed_row(&mut self, cx: &mut Context) -> Poll<Option<Result<()>>> {
        loop {
            match &self.streamed_state {
                StreamedState::Init => {
                    if self.streamed_batch.idx + 1 < self.streamed_batch.batch.num_rows()
                    {
                        self.streamed_batch.idx += 1;
                        self.streamed_state = StreamedState::Ready;
                        return Poll::Ready(Some(Ok(())));
                    } else {
                        self.streamed_state = StreamedState::Polling;
                    }
                }
                StreamedState::Polling => match self.streamed.poll_next_unpin(cx)? {
                    Poll::Pending => {
                        return Poll::Pending;
                    }
                    Poll::Ready(None) => {
                        self.streamed_state = StreamedState::Exhausted;
                    }
                    Poll::Ready(Some(batch)) => {
                        if batch.num_rows() > 0 {
                            self.freeze_streamed()?;
                            self.join_metrics.input_batches.add(1);
                            self.join_metrics.input_rows.add(batch.num_rows());
                            self.streamed_batch =
                                StreamedBatch::new(batch, &self.on_streamed);
                            // Every incoming streaming batch should have its unique id
                            // Check `JoinedRecordBatches.self.streamed_batch_counter` documentation
                            self.streamed_batch_counter
                                .fetch_add(1, std::sync::atomic::Ordering::SeqCst);
                            self.streamed_state = StreamedState::Ready;
                        }
                    }
                },
                StreamedState::Ready => {
                    return Poll::Ready(Some(Ok(())));
                }
                StreamedState::Exhausted => {
                    return Poll::Ready(None);
                }
            }
        }
    }

    fn free_reservation(&mut self, buffered_batch: BufferedBatch) -> Result<()> {
        // Shrink memory usage for in-memory batches only
        if buffered_batch.spill_file.is_none() && buffered_batch.batch.is_some() {
            self.reservation
                .try_shrink(buffered_batch.size_estimation)?;
        }

        Ok(())
    }

    fn allocate_reservation(&mut self, mut buffered_batch: BufferedBatch) -> Result<()> {
        match self.reservation.try_grow(buffered_batch.size_estimation) {
            Ok(_) => {
                self.join_metrics
                    .peak_mem_used
                    .set_max(self.reservation.size());
                Ok(())
            }
            Err(_) if self.runtime_env.disk_manager.tmp_files_enabled() => {
                // spill buffered batch to disk
                let spill_file = self
                    .runtime_env
                    .disk_manager
                    .create_tmp_file("sort_merge_join_buffered_spill")?;

                if let Some(batch) = buffered_batch.batch {
                    spill_record_batches(
                        vec![batch],
                        spill_file.path().into(),
                        Arc::clone(&self.buffered_schema),
                    )?;
                    buffered_batch.spill_file = Some(spill_file);
                    buffered_batch.batch = None;

                    // update metrics to register spill
                    self.join_metrics.spill_count.add(1);
                    self.join_metrics
                        .spilled_bytes
                        .add(buffered_batch.size_estimation);
                    self.join_metrics.spilled_rows.add(buffered_batch.num_rows);
                    Ok(())
                } else {
                    internal_err!("Buffered batch has empty body")
                }
            }
            Err(e) => exec_err!("{}. Disk spilling disabled.", e.message()),
        }?;

        self.buffered_data.batches.push_back(buffered_batch);
        Ok(())
    }

    /// Poll next buffered batches
    fn poll_buffered_batches(&mut self, cx: &mut Context) -> Poll<Option<Result<()>>> {
        loop {
            match &self.buffered_state {
                BufferedState::Init => {
                    // pop previous buffered batches
                    while !self.buffered_data.batches.is_empty() {
                        let head_batch = self.buffered_data.head_batch();
                        // If the head batch is fully processed, dequeue it and produce output of it.
                        if head_batch.range.end == head_batch.num_rows {
                            self.freeze_dequeuing_buffered()?;
                            if let Some(buffered_batch) =
                                self.buffered_data.batches.pop_front()
                            {
                                self.free_reservation(buffered_batch)?;
                            }
                        } else {
                            // If the head batch is not fully processed, break the loop.
                            // Streamed batch will be joined with the head batch in the next step.
                            break;
                        }
                    }
                    if self.buffered_data.batches.is_empty() {
                        self.buffered_state = BufferedState::PollingFirst;
                    } else {
                        let tail_batch = self.buffered_data.tail_batch_mut();
                        tail_batch.range.start = tail_batch.range.end;
                        tail_batch.range.end += 1;
                        self.buffered_state = BufferedState::PollingRest;
                    }
                }
                BufferedState::PollingFirst => match self.buffered.poll_next_unpin(cx)? {
                    Poll::Pending => {
                        return Poll::Pending;
                    }
                    Poll::Ready(None) => {
                        self.buffered_state = BufferedState::Exhausted;
                        return Poll::Ready(None);
                    }
                    Poll::Ready(Some(batch)) => {
                        self.join_metrics.input_batches.add(1);
                        self.join_metrics.input_rows.add(batch.num_rows());

                        if batch.num_rows() > 0 {
                            let buffered_batch =
                                BufferedBatch::new(batch, 0..1, &self.on_buffered);

                            self.allocate_reservation(buffered_batch)?;
                            self.buffered_state = BufferedState::PollingRest;
                        }
                    }
                },
                BufferedState::PollingRest => {
                    if self.buffered_data.tail_batch().range.end
                        < self.buffered_data.tail_batch().num_rows
                    {
                        while self.buffered_data.tail_batch().range.end
                            < self.buffered_data.tail_batch().num_rows
                        {
                            if is_join_arrays_equal(
                                &self.buffered_data.head_batch().join_arrays,
                                self.buffered_data.head_batch().range.start,
                                &self.buffered_data.tail_batch().join_arrays,
                                self.buffered_data.tail_batch().range.end,
                            )? {
                                self.buffered_data.tail_batch_mut().range.end += 1;
                            } else {
                                self.buffered_state = BufferedState::Ready;
                                return Poll::Ready(Some(Ok(())));
                            }
                        }
                    } else {
                        match self.buffered.poll_next_unpin(cx)? {
                            Poll::Pending => {
                                return Poll::Pending;
                            }
                            Poll::Ready(None) => {
                                self.buffered_state = BufferedState::Ready;
                            }
                            Poll::Ready(Some(batch)) => {
                                // Polling batches coming concurrently as multiple partitions
                                self.join_metrics.input_batches.add(1);
                                self.join_metrics.input_rows.add(batch.num_rows());
                                if batch.num_rows() > 0 {
                                    let buffered_batch = BufferedBatch::new(
                                        batch,
                                        0..0,
                                        &self.on_buffered,
                                    );
                                    self.allocate_reservation(buffered_batch)?;
                                }
                            }
                        }
                    }
                }
                BufferedState::Ready => {
                    return Poll::Ready(Some(Ok(())));
                }
                BufferedState::Exhausted => {
                    return Poll::Ready(None);
                }
            }
        }
    }

    /// Get comparison result of streamed row and buffered batches
    fn compare_streamed_buffered(&self) -> Result<Ordering> {
        if self.streamed_state == StreamedState::Exhausted {
            return Ok(Ordering::Greater);
        }
        if !self.buffered_data.has_buffered_rows() {
            return Ok(Ordering::Less);
        }

        compare_join_arrays(
            &self.streamed_batch.join_arrays,
            self.streamed_batch.idx,
            &self.buffered_data.head_batch().join_arrays,
            self.buffered_data.head_batch().range.start,
            &self.sort_options,
            self.null_equals_null,
        )
    }

    /// Produce join and fill output buffer until reaching target batch size
    /// or the join is finished
    fn join_partial(&mut self) -> Result<()> {
        // Whether to join streamed rows
        let mut join_streamed = false;
        // Whether to join buffered rows
        let mut join_buffered = false;
        // For Mark join we store a dummy id to indicate the the row has a match
        let mut mark_row_as_match = false;

        // determine whether we need to join streamed/buffered rows
        match self.current_ordering {
            Ordering::Less => {
                if matches!(
                    self.join_type,
                    JoinType::Left
                        | JoinType::Right
                        | JoinType::RightSemi
                        | JoinType::Full
                        | JoinType::LeftAnti
                        | JoinType::LeftMark
                ) {
                    join_streamed = !self.streamed_joined;
                }
            }
            Ordering::Equal => {
                if matches!(self.join_type, JoinType::LeftSemi | JoinType::LeftMark) {
                    mark_row_as_match = matches!(self.join_type, JoinType::LeftMark);
                    // if the join filter is specified then its needed to output the streamed index
                    // only if it has not been emitted before
                    // the `join_filter_matched_idxs` keeps track on if streamed index has a successful
                    // filter match and prevents the same index to go into output more than once
                    if self.filter.is_some() {
                        join_streamed = !self
                            .streamed_batch
                            .join_filter_matched_idxs
                            .contains(&(self.streamed_batch.idx as u64))
                            && !self.streamed_joined;
                        // if the join filter specified there can be references to buffered columns
                        // so buffered columns are needed to access them
                        join_buffered = join_streamed;
                    } else {
                        join_streamed = !self.streamed_joined;
                    }
                }
                if matches!(
                    self.join_type,
                    JoinType::Inner | JoinType::Left | JoinType::Right | JoinType::Full
                ) {
                    join_streamed = true;
                    join_buffered = true;
                };

                if matches!(self.join_type, JoinType::LeftAnti) && self.filter.is_some() {
                    join_streamed = !self.streamed_joined;
                    join_buffered = join_streamed;
                }
            }
            Ordering::Greater => {
                if matches!(self.join_type, JoinType::Full) {
                    join_buffered = !self.buffered_joined;
                };
            }
        }
        if !join_streamed && !join_buffered {
            // no joined data
            self.buffered_data.scanning_finish();
            return Ok(());
        }

        if join_buffered {
            // joining streamed/nulls and buffered
            while !self.buffered_data.scanning_finished()
                && self.output_size < self.batch_size
            {
                let scanning_idx = self.buffered_data.scanning_idx();
                if join_streamed {
                    // Join streamed row and buffered row
                    self.streamed_batch.append_output_pair(
                        Some(self.buffered_data.scanning_batch_idx),
                        Some(scanning_idx),
                    );
                } else {
                    // Join nulls and buffered row for FULL join
                    self.buffered_data
                        .scanning_batch_mut()
                        .null_joined
                        .push(scanning_idx);
                }
                self.output_size += 1;
                self.buffered_data.scanning_advance();

                if self.buffered_data.scanning_finished() {
                    self.streamed_joined = join_streamed;
                    self.buffered_joined = true;
                }
            }
        } else {
            // joining streamed and nulls
            let scanning_batch_idx = if self.buffered_data.scanning_finished() {
                None
            } else {
                Some(self.buffered_data.scanning_batch_idx)
            };
            // For Mark join we store a dummy id to indicate the the row has a match
            let scanning_idx = mark_row_as_match.then_some(0);

            self.streamed_batch
                .append_output_pair(scanning_batch_idx, scanning_idx);
            self.output_size += 1;
            self.buffered_data.scanning_finish();
            self.streamed_joined = true;
        }
        Ok(())
    }

    fn freeze_all(&mut self) -> Result<()> {
        self.freeze_streamed()?;
        self.freeze_buffered(self.buffered_data.batches.len(), false)?;
        Ok(())
    }

