Fix record batch memory size double counting (apache#13377)

2010YOUY01 · findepi · commit a8932faffb31 · 2024-11-25T20:32:28.000+01:00
* Fix record batch memory size double counting
diff --git a/datafusion/core/tests/memory_limit/mod.rs b/datafusion/core/tests/memory_limit/mod.rs
@@ -31,6 +31,7 @@ use datafusion_execution::memory_pool::{
 };
 use datafusion_expr::{Expr, TableType};
 use datafusion_physical_expr::{LexOrdering, PhysicalSortExpr};
+use datafusion_physical_plan::spill::get_record_batch_memory_size;
 use futures::StreamExt;
 use std::any::Any;
 use std::num::NonZeroUsize;
@@ -265,14 +266,18 @@ async fn sort_spill_reservation() {
     // This test case shows how sort_spill_reservation works by
     // purposely sorting data that requires non trivial memory to
     // sort/merge.
+
+    // Merge operation needs extra memory to do row conversion, so make the
+    // memory limit larger.
+    let mem_limit = partition_size * 2;
     let test = TestCase::new()
     // This query uses a different order than the input table to
     // force a sort. It also needs to have multiple columns to
     // force RowFormat / interner that makes merge require
     // substantial memory
         .with_query("select * from t ORDER BY a , b DESC")
     // enough memory to sort if we don't try to merge it all at once
-        .with_memory_limit(partition_size)
+        .with_memory_limit(mem_limit)
     // use a single partition so only a sort is needed
         .with_scenario(scenario)
         .with_disk_manager_config(DiskManagerConfig::NewOs)
@@ -311,7 +316,7 @@ async fn sort_spill_reservation() {
         // reserve sufficient space up front for merge and this time,
         // which will force the spills to happen with less buffered
         // input and thus with enough to merge.
-        .with_sort_spill_reservation_bytes(partition_size / 2);
+        .with_sort_spill_reservation_bytes(mem_limit / 2);
 
     test.with_config(config).with_expected_success().run().await;
 }
@@ -774,7 +779,7 @@ fn make_dict_batches() -> Vec<RecordBatch> {
 
 // How many bytes does the memory from dict_batches consume?
 fn batches_byte_size(batches: &[RecordBatch]) -> usize {
-    batches.iter().map(|b| b.get_array_memory_size()).sum()
+    batches.iter().map(get_record_batch_memory_size).sum()
 }
 
 #[derive(Debug)]
diff --git a/datafusion/physical-plan/src/sorts/builder.rs b/datafusion/physical-plan/src/sorts/builder.rs
@@ -15,6 +15,7 @@
 // specific language governing permissions and limitations
 // under the License.
 
+use crate::spill::get_record_batch_memory_size;
 use arrow::compute::interleave;
 use arrow::datatypes::SchemaRef;
 use arrow::record_batch::RecordBatch;
@@ -69,7 +70,8 @@ impl BatchBuilder {
 
     /// Append a new batch in `stream_idx`
     pub fn push_batch(&mut self, stream_idx: usize, batch: RecordBatch) -> Result<()> {
-        self.reservation.try_grow(batch.get_array_memory_size())?;
+        self.reservation
+            .try_grow(get_record_batch_memory_size(&batch))?;
         let batch_idx = self.batches.len();
         self.batches.push((stream_idx, batch));
         self.cursors[stream_idx] = BatchCursor {
@@ -141,7 +143,7 @@ impl BatchBuilder {
                 stream_cursor.batch_idx = retained;
                 retained += 1;
             } else {
-                self.reservation.shrink(batch.get_array_memory_size());
+                self.reservation.shrink(get_record_batch_memory_size(batch));
             }
             retain
         });
diff --git a/datafusion/physical-plan/src/sorts/sort.rs b/datafusion/physical-plan/src/sorts/sort.rs
@@ -31,7 +31,9 @@ use crate::metrics::{
     BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricBuilder, MetricsSet,
 };
 use crate::sorts::streaming_merge::StreamingMergeBuilder;
-use crate::spill::{read_spill_as_stream, spill_record_batches};
+use crate::spill::{
+    get_record_batch_memory_size, read_spill_as_stream, spill_record_batches,
+};
 use crate::stream::RecordBatchStreamAdapter;
 use crate::topk::TopK;
 use crate::{
@@ -288,10 +290,12 @@ impl ExternalSorter {
         }
         self.reserve_memory_for_merge()?;
 
-        let size = input.get_array_memory_size();
+        let size = get_record_batch_memory_size(&input);
+
         if self.reservation.try_grow(size).is_err() {
             let before = self.reservation.size();
             self.in_mem_sort().await?;
+
             // Sorting may have freed memory, especially if fetch is `Some`
             //
             // As such we check again, and if the memory usage has dropped by
@@ -426,7 +430,7 @@ impl ExternalSorter {
         let size: usize = self
             .in_mem_batches
             .iter()
-            .map(|x| x.get_array_memory_size())
+            .map(get_record_batch_memory_size)
             .sum();
 
         // Reserve headroom for next sort/merge
@@ -521,7 +525,8 @@ impl ExternalSorter {
             // Concatenate memory batches together and sort
             let batch = concat_batches(&self.schema, &self.in_mem_batches)?;
             self.in_mem_batches.clear();
-            self.reservation.try_resize(batch.get_array_memory_size())?;
+            self.reservation
+                .try_resize(get_record_batch_memory_size(&batch))?;
             let reservation = self.reservation.take();
             return self.sort_batch_stream(batch, metrics, reservation);
         }
@@ -530,7 +535,8 @@ impl ExternalSorter {
             .into_iter()
             .map(|batch| {
                 let metrics = self.metrics.baseline.intermediate();
-                let reservation = self.reservation.split(batch.get_array_memory_size());
+                let reservation =
+                    self.reservation.split(get_record_batch_memory_size(&batch));
                 let input = self.sort_batch_stream(batch, metrics, reservation)?;
                 Ok(spawn_buffered(input, 1))
             })
@@ -557,7 +563,7 @@ impl ExternalSorter {
         metrics: BaselineMetrics,
         reservation: MemoryReservation,
     ) -> Result<SendableRecordBatchStream> {
-        assert_eq!(batch.get_array_memory_size(), reservation.size());
+        assert_eq!(get_record_batch_memory_size(&batch), reservation.size());
         let schema = batch.schema();
 
         let fetch = self.fetch;
@@ -1187,9 +1193,9 @@ mod tests {
 
         assert_eq!(metrics.output_rows().unwrap(), 10000);
         assert!(metrics.elapsed_compute().unwrap() > 0);
-        assert_eq!(metrics.spill_count().unwrap(), 4);
-        assert_eq!(metrics.spilled_bytes().unwrap(), 38784);
-        assert_eq!(metrics.spilled_rows().unwrap(), 9600);
+        assert_eq!(metrics.spill_count().unwrap(), 3);
+        assert_eq!(metrics.spilled_bytes().unwrap(), 36000);
+        assert_eq!(metrics.spilled_rows().unwrap(), 9000);
 
         let columns = result[0].columns();
 
diff --git a/datafusion/physical-plan/src/spill.rs b/datafusion/physical-plan/src/spill.rs
@@ -20,14 +20,16 @@
 use std::fs::File;
 use std::io::BufReader;
 use std::path::{Path, PathBuf};
+use std::ptr::NonNull;
 
+use arrow::array::ArrayData;
 use arrow::datatypes::SchemaRef;
 use arrow::ipc::reader::FileReader;
 use arrow::record_batch::RecordBatch;
 use log::debug;
 use tokio::sync::mpsc::Sender;
 
-use datafusion_common::{exec_datafusion_err, Result};
+use datafusion_common::{exec_datafusion_err, HashSet, Result};
 use datafusion_execution::disk_manager::RefCountedTempFile;
 use datafusion_execution::memory_pool::human_readable_size;
 use datafusion_execution::SendableRecordBatchStream;
@@ -109,10 +111,83 @@ pub fn spill_record_batch_by_size(
     Ok(())
 }
 