    // Produces and stages record batches to ensure dequeued buffered batch
    // no longer needed:
    //   1. freezes all indices joined to streamed side
    //   2. freezes NULLs joined to dequeued buffered batch to "release" it
    fn freeze_dequeuing_buffered(&mut self) -> Result<()> {
        self.freeze_streamed()?;
        // Only freeze and produce the first batch in buffered_data as the batch is fully processed
        self.freeze_buffered(1, true)?;
        Ok(())
    }

    // Produces and stages record batch from buffered indices with corresponding
    // NULLs on streamed side.
    //
    // Applicable only in case of Full join.
    //
    // If `output_not_matched_filter` is true, this will also produce record batches
    // for buffered rows which are joined with streamed side but don't match join filter.
    fn freeze_buffered(
        &mut self,
        batch_count: usize,
        output_not_matched_filter: bool,
    ) -> Result<()> {
        if !matches!(self.join_type, JoinType::Full) {
            return Ok(());
        }
        for buffered_batch in self.buffered_data.batches.range_mut(..batch_count) {
            let buffered_indices = UInt64Array::from_iter_values(
                buffered_batch.null_joined.iter().map(|&index| index as u64),
            );
            if let Some(record_batch) = produce_buffered_null_batch(
                &self.schema,
                &self.streamed_schema,
                &buffered_indices,
                buffered_batch,
            )? {
                self.output_record_batches.batches.push(record_batch);
            }
            buffered_batch.null_joined.clear();

            // For buffered row which is joined with streamed side rows but all joined rows
            // don't satisfy the join filter
            if output_not_matched_filter {
                let not_matched_buffered_indices = buffered_batch
                    .join_filter_failed_map
                    .iter()
                    .filter_map(|(idx, failed)| if *failed { Some(*idx) } else { None })
                    .collect::<Vec<_>>();

                let buffered_indices = UInt64Array::from_iter_values(
                    not_matched_buffered_indices.iter().copied(),
                );

                if let Some(record_batch) = produce_buffered_null_batch(
                    &self.schema,
                    &self.streamed_schema,
                    &buffered_indices,
                    buffered_batch,
                )? {
                    self.output_record_batches.batches.push(record_batch);
                }
                buffered_batch.join_filter_failed_map.clear();
            }
        }
        Ok(())
    }

    // Produces and stages record batch for all output indices found
    // for current streamed batch and clears staged output indices.
    fn freeze_streamed(&mut self) -> Result<()> {
        for chunk in self.streamed_batch.output_indices.iter_mut() {
            // The row indices of joined streamed batch
            let streamed_indices = chunk.streamed_indices.finish();

            if streamed_indices.is_empty() {
                continue;
            }

            let mut streamed_columns = self
                .streamed_batch
                .batch
                .columns()
                .iter()
                .map(|column| take(column, &streamed_indices, None))
                .collect::<Result<Vec<_>, ArrowError>>()?;

            // The row indices of joined buffered batch
            let buffered_indices: UInt64Array = chunk.buffered_indices.finish();
            let mut buffered_columns = if matches!(self.join_type, JoinType::LeftMark) {
                vec![Arc::new(is_not_null(&buffered_indices)?) as ArrayRef]
            } else if matches!(self.join_type, JoinType::LeftSemi | JoinType::LeftAnti) {
                vec![]
            } else if let Some(buffered_idx) = chunk.buffered_batch_idx {
                get_buffered_columns(
                    &self.buffered_data,
                    buffered_idx,
                    &buffered_indices,
                )?
            } else {
                // If buffered batch none, meaning it is null joined batch.
                // We need to create null arrays for buffered columns to join with streamed rows.
                create_unmatched_columns(
                    self.join_type,
                    &self.buffered_schema,
                    buffered_indices.len(),
                )
            };

            let streamed_columns_length = streamed_columns.len();

            // Prepare the columns we apply join filter on later.
            // Only for joined rows between streamed and buffered.
            let filter_columns = if chunk.buffered_batch_idx.is_some() {
                if matches!(self.join_type, JoinType::Right) {
                    get_filter_column(&self.filter, &buffered_columns, &streamed_columns)
                } else if matches!(
                    self.join_type,
                    JoinType::LeftSemi | JoinType::LeftAnti | JoinType::LeftMark
                ) {
                    // unwrap is safe here as we check is_some on top of if statement
                    let buffered_columns = get_buffered_columns(
                        &self.buffered_data,
                        chunk.buffered_batch_idx.unwrap(),
                        &buffered_indices,
                    )?;

                    get_filter_column(&self.filter, &streamed_columns, &buffered_columns)
                } else {
                    get_filter_column(&self.filter, &streamed_columns, &buffered_columns)
                }
            } else {
                // This chunk is totally for null joined rows (outer join), we don't need to apply join filter.
                // Any join filter applied only on either streamed or buffered side will be pushed already.
                vec![]
            };

            let columns = if matches!(self.join_type, JoinType::Right) {
                buffered_columns.extend(streamed_columns);
                buffered_columns
            } else {
                streamed_columns.extend(buffered_columns);
                streamed_columns
            };

            let output_batch = RecordBatch::try_new(Arc::clone(&self.schema), columns)?;
            // Apply join filter if any
            if !filter_columns.is_empty() {
                if let Some(f) = &self.filter {
                    // Construct batch with only filter columns
                    let filter_batch = RecordBatch::try_new(
                        Arc::new(f.schema().clone()),
                        filter_columns,
                    )?;

                    let filter_result = f
                        .expression()
                        .evaluate(&filter_batch)?
                        .into_array(filter_batch.num_rows())?;

                    // The boolean selection mask of the join filter result
                    let pre_mask =
                        datafusion_common::cast::as_boolean_array(&filter_result)?;

                    // If there are nulls in join filter result, exclude them from selecting
                    // the rows to output.
                    let mask = if pre_mask.null_count() > 0 {
                        compute::prep_null_mask_filter(
                            datafusion_common::cast::as_boolean_array(&filter_result)?,
                        )
                    } else {
                        pre_mask.clone()
                    };

                    // Push the filtered batch which contains rows passing join filter to the output
                    if matches!(
                        self.join_type,
                        JoinType::Left
                            | JoinType::LeftSemi
                            | JoinType::Right
                            | JoinType::LeftAnti
                            | JoinType::LeftMark
                    ) {
                        self.output_record_batches
                            .batches
                            .push(output_batch.clone());
                    } else {
                        let filtered_batch = filter_record_batch(&output_batch, &mask)?;
                        self.output_record_batches.batches.push(filtered_batch);
                    }

                    self.output_record_batches.filter_mask.extend(&mask);
                    self.output_record_batches
                        .row_indices
                        .extend(&streamed_indices);
                    self.output_record_batches.batch_ids.extend(vec![
                            self.streamed_batch_counter.load(Relaxed);
                            streamed_indices.len()
                        ]);

                    // For outer joins, we need to push the null joined rows to the output if
                    // all joined rows are failed on the join filter.
                    // I.e., if all rows joined from a streamed row are failed with the join filter,
                    // we need to join it with nulls as buffered side.
                    if matches!(self.join_type, JoinType::Full) {
                        // We need to get the mask for row indices that the joined rows are failed
                        // on the join filter. I.e., for a row in streamed side, if all joined rows
                        // between it and all buffered rows are failed on the join filter, we need to
                        // output it with null columns from buffered side. For the mask here, it
                        // behaves like LeftAnti join.
                        let not_mask = if mask.null_count() > 0 {
                            // If the mask contains nulls, we need to use `prep_null_mask_filter` to
                            // handle the nulls in the mask as false to produce rows where the mask
                            // was null itself.
                            compute::not(&compute::prep_null_mask_filter(&mask))?
                        } else {
                            compute::not(&mask)?
                        };

                        let null_joined_batch =
                            filter_record_batch(&output_batch, &not_mask)?;

                        let buffered_columns = self
                            .buffered_schema
                            .fields()
                            .iter()
                            .map(|f| {
                                new_null_array(
                                    f.data_type(),
                                    null_joined_batch.num_rows(),
                                )
                            })
                            .collect::<Vec<_>>();

                        let columns = {
                            let mut streamed_columns = null_joined_batch
                                .columns()
                                .iter()
                                .take(streamed_columns_length)
                                .cloned()
                                .collect::<Vec<_>>();

                            streamed_columns.extend(buffered_columns);
                            streamed_columns
                        };

                        // Push the streamed/buffered batch joined nulls to the output
                        let null_joined_streamed_batch =
                            RecordBatch::try_new(Arc::clone(&self.schema), columns)?;

                        self.output_record_batches
                            .batches
                            .push(null_joined_streamed_batch);

                        // For full join, we also need to output the null joined rows from the buffered side.
                        // Usually this is done by `freeze_buffered`. However, if a buffered row is joined with
                        // streamed side, it won't be outputted by `freeze_buffered`.
                        // We need to check if a buffered row is joined with streamed side and output.
                        // If it is joined with streamed side, but doesn't match the join filter,
                        // we need to output it with nulls as streamed side.
                        if matches!(self.join_type, JoinType::Full) {
                            let buffered_batch = &mut self.buffered_data.batches
                                [chunk.buffered_batch_idx.unwrap()];

                            for i in 0..pre_mask.len() {
                                // If the buffered row is not joined with streamed side,
                                // skip it.
                                if buffered_indices.is_null(i) {
                                    continue;
                                }

                                let buffered_index = buffered_indices.value(i);

                                buffered_batch.join_filter_failed_map.insert(
                                    buffered_index,
                                    *buffered_batch
                                        .join_filter_failed_map
                                        .get(&buffered_index)
                                        .unwrap_or(&true)
                                        && !pre_mask.value(i),
                                );
                            }
                        }
                    }
                } else {
                    self.output_record_batches.batches.push(output_batch);
                }
            } else {
                self.output_record_batches.batches.push(output_batch);
            }
        }

        self.streamed_batch.output_indices.clear();