+/// Calculate total used memory of this batch.
+///
+/// This function is used to estimate the physical memory usage of the `RecordBatch`.
+/// It only counts the memory of large data `Buffer`s, and ignores metadata like
+/// types and pointers.
+/// The implementation will add up all unique `Buffer`'s memory
+/// size, due to:
+/// - The data pointer inside `Buffer` are memory regions returned by global memory
+///   allocator, those regions can't have overlap.
+/// - The actual used range of `ArrayRef`s inside `RecordBatch` can have overlap
+///   or reuse the same `Buffer`. For example: taking a slice from `Array`.
+///
+/// Example:
+/// For a `RecordBatch` with two columns: `col1` and `col2`, two columns are pointing
+/// to a sub-region of the same buffer.
+///
+/// {xxxxxxxxxxxxxxxxxxx} <--- buffer
+///       ^    ^  ^    ^
+///       |    |  |    |
+/// col1->{    }  |    |    
+/// col2--------->{    }
+///
+/// In the above case, `get_record_batch_memory_size` will return the size of
+/// the buffer, instead of the sum of `col1` and `col2`'s actual memory size.
+///
+/// Note: Current `RecordBatch`.get_array_memory_size()` will double count the
+/// buffer memory size if multiple arrays within the batch are sharing the same
+/// `Buffer`. This method provides temporary fix until the issue is resolved:
+/// <https://github.com/apache/arrow-rs/issues/6439>
+pub fn get_record_batch_memory_size(batch: &RecordBatch) -> usize {
+    // Store pointers to `Buffer`'s start memory address (instead of actual
+    // used data region's pointer represented by current `Array`)
+    let mut counted_buffers: HashSet<NonNull<u8>> = HashSet::new();
+    let mut total_size = 0;
+
+    for array in batch.columns() {
+        let array_data = array.to_data();
+        count_array_data_memory_size(&array_data, &mut counted_buffers, &mut total_size);
+    }
+
+    total_size
+}
+
+/// Count the memory usage of `array_data` and its children recursively.
+fn count_array_data_memory_size(
+    array_data: &ArrayData,
+    counted_buffers: &mut HashSet<NonNull<u8>>,
+    total_size: &mut usize,
+) {
+    // Count memory usage for `array_data`
+    for buffer in array_data.buffers() {
+        if counted_buffers.insert(buffer.data_ptr()) {
+            *total_size += buffer.capacity();
+        } // Otherwise the buffer's memory is already counted
+    }
+
+    if let Some(null_buffer) = array_data.nulls() {
+        if counted_buffers.insert(null_buffer.inner().inner().data_ptr()) {
+            *total_size += null_buffer.inner().inner().capacity();
+        }
+    }
+
+    // Count all children `ArrayData` recursively
+    for child in array_data.child_data() {
+        count_array_data_memory_size(child, counted_buffers, total_size);
+    }
+}
+
 #[cfg(test)]
 mod tests {
+    use super::*;
     use crate::spill::{spill_record_batch_by_size, spill_record_batches};
     use crate::test::build_table_i32;
+    use arrow::array::{Float64Array, Int32Array};
+    use arrow::datatypes::{DataType, Field, Int32Type, Schema};
+    use arrow::record_batch::RecordBatch;
+    use arrow_array::ListArray;
     use datafusion_common::Result;
     use datafusion_execution::disk_manager::DiskManagerConfig;
     use datafusion_execution::DiskManager;
@@ -147,7 +222,7 @@ mod tests {
         assert_eq!(cnt.unwrap(), num_rows);
 
         let file = BufReader::new(File::open(spill_file.path())?);
-        let reader = arrow::ipc::reader::FileReader::try_new(file, None)?;
+        let reader = FileReader::try_new(file, None)?;
 
         assert_eq!(reader.num_batches(), 2);
         assert_eq!(reader.schema(), schema);
@@ -175,11 +250,138 @@ mod tests {
         )?;
 
         let file = BufReader::new(File::open(spill_file.path())?);
-        let reader = arrow::ipc::reader::FileReader::try_new(file, None)?;
+        let reader = FileReader::try_new(file, None)?;
 
         assert_eq!(reader.num_batches(), 4);
         assert_eq!(reader.schema(), schema);
 