        Ok(())
    }

    fn output_record_batch_and_reset(&mut self) -> Result<RecordBatch> {
        let record_batch =
            concat_batches(&self.schema, &self.output_record_batches.batches)?;
        self.join_metrics.output_batches.add(1);
        self.join_metrics.output_rows.add(record_batch.num_rows());
        // If join filter exists, `self.output_size` is not accurate as we don't know the exact
        // number of rows in the output record batch. If streamed row joined with buffered rows,
        // once join filter is applied, the number of output rows may be more than 1.
        // If `record_batch` is empty, we should reset `self.output_size` to 0. It could be happened
        // when the join filter is applied and all rows are filtered out.
        if record_batch.num_rows() == 0 || record_batch.num_rows() > self.output_size {
            self.output_size = 0;
        } else {
            self.output_size -= record_batch.num_rows();
        }

        if !(self.filter.is_some()
            && matches!(
                self.join_type,
                JoinType::Left
                    | JoinType::LeftSemi
                    | JoinType::Right
                    | JoinType::LeftAnti
                    | JoinType::LeftMark
            ))
        {
            self.output_record_batches.batches.clear();
        }
        Ok(record_batch)
    }

    fn filter_joined_batch(&mut self) -> Result<RecordBatch> {
        let record_batch = self.output_record_batch_and_reset()?;
        let out_indices = self.output_record_batches.row_indices.finish();
        let out_mask = self.output_record_batches.filter_mask.finish();
        let maybe_corrected_mask = get_corrected_filter_mask(
            self.join_type,
            &out_indices,
            &self.output_record_batches.batch_ids,
            &out_mask,
            record_batch.num_rows(),
        );

        let corrected_mask = if let Some(ref filtered_join_mask) = maybe_corrected_mask {
            filtered_join_mask
        } else {
            &out_mask
        };

        let mut filtered_record_batch =
            filter_record_batch(&record_batch, corrected_mask)?;
        let buffered_columns_length = self.buffered_schema.fields.len();
        let streamed_columns_length = self.streamed_schema.fields.len();

        if matches!(
            self.join_type,
            JoinType::Left | JoinType::LeftMark | JoinType::Right
        ) {
            let null_mask = compute::not(corrected_mask)?;
            let null_joined_batch = filter_record_batch(&record_batch, &null_mask)?;

            let mut buffered_columns = create_unmatched_columns(
                self.join_type,
                &self.buffered_schema,
                null_joined_batch.num_rows(),
            );

            let columns = if matches!(self.join_type, JoinType::Right) {
                let streamed_columns = null_joined_batch
                    .columns()
                    .iter()
                    .skip(buffered_columns_length)
                    .cloned()
                    .collect::<Vec<_>>();

                buffered_columns.extend(streamed_columns);
                buffered_columns
            } else {
                // Left join or full outer join
                let mut streamed_columns = null_joined_batch
                    .columns()
                    .iter()
                    .take(streamed_columns_length)
                    .cloned()
                    .collect::<Vec<_>>();

                streamed_columns.extend(buffered_columns);
                streamed_columns
            };

            // Push the streamed/buffered batch joined nulls to the output
            let null_joined_streamed_batch =
                RecordBatch::try_new(Arc::clone(&self.schema), columns)?;

            filtered_record_batch = concat_batches(
                &self.schema,
                &[filtered_record_batch, null_joined_streamed_batch],
            )?;
        } else if matches!(self.join_type, JoinType::LeftSemi | JoinType::LeftAnti) {
            let output_column_indices = (0..streamed_columns_length).collect::<Vec<_>>();
            filtered_record_batch =
                filtered_record_batch.project(&output_column_indices)?;
        }

        self.output_record_batches.batches.clear();
        self.output_record_batches.batch_ids = vec![];
        self.output_record_batches.filter_mask = BooleanBuilder::new();
        self.output_record_batches.row_indices = UInt64Builder::new();
        Ok(filtered_record_batch)
    }
}

fn create_unmatched_columns(
    join_type: JoinType,
    schema: &SchemaRef,
    size: usize,
) -> Vec<ArrayRef> {
    if matches!(join_type, JoinType::LeftMark) {
        vec![Arc::new(BooleanArray::from(vec![false; size])) as ArrayRef]
    } else {
        schema
            .fields()
            .iter()
            .map(|f| new_null_array(f.data_type(), size))
            .collect::<Vec<_>>()
    }
}

/// Gets the arrays which join filters are applied on.
fn get_filter_column(
    join_filter: &Option<JoinFilter>,
    streamed_columns: &[ArrayRef],
    buffered_columns: &[ArrayRef],
) -> Vec<ArrayRef> {
    let mut filter_columns = vec![];

    if let Some(f) = join_filter {
        let left_columns = f
            .column_indices()
            .iter()
            .filter(|col_index| col_index.side == JoinSide::Left)
            .map(|i| Arc::clone(&streamed_columns[i.index]))
            .collect::<Vec<_>>();

        let right_columns = f
            .column_indices()
            .iter()
            .filter(|col_index| col_index.side == JoinSide::Right)
            .map(|i| Arc::clone(&buffered_columns[i.index]))
            .collect::<Vec<_>>();

        filter_columns.extend(left_columns);
        filter_columns.extend(right_columns);
    }

    filter_columns
}

fn produce_buffered_null_batch(
    schema: &SchemaRef,
    streamed_schema: &SchemaRef,
    buffered_indices: &PrimitiveArray<UInt64Type>,
    buffered_batch: &BufferedBatch,
) -> Result<Option<RecordBatch>> {
    if buffered_indices.is_empty() {
        return Ok(None);
    }

    // Take buffered (right) columns
    let buffered_columns =
        get_buffered_columns_from_batch(buffered_batch, buffered_indices)?;

    // Create null streamed (left) columns
    let mut streamed_columns = streamed_schema
        .fields()
        .iter()
        .map(|f| new_null_array(f.data_type(), buffered_indices.len()))
        .collect::<Vec<_>>();

    streamed_columns.extend(buffered_columns);

    Ok(Some(RecordBatch::try_new(
        Arc::clone(schema),
        streamed_columns,
    )?))
}

/// Get `buffered_indices` rows for `buffered_data[buffered_batch_idx]`
#[inline(always)]
fn get_buffered_columns(
    buffered_data: &BufferedData,
    buffered_batch_idx: usize,
    buffered_indices: &UInt64Array,
) -> Result<Vec<ArrayRef>> {
    get_buffered_columns_from_batch(
        &buffered_data.batches[buffered_batch_idx],
        buffered_indices,
    )
}

#[inline(always)]
fn get_buffered_columns_from_batch(
    buffered_batch: &BufferedBatch,
    buffered_indices: &UInt64Array,
) -> Result<Vec<ArrayRef>> {
    match (&buffered_batch.spill_file, &buffered_batch.batch) {
        // In memory batch
        (None, Some(batch)) => Ok(batch
            .columns()
            .iter()
            .map(|column| take(column, &buffered_indices, None))
            .collect::<Result<Vec<_>, ArrowError>>()
            .map_err(Into::<DataFusionError>::into)?),
        // If the batch was spilled to disk, less likely
        (Some(spill_file), None) => {
            let mut buffered_cols: Vec<ArrayRef> =
                Vec::with_capacity(buffered_indices.len());

            let file = BufReader::new(File::open(spill_file.path())?);
            let reader = FileReader::try_new(file, None)?;

            for batch in reader {
                batch?.columns().iter().for_each(|column| {
                    buffered_cols.extend(take(column, &buffered_indices, None))
                });
            }

            Ok(buffered_cols)
        }
        // Invalid combination
        (spill, batch) => internal_err!("Unexpected buffered batch spill status. Spill exists: {}. In-memory exists: {}", spill.is_some(), batch.is_some()),
    }
}

/// Buffered data contains all buffered batches with one unique join key
#[derive(Debug, Default)]
struct BufferedData {
    /// Buffered batches with the same key
    pub batches: VecDeque<BufferedBatch>,
    /// current scanning batch index used in join_partial()
    pub scanning_batch_idx: usize,
    /// current scanning offset used in join_partial()
    pub scanning_offset: usize,
}

impl BufferedData {
    pub fn head_batch(&self) -> &BufferedBatch {
        self.batches.front().unwrap()
    }

    pub fn tail_batch(&self) -> &BufferedBatch {
        self.batches.back().unwrap()
    }

    pub fn tail_batch_mut(&mut self) -> &mut BufferedBatch {
        self.batches.back_mut().unwrap()
    }

    pub fn has_buffered_rows(&self) -> bool {
        self.batches.iter().any(|batch| !batch.range.is_empty())
    }

    pub fn scanning_reset(&mut self) {
        self.scanning_batch_idx = 0;
        self.scanning_offset = 0;
    }

    pub fn scanning_advance(&mut self) {
        self.scanning_offset += 1;
        while !self.scanning_finished() && self.scanning_batch_finished() {
            self.scanning_batch_idx += 1;
            self.scanning_offset = 0;
        }
    }

    pub fn scanning_batch(&self) -> &BufferedBatch {
        &self.batches[self.scanning_batch_idx]
    }

    pub fn scanning_batch_mut(&mut self) -> &mut BufferedBatch {
        &mut self.batches[self.scanning_batch_idx]
    }

    pub fn scanning_idx(&self) -> usize {
        self.scanning_batch().range.start + self.scanning_offset
    }

    pub fn scanning_batch_finished(&self) -> bool {
        self.scanning_offset == self.scanning_batch().range.len()
    }

    pub fn scanning_finished(&self) -> bool {
        self.scanning_batch_idx == self.batches.len()
    }

    pub fn scanning_finish(&mut self) {
        self.scanning_batch_idx = self.batches.len();
        self.scanning_offset = 0;
    }
}

/// Get join array refs of given batch and join columns
fn join_arrays(batch: &RecordBatch, on_column: &[PhysicalExprRef]) -> Vec<ArrayRef> {
    on_column
        .iter()
        .map(|c| {
            let num_rows = batch.num_rows();
            let c = c.evaluate(batch).unwrap();
            c.into_array(num_rows).unwrap()
        })
        .collect()
}

/// Get comparison result of two rows of join arrays
fn compare_join_arrays(
    left_arrays: &[ArrayRef],
    left: usize,
    right_arrays: &[ArrayRef],
    right: usize,
    sort_options: &[SortOptions],
    null_equals_null: bool,
) -> Result<Ordering> {
    let mut res = Ordering::Equal;
    for ((left_array, right_array), sort_options) in
        left_arrays.iter().zip(right_arrays).zip(sort_options)
    {
        macro_rules! compare_value {
            ($T:ty) => {{
                let left_array = left_array.as_any().downcast_ref::<$T>().unwrap();
                let right_array = right_array.as_any().downcast_ref::<$T>().unwrap();
                match (left_array.is_null(left), right_array.is_null(right)) {
                    (false, false) => {
                        let left_value = &left_array.value(left);
                        let right_value = &right_array.value(right);
                        res = left_value.partial_cmp(right_value).unwrap();
                        if sort_options.descending {
                            res = res.reverse();
                        }
                    }
                    (true, false) => {
                        res = if sort_options.nulls_first {
                            Ordering::Less
                        } else {
                            Ordering::Greater
                        };
                    }
                    (false, true) => {
                        res = if sort_options.nulls_first {
                            Ordering::Greater
                        } else {
                            Ordering::Less
                        };
                    }
                    _ => {
                        res = if null_equals_null {
                            Ordering::Equal
                        } else {
                            Ordering::Less
                        };
                    }
                }
            }};
        }

        match left_array.data_type() {
            DataType::Null => {}
            DataType::Boolean => compare_value!(BooleanArray),
            DataType::Int8 => compare_value!(Int8Array),
            DataType::Int16 => compare_value!(Int16Array),
            DataType::Int32 => compare_value!(Int32Array),
            DataType::Int64 => compare_value!(Int64Array),
            DataType::UInt8 => compare_value!(UInt8Array),
            DataType::UInt16 => compare_value!(UInt16Array),
            DataType::UInt32 => compare_value!(UInt32Array),
            DataType::UInt64 => compare_value!(UInt64Array),
            DataType::Float32 => compare_value!(Float32Array),
            DataType::Float64 => compare_value!(Float64Array),
            DataType::Utf8 => compare_value!(StringArray),
            DataType::LargeUtf8 => compare_value!(LargeStringArray),
            DataType::Decimal128(..) => compare_value!(Decimal128Array),
            DataType::Timestamp(time_unit, None) => match time_unit {
                TimeUnit::Second => compare_value!(TimestampSecondArray),
                TimeUnit::Millisecond => compare_value!(TimestampMillisecondArray),
                TimeUnit::Microsecond => compare_value!(TimestampMicrosecondArray),
                TimeUnit::Nanosecond => compare_value!(TimestampNanosecondArray),
            },
            DataType::Date32 => compare_value!(Date32Array),
            DataType::Date64 => compare_value!(Date64Array),
            dt => {
                return not_impl_err!(
                    "Unsupported data type in sort merge join comparator: {}",
                    dt
                );
            }
        }
        if !res.is_eq() {
            break;
        }
    }
    Ok(res)
}

/// A faster version of compare_join_arrays() that only output whether
/// the given two rows are equal
fn is_join_arrays_equal(
    left_arrays: &[ArrayRef],
    left: usize,
    right_arrays: &[ArrayRef],
    right: usize,
) -> Result<bool> {
    let mut is_equal = true;
    for (left_array, right_array) in left_arrays.iter().zip(right_arrays) {
        macro_rules! compare_value {
            ($T:ty) => {{
                match (left_array.is_null(left), right_array.is_null(right)) {
                    (false, false) => {
                        let left_array =
                            left_array.as_any().downcast_ref::<$T>().unwrap();
                        let right_array =
                            right_array.as_any().downcast_ref::<$T>().unwrap();
                        if left_array.value(left) != right_array.value(right) {
                            is_equal = false;
                        }
                    }
                    (true, false) => is_equal = false,
                    (false, true) => is_equal = false,
                    _ => {}
                }
            }};
        }

        match left_array.data_type() {
            DataType::Null => {}
            DataType::Boolean => compare_value!(BooleanArray),
            DataType::Int8 => compare_value!(Int8Array),
            DataType::Int16 => compare_value!(Int16Array),
            DataType::Int32 => compare_value!(Int32Array),
            DataType::Int64 => compare_value!(Int64Array),
            DataType::UInt8 => compare_value!(UInt8Array),
            DataType::UInt16 => compare_value!(UInt16Array),
            DataType::UInt32 => compare_value!(UInt32Array),
            DataType::UInt64 => compare_value!(UInt64Array),
            DataType::Float32 => compare_value!(Float32Array),
            DataType::Float64 => compare_value!(Float64Array),
            DataType::Utf8 => compare_value!(StringArray),
            DataType::LargeUtf8 => compare_value!(LargeStringArray),
            DataType::Decimal128(..) => compare_value!(Decimal128Array),
            DataType::Timestamp(time_unit, None) => match time_unit {
                TimeUnit::Second => compare_value!(TimestampSecondArray),
                TimeUnit::Millisecond => compare_value!(TimestampMillisecondArray),
                TimeUnit::Microsecond => compare_value!(TimestampMicrosecondArray),
                TimeUnit::Nanosecond => compare_value!(TimestampNanosecondArray),
            },
            DataType::Date32 => compare_value!(Date32Array),
            DataType::Date64 => compare_value!(Date64Array),
            dt => {
                return not_impl_err!(
                    "Unsupported data type in sort merge join comparator: {}",
                    dt
                );
            }
        }
        if !is_equal {
            return Ok(false);
        }
    }
    Ok(true)
}

#[cfg(test)]
mod tests {
    use std::sync::Arc;

    use arrow::array::{Date32Array, Date64Array, Int32Array};
    use arrow::compute::{concat_batches, filter_record_batch, SortOptions};
    use arrow::datatypes::{DataType, Field, Schema};
    use arrow::record_batch::RecordBatch;
    use arrow_array::builder::{BooleanBuilder, UInt64Builder};
    use arrow_array::{BooleanArray, UInt64Array};

    use datafusion_common::JoinType::*;
    use datafusion_common::{
        assert_batches_eq, assert_batches_sorted_eq, assert_contains, JoinType, Result,
    };
    use datafusion_execution::config::SessionConfig;
    use datafusion_execution::disk_manager::DiskManagerConfig;
    use datafusion_execution::runtime_env::RuntimeEnvBuilder;
    use datafusion_execution::TaskContext;

    use crate::expressions::Column;
    use crate::joins::sort_merge_join::{get_corrected_filter_mask, JoinedRecordBatches};
    use crate::joins::utils::JoinOn;
    use crate::joins::SortMergeJoinExec;
    use crate::memory::MemoryExec;
    use crate::test::build_table_i32;
    use crate::{common, ExecutionPlan};

    fn build_table(
        a: (&str, &Vec<i32>),
        b: (&str, &Vec<i32>),
        c: (&str, &Vec<i32>),
    ) -> Arc<dyn ExecutionPlan> {
        let batch = build_table_i32(a, b, c);
        let schema = batch.schema();
        Arc::new(MemoryExec::try_new(&[vec![batch]], schema, None).unwrap())
    }

    fn build_table_from_batches(batches: Vec<RecordBatch>) -> Arc<dyn ExecutionPlan> {
        let schema = batches.first().unwrap().schema();
        Arc::new(MemoryExec::try_new(&[batches], schema, None).unwrap())
    }

    fn build_date_table(
        a: (&str, &Vec<i32>),
        b: (&str, &Vec<i32>),
        c: (&str, &Vec<i32>),
    ) -> Arc<dyn ExecutionPlan> {
        let schema = Schema::new(vec![
            Field::new(a.0, DataType::Date32, false),
            Field::new(b.0, DataType::Date32, false),
            Field::new(c.0, DataType::Date32, false),
        ]);

        let batch = RecordBatch::try_new(
            Arc::new(schema),
            vec![
                Arc::new(Date32Array::from(a.1.clone())),
                Arc::new(Date32Array::from(b.1.clone())),
                Arc::new(Date32Array::from(c.1.clone())),
            ],
        )
        .unwrap();

        let schema = batch.schema();
        Arc::new(MemoryExec::try_new(&[vec![batch]], schema, None).unwrap())
    }

    fn build_date64_table(
        a: (&str, &Vec<i64>),
        b: (&str, &Vec<i64>),
        c: (&str, &Vec<i64>),
    ) -> Arc<dyn ExecutionPlan> {
        let schema = Schema::new(vec![
            Field::new(a.0, DataType::Date64, false),
            Field::new(b.0, DataType::Date64, false),
            Field::new(c.0, DataType::Date64, false),
        ]);

        let batch = RecordBatch::try_new(
            Arc::new(schema),
            vec![
                Arc::new(Date64Array::from(a.1.clone())),
                Arc::new(Date64Array::from(b.1.clone())),
                Arc::new(Date64Array::from(c.1.clone())),
            ],
        )
        .unwrap();

        let schema = batch.schema();
        Arc::new(MemoryExec::try_new(&[vec![batch]], schema, None).unwrap())
    }

    /// returns a table with 3 columns of i32 in memory
    pub fn build_table_i32_nullable(
        a: (&str, &Vec<Option<i32>>),
        b: (&str, &Vec<Option<i32>>),
        c: (&str, &Vec<Option<i32>>),
    ) -> Arc<dyn ExecutionPlan> {
        let schema = Arc::new(Schema::new(vec![
            Field::new(a.0, DataType::Int32, true),
            Field::new(b.0, DataType::Int32, true),
            Field::new(c.0, DataType::Int32, true),
        ]));
        let batch = RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(a.1.clone())),
                Arc::new(Int32Array::from(b.1.clone())),
                Arc::new(Int32Array::from(c.1.clone())),
            ],
        )
        .unwrap();
        Arc::new(MemoryExec::try_new(&[vec![batch]], schema, None).unwrap())
    }

    fn join(
        left: Arc<dyn ExecutionPlan>,
        right: Arc<dyn ExecutionPlan>,
        on: JoinOn,
        join_type: JoinType,
    ) -> Result<SortMergeJoinExec> {
        let sort_options = vec![SortOptions::default(); on.len()];
        SortMergeJoinExec::try_new(left, right, on, None, join_type, sort_options, false)
    }

    fn join_with_options(
        left: Arc<dyn ExecutionPlan>,
        right: Arc<dyn ExecutionPlan>,
        on: JoinOn,
        join_type: JoinType,
        sort_options: Vec<SortOptions>,
        null_equals_null: bool,
    ) -> Result<SortMergeJoinExec> {
        SortMergeJoinExec::try_new(
            left,
            right,
            on,
            None,
            join_type,
            sort_options,
            null_equals_null,
        )
    }

    async fn join_collect(
        left: Arc<dyn ExecutionPlan>,
        right: Arc<dyn ExecutionPlan>,
        on: JoinOn,
        join_type: JoinType,
    ) -> Result<(Vec<String>, Vec<RecordBatch>)> {
        let sort_options = vec![SortOptions::default(); on.len()];
        join_collect_with_options(left, right, on, join_type, sort_options, false).await
    }

    async fn join_collect_with_options(
        left: Arc<dyn ExecutionPlan>,
        right: Arc<dyn ExecutionPlan>,
        on: JoinOn,
        join_type: JoinType,
        sort_options: Vec<SortOptions>,
        null_equals_null: bool,
    ) -> Result<(Vec<String>, Vec<RecordBatch>)> {
        let task_ctx = Arc::new(TaskContext::default());
        let join = join_with_options(
            left,
            right,
            on,
            join_type,
            sort_options,
            null_equals_null,
        )?;
        let columns = columns(&join.schema());

        let stream = join.execute(0, task_ctx)?;
        let batches = common::collect(stream).await?;
        Ok((columns, batches))
    }

    async fn join_collect_batch_size_equals_two(
        left: Arc<dyn ExecutionPlan>,
        right: Arc<dyn ExecutionPlan>,
        on: JoinOn,
        join_type: JoinType,
    ) -> Result<(Vec<String>, Vec<RecordBatch>)> {
        let task_ctx = TaskContext::default()
            .with_session_config(SessionConfig::new().with_batch_size(2));
        let task_ctx = Arc::new(task_ctx);
        let join = join(left, right, on, join_type)?;
        let columns = columns(&join.schema());

        let stream = join.execute(0, task_ctx)?;
        let batches = common::collect(stream).await?;
        Ok((columns, batches))
    }