         Ok(())
     }
+
+    #[test]
+    fn test_get_record_batch_memory_size() {
+        // Create a simple record batch with two columns
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("ints", DataType::Int32, true),
+            Field::new("float64", DataType::Float64, false),
+        ]));
+
+        let int_array =
+            Int32Array::from(vec![Some(1), Some(2), Some(3), Some(4), Some(5)]);
+        let float64_array = Float64Array::from(vec![1.0, 2.0, 3.0, 4.0, 5.0]);
+
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(int_array), Arc::new(float64_array)],
+        )
+        .unwrap();
+
+        let size = get_record_batch_memory_size(&batch);
+        assert_eq!(size, 60);
+    }
+
+    #[test]
+    fn test_get_record_batch_memory_size_with_null() {
+        // Create a simple record batch with two columns
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("ints", DataType::Int32, true),
+            Field::new("float64", DataType::Float64, false),
+        ]));
+
+        let int_array = Int32Array::from(vec![None, Some(2), Some(3)]);
+        let float64_array = Float64Array::from(vec![1.0, 2.0, 3.0]);
+
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(int_array), Arc::new(float64_array)],
+        )
+        .unwrap();
+
+        let size = get_record_batch_memory_size(&batch);
+        assert_eq!(size, 100);
+    }
+
+    #[test]
+    fn test_get_record_batch_memory_size_empty() {
+        // Test with empty record batch
+        let schema = Arc::new(Schema::new(vec![Field::new(
+            "ints",
+            DataType::Int32,
+            false,
+        )]));
+
+        let int_array: Int32Array = Int32Array::from(vec![] as Vec<i32>);
+        let batch = RecordBatch::try_new(schema, vec![Arc::new(int_array)]).unwrap();
+
+        let size = get_record_batch_memory_size(&batch);
+        assert_eq!(size, 0, "Empty batch should have 0 memory size");
+    }
+
+    #[test]
+    fn test_get_record_batch_memory_size_shared_buffer() {
+        // Test with slices that share the same underlying buffer
+        let original = Int32Array::from(vec![1, 2, 3, 4, 5]);
+        let slice1 = original.slice(0, 3);
+        let slice2 = original.slice(2, 3);
+
+        // `RecordBatch` with `original` array
+        // ----
+        let schema_origin = Arc::new(Schema::new(vec![Field::new(
+            "origin_col",
+            DataType::Int32,
+            false,
+        )]));
+        let batch_origin =
+            RecordBatch::try_new(schema_origin, vec![Arc::new(original)]).unwrap();
+
+        // `RecordBatch` with all columns are reference to `original` array
+        // ----
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("slice1", DataType::Int32, false),
+            Field::new("slice2", DataType::Int32, false),
+        ]));
+
+        let batch_sliced =
+            RecordBatch::try_new(schema, vec![Arc::new(slice1), Arc::new(slice2)])
+                .unwrap();
+
+        // Two sizes should all be only counting the buffer in `original` array
+        let size_origin = get_record_batch_memory_size(&batch_origin);
+        let size_sliced = get_record_batch_memory_size(&batch_sliced);
+
+        assert_eq!(size_origin, size_sliced);
+    }
+
+    #[test]
+    fn test_get_record_batch_memory_size_nested_array() {
+        let schema = Arc::new(Schema::new(vec![
+            Field::new(
+                "nested_int",
+                DataType::List(Arc::new(Field::new("item", DataType::Int32, true))),
+                false,
+            ),
+            Field::new(
+                "nested_int2",
+                DataType::List(Arc::new(Field::new("item", DataType::Int32, true))),
+                false,
+            ),
+        ]));
+
+        let int_list_array = ListArray::from_iter_primitive::<Int32Type, _, _>(vec![
+            Some(vec![Some(1), Some(2), Some(3)]),
+        ]);
+
+        let int_list_array2 = ListArray::from_iter_primitive::<Int32Type, _, _>(vec![
+            Some(vec![Some(4), Some(5), Some(6)]),
+        ]);
+
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(int_list_array), Arc::new(int_list_array2)],
+        )
+        .unwrap();
+
+        let size = get_record_batch_memory_size(&batch);
+        assert_eq!(size, 8320);
+    }
 }