    #[tokio::test]
    async fn join_inner_one() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 3]),
            ("b1", &vec![4, 5, 5]), // this has a repetition
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![4, 5, 6]),
            ("c2", &vec![70, 80, 90]),
        );

        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Inner).await?;

        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b1 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 4  | 7  | 10 | 4  | 70 |",
            "| 2  | 5  | 8  | 20 | 5  | 80 |",
            "| 3  | 5  | 9  | 20 | 5  | 80 |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_inner_two() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 2]),
            ("b2", &vec![1, 2, 2]),
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a1", &vec![1, 2, 3]),
            ("b2", &vec![1, 2, 2]),
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![
            (
                Arc::new(Column::new_with_schema("a1", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("a1", &right.schema())?) as _,
            ),
            (
                Arc::new(Column::new_with_schema("b2", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
            ),
        ];

        let (_columns, batches) = join_collect(left, right, on, Inner).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b2 | c1 | a1 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 1  | 7  | 1  | 1  | 70 |",
            "| 2  | 2  | 8  | 2  | 2  | 80 |",
            "| 2  | 2  | 9  | 2  | 2  | 80 |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_inner_two_two() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 1, 2]),
            ("b2", &vec![1, 1, 2]),
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a1", &vec![1, 1, 3]),
            ("b2", &vec![1, 1, 2]),
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![
            (
                Arc::new(Column::new_with_schema("a1", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("a1", &right.schema())?) as _,
            ),
            (
                Arc::new(Column::new_with_schema("b2", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
            ),
        ];

        let (_columns, batches) = join_collect(left, right, on, Inner).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b2 | c1 | a1 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 1  | 7  | 1  | 1  | 70 |",
            "| 1  | 1  | 7  | 1  | 1  | 80 |",
            "| 1  | 1  | 8  | 1  | 1  | 70 |",
            "| 1  | 1  | 8  | 1  | 1  | 80 |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_inner_with_nulls() -> Result<()> {
        let left = build_table_i32_nullable(
            ("a1", &vec![Some(1), Some(1), Some(2), Some(2)]),
            ("b2", &vec![None, Some(1), Some(2), Some(2)]), // null in key field
            ("c1", &vec![Some(1), None, Some(8), Some(9)]), // null in non-key field
        );
        let right = build_table_i32_nullable(
            ("a1", &vec![Some(1), Some(1), Some(2), Some(3)]),
            ("b2", &vec![None, Some(1), Some(2), Some(2)]),
            ("c2", &vec![Some(10), Some(70), Some(80), Some(90)]),
        );
        let on = vec![
            (
                Arc::new(Column::new_with_schema("a1", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("a1", &right.schema())?) as _,
            ),
            (
                Arc::new(Column::new_with_schema("b2", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
            ),
        ];

        let (_, batches) = join_collect(left, right, on, Inner).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b2 | c1 | a1 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 1  |    | 1  | 1  | 70 |",
            "| 2  | 2  | 8  | 2  | 2  | 80 |",
            "| 2  | 2  | 9  | 2  | 2  | 80 |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_inner_with_nulls_with_options() -> Result<()> {
        let left = build_table_i32_nullable(
            ("a1", &vec![Some(2), Some(2), Some(1), Some(1)]),
            ("b2", &vec![Some(2), Some(2), Some(1), None]), // null in key field
            ("c1", &vec![Some(9), Some(8), None, Some(1)]), // null in non-key field
        );
        let right = build_table_i32_nullable(
            ("a1", &vec![Some(3), Some(2), Some(1), Some(1)]),
            ("b2", &vec![Some(2), Some(2), Some(1), None]),
            ("c2", &vec![Some(90), Some(80), Some(70), Some(10)]),
        );
        let on = vec![
            (
                Arc::new(Column::new_with_schema("a1", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("a1", &right.schema())?) as _,
            ),
            (
                Arc::new(Column::new_with_schema("b2", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
            ),
        ];
        let (_, batches) = join_collect_with_options(
            left,
            right,
            on,
            Inner,
            vec![
                SortOptions {
                    descending: true,
                    nulls_first: false,
                };
                2
            ],
            true,
        )
        .await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b2 | c1 | a1 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 2  | 2  | 9  | 2  | 2  | 80 |",
            "| 2  | 2  | 8  | 2  | 2  | 80 |",
            "| 1  | 1  |    | 1  | 1  | 70 |",
            "| 1  |    | 1  | 1  |    | 10 |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_inner_output_two_batches() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 2]),
            ("b2", &vec![1, 2, 2]),
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a1", &vec![1, 2, 3]),
            ("b2", &vec![1, 2, 2]),
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![
            (
                Arc::new(Column::new_with_schema("a1", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("a1", &right.schema())?) as _,
            ),
            (
                Arc::new(Column::new_with_schema("b2", &left.schema())?) as _,
                Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
            ),
        ];

        let (_, batches) =
            join_collect_batch_size_equals_two(left, right, on, Inner).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b2 | c1 | a1 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 1  | 7  | 1  | 1  | 70 |",
            "| 2  | 2  | 8  | 2  | 2  | 80 |",
            "| 2  | 2  | 9  | 2  | 2  | 80 |",
            "+----+----+----+----+----+----+",
        ];
        assert_eq!(batches.len(), 2);
        assert_eq!(batches[0].num_rows(), 2);
        assert_eq!(batches[1].num_rows(), 1);
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_left_one() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 3]),
            ("b1", &vec![4, 5, 7]), // 7 does not exist on the right
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![4, 5, 6]),
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Left).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b1 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 4  | 7  | 10 | 4  | 70 |",
            "| 2  | 5  | 8  | 20 | 5  | 80 |",
            "| 3  | 7  | 9  |    |    |    |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_right_one() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 3]),
            ("b1", &vec![4, 5, 7]),
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![4, 5, 6]), // 6 does not exist on the left
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Right).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b1 | c2 |",
            "+----+----+----+----+----+----+",
            "| 1  | 4  | 7  | 10 | 4  | 70 |",
            "| 2  | 5  | 8  | 20 | 5  | 80 |",
            "|    |    |    | 30 | 6  | 90 |",
            "+----+----+----+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_full_one() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 3]),
            ("b1", &vec![4, 5, 7]), // 7 does not exist on the right
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30]),
            ("b2", &vec![4, 5, 6]),
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema()).unwrap()) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema()).unwrap()) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Full).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "|    |    |    | 30 | 6  | 90 |",
            "| 1  | 4  | 7  | 10 | 4  | 70 |",
            "| 2  | 5  | 8  | 20 | 5  | 80 |",
            "| 3  | 7  | 9  |    |    |    |",
            "+----+----+----+----+----+----+",
        ];
        assert_batches_sorted_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_anti() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 2, 3, 5]),
            ("b1", &vec![4, 5, 5, 7, 7]), // 7 does not exist on the right
            ("c1", &vec![7, 8, 8, 9, 11]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![4, 5, 6]),
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, LeftAnti).await?;
        let expected = [
            "+----+----+----+",
            "| a1 | b1 | c1 |",
            "+----+----+----+",
            "| 3  | 7  | 9  |",
            "| 5  | 7  | 11 |",
            "+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_semi() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 2, 3]),
            ("b1", &vec![4, 5, 5, 7]), // 7 does not exist on the right
            ("c1", &vec![7, 8, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![4, 5, 6]), // 5 is double on the right
            ("c2", &vec![70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, LeftSemi).await?;
        let expected = [
            "+----+----+----+",
            "| a1 | b1 | c1 |",
            "+----+----+----+",
            "| 1  | 4  | 7  |",
            "| 2  | 5  | 8  |",
            "| 2  | 5  | 8  |",
            "+----+----+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_left_mark() -> Result<()> {
        let left = build_table(
            ("a1", &vec![1, 2, 2, 3]),
            ("b1", &vec![4, 5, 5, 7]), // 7 does not exist on the right
            ("c1", &vec![7, 8, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![10, 20, 30, 40]),
            ("b1", &vec![4, 4, 5, 6]), // 5 is double on the right
            ("c2", &vec![60, 70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, LeftMark).await?;
        let expected = [
            "+----+----+----+-------+",
            "| a1 | b1 | c1 | mark  |",
            "+----+----+----+-------+",
            "| 1  | 4  | 7  | true  |",
            "| 2  | 5  | 8  | true  |",
            "| 2  | 5  | 8  | true  |",
            "| 3  | 7  | 9  | false |",
            "+----+----+----+-------+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_with_duplicated_column_names() -> Result<()> {
        let left = build_table(
            ("a", &vec![1, 2, 3]),
            ("b", &vec![4, 5, 7]),
            ("c", &vec![7, 8, 9]),
        );
        let right = build_table(
            ("a", &vec![10, 20, 30]),
            ("b", &vec![1, 2, 7]),
            ("c", &vec![70, 80, 90]),
        );
        let on = vec![(
            // join on a=b so there are duplicate column names on unjoined columns
            Arc::new(Column::new_with_schema("a", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Inner).await?;
        let expected = [
            "+---+---+---+----+---+----+",
            "| a | b | c | a  | b | c  |",
            "+---+---+---+----+---+----+",
            "| 1 | 4 | 7 | 10 | 1 | 70 |",
            "| 2 | 5 | 8 | 20 | 2 | 80 |",
            "+---+---+---+----+---+----+",
        ];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_date32() -> Result<()> {
        let left = build_date_table(
            ("a1", &vec![1, 2, 3]),
            ("b1", &vec![19107, 19108, 19108]), // this has a repetition
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_date_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![19107, 19108, 19109]),
            ("c2", &vec![70, 80, 90]),
        );

        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Inner).await?;

        let expected = ["+------------+------------+------------+------------+------------+------------+",
            "| a1         | b1         | c1         | a2         | b1         | c2         |",
            "+------------+------------+------------+------------+------------+------------+",
            "| 1970-01-02 | 2022-04-25 | 1970-01-08 | 1970-01-11 | 2022-04-25 | 1970-03-12 |",
            "| 1970-01-03 | 2022-04-26 | 1970-01-09 | 1970-01-21 | 2022-04-26 | 1970-03-22 |",
            "| 1970-01-04 | 2022-04-26 | 1970-01-10 | 1970-01-21 | 2022-04-26 | 1970-03-22 |",
            "+------------+------------+------------+------------+------------+------------+"];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_date64() -> Result<()> {
        let left = build_date64_table(
            ("a1", &vec![1, 2, 3]),
            ("b1", &vec![1650703441000, 1650903441000, 1650903441000]), // this has a repetition
            ("c1", &vec![7, 8, 9]),
        );
        let right = build_date64_table(
            ("a2", &vec![10, 20, 30]),
            ("b1", &vec![1650703441000, 1650503441000, 1650903441000]),
            ("c2", &vec![70, 80, 90]),
        );

        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b1", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Inner).await?;

        let expected = ["+-------------------------+---------------------+-------------------------+-------------------------+---------------------+-------------------------+",
            "| a1                      | b1                  | c1                      | a2                      | b1                  | c2                      |",
            "+-------------------------+---------------------+-------------------------+-------------------------+---------------------+-------------------------+",
            "| 1970-01-01T00:00:00.001 | 2022-04-23T08:44:01 | 1970-01-01T00:00:00.007 | 1970-01-01T00:00:00.010 | 2022-04-23T08:44:01 | 1970-01-01T00:00:00.070 |",
            "| 1970-01-01T00:00:00.002 | 2022-04-25T16:17:21 | 1970-01-01T00:00:00.008 | 1970-01-01T00:00:00.030 | 2022-04-25T16:17:21 | 1970-01-01T00:00:00.090 |",
            "| 1970-01-01T00:00:00.003 | 2022-04-25T16:17:21 | 1970-01-01T00:00:00.009 | 1970-01-01T00:00:00.030 | 2022-04-25T16:17:21 | 1970-01-01T00:00:00.090 |",
            "+-------------------------+---------------------+-------------------------+-------------------------+---------------------+-------------------------+"];
        // The output order is important as SMJ preserves sortedness
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_left_sort_order() -> Result<()> {
        let left = build_table(
            ("a1", &vec![0, 1, 2, 3, 4, 5]),
            ("b1", &vec![3, 4, 5, 6, 6, 7]),
            ("c1", &vec![4, 5, 6, 7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![0, 10, 20, 30, 40]),
            ("b2", &vec![2, 4, 6, 6, 8]),
            ("c2", &vec![50, 60, 70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Left).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 0  | 3  | 4  |    |    |    |",
            "| 1  | 4  | 5  | 10 | 4  | 60 |",
            "| 2  | 5  | 6  |    |    |    |",
            "| 3  | 6  | 7  | 20 | 6  | 70 |",
            "| 3  | 6  | 7  | 30 | 6  | 80 |",
            "| 4  | 6  | 8  | 20 | 6  | 70 |",
            "| 4  | 6  | 8  | 30 | 6  | 80 |",
            "| 5  | 7  | 9  |    |    |    |",
            "+----+----+----+----+----+----+",
        ];
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_right_sort_order() -> Result<()> {
        let left = build_table(
            ("a1", &vec![0, 1, 2, 3]),
            ("b1", &vec![3, 4, 5, 7]),
            ("c1", &vec![6, 7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![0, 10, 20, 30]),
            ("b2", &vec![2, 4, 5, 6]),
            ("c2", &vec![60, 70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Right).await?;
        let expected = [
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "|    |    |    | 0  | 2  | 60 |",
            "| 1  | 4  | 7  | 10 | 4  | 70 |",
            "| 2  | 5  | 8  | 20 | 5  | 80 |",
            "|    |    |    | 30 | 6  | 90 |",
            "+----+----+----+----+----+----+",
        ];
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_left_multiple_batches() -> Result<()> {
        let left_batch_1 = build_table_i32(
            ("a1", &vec![0, 1, 2]),
            ("b1", &vec![3, 4, 5]),
            ("c1", &vec![4, 5, 6]),
        );
        let left_batch_2 = build_table_i32(
            ("a1", &vec![3, 4, 5, 6]),
            ("b1", &vec![6, 6, 7, 9]),
            ("c1", &vec![7, 8, 9, 9]),
        );
        let right_batch_1 = build_table_i32(
            ("a2", &vec![0, 10, 20]),
            ("b2", &vec![2, 4, 6]),
            ("c2", &vec![50, 60, 70]),
        );
        let right_batch_2 = build_table_i32(
            ("a2", &vec![30, 40]),
            ("b2", &vec![6, 8]),
            ("c2", &vec![80, 90]),
        );
        let left = build_table_from_batches(vec![left_batch_1, left_batch_2]);
        let right = build_table_from_batches(vec![right_batch_1, right_batch_2]);
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Left).await?;
        let expected = vec![
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "| 0  | 3  | 4  |    |    |    |",
            "| 1  | 4  | 5  | 10 | 4  | 60 |",
            "| 2  | 5  | 6  |    |    |    |",
            "| 3  | 6  | 7  | 20 | 6  | 70 |",
            "| 3  | 6  | 7  | 30 | 6  | 80 |",
            "| 4  | 6  | 8  | 20 | 6  | 70 |",
            "| 4  | 6  | 8  | 30 | 6  | 80 |",
            "| 5  | 7  | 9  |    |    |    |",
            "| 6  | 9  | 9  |    |    |    |",
            "+----+----+----+----+----+----+",
        ];
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_right_multiple_batches() -> Result<()> {
        let right_batch_1 = build_table_i32(
            ("a2", &vec![0, 1, 2]),
            ("b2", &vec![3, 4, 5]),
            ("c2", &vec![4, 5, 6]),
        );
        let right_batch_2 = build_table_i32(
            ("a2", &vec![3, 4, 5, 6]),
            ("b2", &vec![6, 6, 7, 9]),
            ("c2", &vec![7, 8, 9, 9]),
        );
        let left_batch_1 = build_table_i32(
            ("a1", &vec![0, 10, 20]),
            ("b1", &vec![2, 4, 6]),
            ("c1", &vec![50, 60, 70]),
        );
        let left_batch_2 = build_table_i32(
            ("a1", &vec![30, 40]),
            ("b1", &vec![6, 8]),
            ("c1", &vec![80, 90]),
        );
        let left = build_table_from_batches(vec![left_batch_1, left_batch_2]);
        let right = build_table_from_batches(vec![right_batch_1, right_batch_2]);
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Right).await?;
        let expected = vec![
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "|    |    |    | 0  | 3  | 4  |",
            "| 10 | 4  | 60 | 1  | 4  | 5  |",
            "|    |    |    | 2  | 5  | 6  |",
            "| 20 | 6  | 70 | 3  | 6  | 7  |",
            "| 30 | 6  | 80 | 3  | 6  | 7  |",
            "| 20 | 6  | 70 | 4  | 6  | 8  |",
            "| 30 | 6  | 80 | 4  | 6  | 8  |",
            "|    |    |    | 5  | 7  | 9  |",
            "|    |    |    | 6  | 9  | 9  |",
            "+----+----+----+----+----+----+",
        ];
        assert_batches_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn join_full_multiple_batches() -> Result<()> {
        let left_batch_1 = build_table_i32(
            ("a1", &vec![0, 1, 2]),
            ("b1", &vec![3, 4, 5]),
            ("c1", &vec![4, 5, 6]),
        );
        let left_batch_2 = build_table_i32(
            ("a1", &vec![3, 4, 5, 6]),
            ("b1", &vec![6, 6, 7, 9]),
            ("c1", &vec![7, 8, 9, 9]),
        );
        let right_batch_1 = build_table_i32(
            ("a2", &vec![0, 10, 20]),
            ("b2", &vec![2, 4, 6]),
            ("c2", &vec![50, 60, 70]),
        );
        let right_batch_2 = build_table_i32(
            ("a2", &vec![30, 40]),
            ("b2", &vec![6, 8]),
            ("c2", &vec![80, 90]),
        );
        let left = build_table_from_batches(vec![left_batch_1, left_batch_2]);
        let right = build_table_from_batches(vec![right_batch_1, right_batch_2]);
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];

        let (_, batches) = join_collect(left, right, on, Full).await?;
        let expected = vec![
            "+----+----+----+----+----+----+",
            "| a1 | b1 | c1 | a2 | b2 | c2 |",
            "+----+----+----+----+----+----+",
            "|    |    |    | 0  | 2  | 50 |",
            "|    |    |    | 40 | 8  | 90 |",
            "| 0  | 3  | 4  |    |    |    |",
            "| 1  | 4  | 5  | 10 | 4  | 60 |",
            "| 2  | 5  | 6  |    |    |    |",
            "| 3  | 6  | 7  | 20 | 6  | 70 |",
            "| 3  | 6  | 7  | 30 | 6  | 80 |",
            "| 4  | 6  | 8  | 20 | 6  | 70 |",
            "| 4  | 6  | 8  | 30 | 6  | 80 |",
            "| 5  | 7  | 9  |    |    |    |",
            "| 6  | 9  | 9  |    |    |    |",
            "+----+----+----+----+----+----+",
        ];
        assert_batches_sorted_eq!(expected, &batches);
        Ok(())
    }

    #[tokio::test]
    async fn overallocation_single_batch_no_spill() -> Result<()> {
        let left = build_table(
            ("a1", &vec![0, 1, 2, 3, 4, 5]),
            ("b1", &vec![1, 2, 3, 4, 5, 6]),
            ("c1", &vec![4, 5, 6, 7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![0, 10, 20, 30, 40]),
            ("b2", &vec![1, 3, 4, 6, 8]),
            ("c2", &vec![50, 60, 70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];
        let sort_options = vec![SortOptions::default(); on.len()];

        let join_types = vec![Inner, Left, Right, Full, LeftSemi, LeftAnti, LeftMark];

        // Disable DiskManager to prevent spilling
        let runtime = RuntimeEnvBuilder::new()
            .with_memory_limit(100, 1.0)
            .with_disk_manager(DiskManagerConfig::Disabled)
            .build_arc()?;
        let session_config = SessionConfig::default().with_batch_size(50);

        for join_type in join_types {
            let task_ctx = TaskContext::default()
                .with_session_config(session_config.clone())
                .with_runtime(Arc::clone(&runtime));
            let task_ctx = Arc::new(task_ctx);

            let join = join_with_options(
                Arc::clone(&left),
                Arc::clone(&right),
                on.clone(),
                join_type,
                sort_options.clone(),
                false,
            )?;

            let stream = join.execute(0, task_ctx)?;
            let err = common::collect(stream).await.unwrap_err();

            assert_contains!(err.to_string(), "Failed to allocate additional");
            assert_contains!(err.to_string(), "SMJStream[0]");
            assert_contains!(err.to_string(), "Disk spilling disabled");
            assert!(join.metrics().is_some());
            assert_eq!(join.metrics().unwrap().spill_count(), Some(0));
            assert_eq!(join.metrics().unwrap().spilled_bytes(), Some(0));
            assert_eq!(join.metrics().unwrap().spilled_rows(), Some(0));
        }

        Ok(())
    }

    #[tokio::test]
    async fn overallocation_multi_batch_no_spill() -> Result<()> {
        let left_batch_1 = build_table_i32(
            ("a1", &vec![0, 1]),
            ("b1", &vec![1, 1]),
            ("c1", &vec![4, 5]),
        );
        let left_batch_2 = build_table_i32(
            ("a1", &vec![2, 3]),
            ("b1", &vec![1, 1]),
            ("c1", &vec![6, 7]),
        );
        let left_batch_3 = build_table_i32(
            ("a1", &vec![4, 5]),
            ("b1", &vec![1, 1]),
            ("c1", &vec![8, 9]),
        );
        let right_batch_1 = build_table_i32(
            ("a2", &vec![0, 10]),
            ("b2", &vec![1, 1]),
            ("c2", &vec![50, 60]),
        );
        let right_batch_2 = build_table_i32(
            ("a2", &vec![20, 30]),
            ("b2", &vec![1, 1]),
            ("c2", &vec![70, 80]),
        );
        let right_batch_3 =
            build_table_i32(("a2", &vec![40]), ("b2", &vec![1]), ("c2", &vec![90]));
        let left =
            build_table_from_batches(vec![left_batch_1, left_batch_2, left_batch_3]);
        let right =
            build_table_from_batches(vec![right_batch_1, right_batch_2, right_batch_3]);
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];
        let sort_options = vec![SortOptions::default(); on.len()];

        let join_types = vec![Inner, Left, Right, Full, LeftSemi, LeftAnti, LeftMark];

        // Disable DiskManager to prevent spilling
        let runtime = RuntimeEnvBuilder::new()
            .with_memory_limit(100, 1.0)
            .with_disk_manager(DiskManagerConfig::Disabled)
            .build_arc()?;
        let session_config = SessionConfig::default().with_batch_size(50);

        for join_type in join_types {
            let task_ctx = TaskContext::default()
                .with_session_config(session_config.clone())
                .with_runtime(Arc::clone(&runtime));
            let task_ctx = Arc::new(task_ctx);
            let join = join_with_options(
                Arc::clone(&left),
                Arc::clone(&right),
                on.clone(),
                join_type,
                sort_options.clone(),
                false,
            )?;

            let stream = join.execute(0, task_ctx)?;
            let err = common::collect(stream).await.unwrap_err();

            assert_contains!(err.to_string(), "Failed to allocate additional");
            assert_contains!(err.to_string(), "SMJStream[0]");
            assert_contains!(err.to_string(), "Disk spilling disabled");
            assert!(join.metrics().is_some());
            assert_eq!(join.metrics().unwrap().spill_count(), Some(0));
            assert_eq!(join.metrics().unwrap().spilled_bytes(), Some(0));
            assert_eq!(join.metrics().unwrap().spilled_rows(), Some(0));
        }

        Ok(())
    }

    #[tokio::test]
    async fn overallocation_single_batch_spill() -> Result<()> {
        let left = build_table(
            ("a1", &vec![0, 1, 2, 3, 4, 5]),
            ("b1", &vec![1, 2, 3, 4, 5, 6]),
            ("c1", &vec![4, 5, 6, 7, 8, 9]),
        );
        let right = build_table(
            ("a2", &vec![0, 10, 20, 30, 40]),
            ("b2", &vec![1, 3, 4, 6, 8]),
            ("c2", &vec![50, 60, 70, 80, 90]),
        );
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];
        let sort_options = vec![SortOptions::default(); on.len()];

        let join_types = [Inner, Left, Right, Full, LeftSemi, LeftAnti, LeftMark];

        // Enable DiskManager to allow spilling
        let runtime = RuntimeEnvBuilder::new()
            .with_memory_limit(100, 1.0)
            .with_disk_manager(DiskManagerConfig::NewOs)
            .build_arc()?;

        for batch_size in [1, 50] {
            let session_config = SessionConfig::default().with_batch_size(batch_size);

            for join_type in &join_types {
                let task_ctx = TaskContext::default()
                    .with_session_config(session_config.clone())
                    .with_runtime(Arc::clone(&runtime));
                let task_ctx = Arc::new(task_ctx);

                let join = join_with_options(
                    Arc::clone(&left),
                    Arc::clone(&right),
                    on.clone(),
                    *join_type,
                    sort_options.clone(),
                    false,
                )?;

                let stream = join.execute(0, task_ctx)?;
                let spilled_join_result = common::collect(stream).await.unwrap();

                assert!(join.metrics().is_some());
                assert!(join.metrics().unwrap().spill_count().unwrap() > 0);
                assert!(join.metrics().unwrap().spilled_bytes().unwrap() > 0);
                assert!(join.metrics().unwrap().spilled_rows().unwrap() > 0);

                // Run the test with no spill configuration as
                let task_ctx_no_spill =
                    TaskContext::default().with_session_config(session_config.clone());
                let task_ctx_no_spill = Arc::new(task_ctx_no_spill);

                let join = join_with_options(
                    Arc::clone(&left),
                    Arc::clone(&right),
                    on.clone(),
                    *join_type,
                    sort_options.clone(),
                    false,
                )?;
                let stream = join.execute(0, task_ctx_no_spill)?;
                let no_spilled_join_result = common::collect(stream).await.unwrap();

                assert!(join.metrics().is_some());
                assert_eq!(join.metrics().unwrap().spill_count(), Some(0));
                assert_eq!(join.metrics().unwrap().spilled_bytes(), Some(0));
                assert_eq!(join.metrics().unwrap().spilled_rows(), Some(0));
                // Compare spilled and non spilled data to check spill logic doesn't corrupt the data
                assert_eq!(spilled_join_result, no_spilled_join_result);
            }
        }

        Ok(())
    }

    #[tokio::test]
    async fn overallocation_multi_batch_spill() -> Result<()> {
        let left_batch_1 = build_table_i32(
            ("a1", &vec![0, 1]),
            ("b1", &vec![1, 1]),
            ("c1", &vec![4, 5]),
        );
        let left_batch_2 = build_table_i32(
            ("a1", &vec![2, 3]),
            ("b1", &vec![1, 1]),
            ("c1", &vec![6, 7]),
        );
        let left_batch_3 = build_table_i32(
            ("a1", &vec![4, 5]),
            ("b1", &vec![1, 1]),
            ("c1", &vec![8, 9]),
        );
        let right_batch_1 = build_table_i32(
            ("a2", &vec![0, 10]),
            ("b2", &vec![1, 1]),
            ("c2", &vec![50, 60]),
        );
        let right_batch_2 = build_table_i32(
            ("a2", &vec![20, 30]),
            ("b2", &vec![1, 1]),
            ("c2", &vec![70, 80]),
        );
        let right_batch_3 =
            build_table_i32(("a2", &vec![40]), ("b2", &vec![1]), ("c2", &vec![90]));
        let left =
            build_table_from_batches(vec![left_batch_1, left_batch_2, left_batch_3]);
        let right =
            build_table_from_batches(vec![right_batch_1, right_batch_2, right_batch_3]);
        let on = vec![(
            Arc::new(Column::new_with_schema("b1", &left.schema())?) as _,
            Arc::new(Column::new_with_schema("b2", &right.schema())?) as _,
        )];
        let sort_options = vec![SortOptions::default(); on.len()];

        let join_types = [Inner, Left, Right, Full, LeftSemi, LeftAnti, LeftMark];

        // Enable DiskManager to allow spilling
        let runtime = RuntimeEnvBuilder::new()
            .with_memory_limit(500, 1.0)
            .with_disk_manager(DiskManagerConfig::NewOs)
            .build_arc()?;

        for batch_size in [1, 50] {
            let session_config = SessionConfig::default().with_batch_size(batch_size);

            for join_type in &join_types {
                let task_ctx = TaskContext::default()
                    .with_session_config(session_config.clone())
                    .with_runtime(Arc::clone(&runtime));
                let task_ctx = Arc::new(task_ctx);
                let join = join_with_options(
                    Arc::clone(&left),
                    Arc::clone(&right),
                    on.clone(),
                    *join_type,
                    sort_options.clone(),
                    false,
                )?;

                let stream = join.execute(0, task_ctx)?;
                let spilled_join_result = common::collect(stream).await.unwrap();
                assert!(join.metrics().is_some());
                assert!(join.metrics().unwrap().spill_count().unwrap() > 0);
                assert!(join.metrics().unwrap().spilled_bytes().unwrap() > 0);
                assert!(join.metrics().unwrap().spilled_rows().unwrap() > 0);

                // Run the test with no spill configuration as
                let task_ctx_no_spill =
                    TaskContext::default().with_session_config(session_config.clone());
                let task_ctx_no_spill = Arc::new(task_ctx_no_spill);

                let join = join_with_options(
                    Arc::clone(&left),
                    Arc::clone(&right),
                    on.clone(),
                    *join_type,
                    sort_options.clone(),
                    false,
                )?;
                let stream = join.execute(0, task_ctx_no_spill)?;
                let no_spilled_join_result = common::collect(stream).await.unwrap();

                assert!(join.metrics().is_some());
                assert_eq!(join.metrics().unwrap().spill_count(), Some(0));
                assert_eq!(join.metrics().unwrap().spilled_bytes(), Some(0));
                assert_eq!(join.metrics().unwrap().spilled_rows(), Some(0));
                // Compare spilled and non spilled data to check spill logic doesn't corrupt the data
                assert_eq!(spilled_join_result, no_spilled_join_result);
            }
        }

        Ok(())
    }

    fn build_joined_record_batches() -> Result<JoinedRecordBatches> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, true),
            Field::new("b", DataType::Int32, true),
            Field::new("x", DataType::Int32, true),
            Field::new("y", DataType::Int32, true),
        ]));

        let mut batches = JoinedRecordBatches {
            batches: vec![],
            filter_mask: BooleanBuilder::new(),
            row_indices: UInt64Builder::new(),
            batch_ids: vec![],
        };

        // Insert already prejoined non-filtered rows
        batches.batches.push(RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(vec![1, 1])),
                Arc::new(Int32Array::from(vec![10, 10])),
                Arc::new(Int32Array::from(vec![1, 1])),
                Arc::new(Int32Array::from(vec![11, 9])),
            ],
        )?);

        batches.batches.push(RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(vec![1])),
                Arc::new(Int32Array::from(vec![11])),
                Arc::new(Int32Array::from(vec![1])),
                Arc::new(Int32Array::from(vec![12])),
            ],
        )?);

        batches.batches.push(RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(vec![1, 1])),
                Arc::new(Int32Array::from(vec![12, 12])),
                Arc::new(Int32Array::from(vec![1, 1])),
                Arc::new(Int32Array::from(vec![11, 13])),
            ],
        )?);

        batches.batches.push(RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(vec![1])),
                Arc::new(Int32Array::from(vec![13])),
                Arc::new(Int32Array::from(vec![1])),
                Arc::new(Int32Array::from(vec![12])),
            ],
        )?);

        batches.batches.push(RecordBatch::try_new(
            Arc::clone(&schema),
            vec![
                Arc::new(Int32Array::from(vec![1, 1])),
                Arc::new(Int32Array::from(vec![14, 14])),
                Arc::new(Int32Array::from(vec![1, 1])),
                Arc::new(Int32Array::from(vec![12, 11])),
            ],
        )?);

        let streamed_indices = vec![0, 0];
        batches.batch_ids.extend(vec![0; streamed_indices.len()]);
        batches
            .row_indices
            .extend(&UInt64Array::from(streamed_indices));

        let streamed_indices = vec![1];
        batches.batch_ids.extend(vec![0; streamed_indices.len()]);
        batches
            .row_indices
            .extend(&UInt64Array::from(streamed_indices));

        let streamed_indices = vec![0, 0];
        batches.batch_ids.extend(vec![1; streamed_indices.len()]);
        batches
            .row_indices
            .extend(&UInt64Array::from(streamed_indices));

        let streamed_indices = vec![0];
        batches.batch_ids.extend(vec![2; streamed_indices.len()]);
        batches
            .row_indices
            .extend(&UInt64Array::from(streamed_indices));

        let streamed_indices = vec![0, 0];
        batches.batch_ids.extend(vec![3; streamed_indices.len()]);
        batches
            .row_indices
            .extend(&UInt64Array::from(streamed_indices));

        batches
            .filter_mask
            .extend(&BooleanArray::from(vec![true, false]));
        batches.filter_mask.extend(&BooleanArray::from(vec![true]));
        batches
            .filter_mask
            .extend(&BooleanArray::from(vec![false, true]));
        batches.filter_mask.extend(&BooleanArray::from(vec![false]));
        batches
            .filter_mask
            .extend(&BooleanArray::from(vec![false, false]));

        Ok(batches)
    }

    #[tokio::test]
    async fn test_left_outer_join_filtered_mask() -> Result<()> {
        let mut joined_batches = build_joined_record_batches()?;
        let schema = joined_batches.batches.first().unwrap().schema();

        let output = concat_batches(&schema, &joined_batches.batches)?;
        let out_mask = joined_batches.filter_mask.finish();
        let out_indices = joined_batches.row_indices.finish();

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0]),
                &[0usize],
                &BooleanArray::from(vec![true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                true, false, false, false, false, false, false, false
            ])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0]),
                &[0usize],
                &BooleanArray::from(vec![false]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                false, false, false, false, false, false, false, false
            ])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0, 0]),
                &[0usize; 2],
                &BooleanArray::from(vec![true, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                true, true, false, false, false, false, false, false
            ])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![true, true, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![true, true, true, false, false, false, false, false])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![true, false, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                Some(true),
                None,
                Some(true),
                Some(false),
                Some(false),
                Some(false),
                Some(false),
                Some(false)
            ])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, false, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                None,
                None,
                Some(true),
                Some(false),
                Some(false),
                Some(false),
                Some(false),
                Some(false)
            ])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, true, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                None,
                Some(true),
                Some(true),
                Some(false),
                Some(false),
                Some(false),
                Some(false),
                Some(false)
            ])
        );

        assert_eq!(
            get_corrected_filter_mask(
                Left,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, false, false]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![
                None,
                None,
                Some(false),
                Some(false),
                Some(false),
                Some(false),
                Some(false),
                Some(false)
            ])
        );

        let corrected_mask = get_corrected_filter_mask(
            Left,
            &out_indices,
            &joined_batches.batch_ids,
            &out_mask,
            output.num_rows(),
        )
        .unwrap();

        assert_eq!(
            corrected_mask,
            BooleanArray::from(vec![
                Some(true),
                None,
                Some(true),
                None,
                Some(true),
                Some(false),
                None,
                Some(false)
            ])
        );

        let filtered_rb = filter_record_batch(&output, &corrected_mask)?;

        assert_batches_eq!(
            &[
                "+---+----+---+----+",
                "| a | b  | x | y  |",
                "+---+----+---+----+",
                "| 1 | 10 | 1 | 11 |",
                "| 1 | 11 | 1 | 12 |",
                "| 1 | 12 | 1 | 13 |",
                "+---+----+---+----+",
            ],
            &[filtered_rb]
        );

        // output null rows

        let null_mask = arrow::compute::not(&corrected_mask)?;
        assert_eq!(
            null_mask,
            BooleanArray::from(vec![
                Some(false),
                None,
                Some(false),
                None,
                Some(false),
                Some(true),
                None,
                Some(true)
            ])
        );

        let null_joined_batch = filter_record_batch(&output, &null_mask)?;

        assert_batches_eq!(
            &[
                "+---+----+---+----+",
                "| a | b  | x | y  |",
                "+---+----+---+----+",
                "| 1 | 13 | 1 | 12 |",
                "| 1 | 14 | 1 | 11 |",
                "+---+----+---+----+",
            ],
            &[null_joined_batch]
        );
        Ok(())
    }

    #[tokio::test]
    async fn test_left_semi_join_filtered_mask() -> Result<()> {
        let mut joined_batches = build_joined_record_batches()?;
        let schema = joined_batches.batches.first().unwrap().schema();

        let output = concat_batches(&schema, &joined_batches.batches)?;
        let out_mask = joined_batches.filter_mask.finish();
        let out_indices = joined_batches.row_indices.finish();

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0]),
                &[0usize],
                &BooleanArray::from(vec![true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![true])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0]),
                &[0usize],
                &BooleanArray::from(vec![false]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0, 0]),
                &[0usize; 2],
                &BooleanArray::from(vec![true, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![Some(true), None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![true, true, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![Some(true), None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![true, false, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![Some(true), None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, false, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, Some(true),])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, true, true]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![None, Some(true), None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftSemi,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, false, false]),
                output.num_rows()
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, None])
        );

        let corrected_mask = get_corrected_filter_mask(
            LeftSemi,
            &out_indices,
            &joined_batches.batch_ids,
            &out_mask,
            output.num_rows(),
        )
        .unwrap();

        assert_eq!(
            corrected_mask,
            BooleanArray::from(vec![
                Some(true),
                None,
                Some(true),
                None,
                Some(true),
                None,
                None,
                None
            ])
        );

        let filtered_rb = filter_record_batch(&output, &corrected_mask)?;

        assert_batches_eq!(
            &[
                "+---+----+---+----+",
                "| a | b  | x | y  |",
                "+---+----+---+----+",
                "| 1 | 10 | 1 | 11 |",
                "| 1 | 11 | 1 | 12 |",
                "| 1 | 12 | 1 | 13 |",
                "+---+----+---+----+",
            ],
            &[filtered_rb]
        );

        // output null rows
        let null_mask = arrow::compute::not(&corrected_mask)?;
        assert_eq!(
            null_mask,
            BooleanArray::from(vec![
                Some(false),
                None,
                Some(false),
                None,
                Some(false),
                None,
                None,
                None
            ])
        );

        let null_joined_batch = filter_record_batch(&output, &null_mask)?;

        assert_batches_eq!(
            &[
                "+---+---+---+---+",
                "| a | b | x | y |",
                "+---+---+---+---+",
                "+---+---+---+---+",
            ],
            &[null_joined_batch]
        );
        Ok(())
    }

    #[tokio::test]
    async fn test_left_anti_join_filtered_mask() -> Result<()> {
        let mut joined_batches = build_joined_record_batches()?;
        let schema = joined_batches.batches.first().unwrap().schema();

        let output = concat_batches(&schema, &joined_batches.batches)?;
        let out_mask = joined_batches.filter_mask.finish();
        let out_indices = joined_batches.row_indices.finish();

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0]),
                &[0usize],
                &BooleanArray::from(vec![true]),
                1
            )
            .unwrap(),
            BooleanArray::from(vec![None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0]),
                &[0usize],
                &BooleanArray::from(vec![false]),
                1
            )
            .unwrap(),
            BooleanArray::from(vec![Some(true)])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0, 0]),
                &[0usize; 2],
                &BooleanArray::from(vec![true, true]),
                2
            )
            .unwrap(),
            BooleanArray::from(vec![None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![true, true, true]),
                3
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![true, false, true]),
                3
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, false, true]),
                3
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, true, true]),
                3
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, None])
        );

        assert_eq!(
            get_corrected_filter_mask(
                LeftAnti,
                &UInt64Array::from(vec![0, 0, 0]),
                &[0usize; 3],
                &BooleanArray::from(vec![false, false, false]),
                3
            )
            .unwrap(),
            BooleanArray::from(vec![None, None, Some(true)])
        );

        let corrected_mask = get_corrected_filter_mask(
            LeftAnti,
            &out_indices,
            &joined_batches.batch_ids,
            &out_mask,
            output.num_rows(),
        )
        .unwrap();

        assert_eq!(
            corrected_mask,
            BooleanArray::from(vec![
                None,
                None,
                None,
                None,
                None,
                Some(true),
                None,
                Some(true)
            ])
        );

        let filtered_rb = filter_record_batch(&output, &corrected_mask)?;

        assert_batches_eq!(
            &[
                "+---+----+---+----+",
                "| a | b  | x | y  |",
                "+---+----+---+----+",
                "| 1 | 13 | 1 | 12 |",
                "| 1 | 14 | 1 | 11 |",
                "+---+----+---+----+",
            ],
            &[filtered_rb]
        );

        // output null rows
        let null_mask = arrow::compute::not(&corrected_mask)?;
        assert_eq!(
            null_mask,
            BooleanArray::from(vec![
                None,
                None,
                None,
                None,
                None,
                Some(false),
                None,
                Some(false),
            ])
        );

        let null_joined_batch = filter_record_batch(&output, &null_mask)?;

        assert_batches_eq!(
            &[
                "+---+---+---+---+",
                "| a | b | x | y |",
                "+---+---+---+---+",
                "+---+---+---+---+",
            ],
            &[null_joined_batch]
        );
        Ok(())
    }

    /// Returns the column names on the schema
    fn columns(schema: &Schema) -> Vec<String> {
        schema.fields().iter().map(|f| f.name().clone()).collect()
    }
}