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The following commit(s) were added to refs/heads/main by this push:
new be3842b55d chore: add example for how to use TrackConsumersPool
(#17213)
be3842b55d is described below
commit be3842b55d61095d77aae54426726ddd1eed5f2c
Author: wiedld <[email protected]>
AuthorDate: Fri Aug 22 14:13:29 2025 -0700
chore: add example for how to use TrackConsumersPool (#17213)
* chore: add docs for how to use TrackConsumersPool
* fix: error in example
* chore: reference the default pool, not a specific pool
* refactor: move code examples to datafusion-examples and reference in docs
* chore: add more docs with links to examples
* chore: examples cleanup
* Make description more concise
* Remove confusing example
* Simplify example
---------
Co-authored-by: Andrew Lamb <[email protected]>
---
datafusion-examples/README.md | 2 +
.../examples/memory_pool_execution_plan.rs | 303 +++++++++++++++++++++
.../examples/memory_pool_tracking.rs | 127 +++++++++
datafusion/execution/src/memory_pool/pool.rs | 48 +++-
datafusion/execution/src/runtime_env.rs | 3 +-
datafusion/physical-plan/src/execution_plan.rs | 9 +
6 files changed, 490 insertions(+), 2 deletions(-)
diff --git a/datafusion-examples/README.md b/datafusion-examples/README.md
index 75a53bc568..8dadd0b9f8 100644
--- a/datafusion-examples/README.md
+++ b/datafusion-examples/README.md
@@ -65,6 +65,8 @@ cargo run --example dataframe
- [`file_stream_provider.rs`](examples/file_stream_provider.rs): Run a query
on `FileStreamProvider` which implements `StreamProvider` for reading and
writing to arbitrary stream sources / sinks.
- [`flight_sql_server.rs`](examples/flight/flight_sql_server.rs): Run
DataFusion as a standalone process and execute SQL queries from JDBC clients
- [`function_factory.rs`](examples/function_factory.rs): Register `CREATE
FUNCTION` handler to implement SQL macros
+- [`memory_pool_tracking.rs`](examples/memory_pool_tracking.rs): Demonstrates
TrackConsumersPool for memory tracking and debugging with enhanced error
messages
+- [`memory_pool_execution_plan.rs`](examples/memory_pool_execution_plan.rs):
Shows how to implement memory-aware ExecutionPlan with memory reservation and
spilling
- [`optimizer_rule.rs`](examples/optimizer_rule.rs): Use a custom
OptimizerRule to replace certain predicates
- [`parquet_embedded_index.rs`](examples/parquet_embedded_index.rs): Store a
custom index inside a Parquet file and use it to speed up queries
- [`parquet_encrypted.rs`](examples/parquet_encrypted.rs): Read and write
encrypted Parquet files using DataFusion
diff --git a/datafusion-examples/examples/memory_pool_execution_plan.rs
b/datafusion-examples/examples/memory_pool_execution_plan.rs
new file mode 100644
index 0000000000..7a77e99691
--- /dev/null
+++ b/datafusion-examples/examples/memory_pool_execution_plan.rs
@@ -0,0 +1,303 @@
+// 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.
+
+//! This example demonstrates how to implement custom ExecutionPlans that
properly
+//! use memory tracking through TrackConsumersPool.
+//!
+//! This shows the pattern for implementing memory-aware operators that:
+//! - Register memory consumers with the pool
+//! - Reserve memory before allocating
+//! - Handle memory pressure by spilling to disk
+//! - Release memory when done
+
+use arrow::record_batch::RecordBatch;
+use arrow_schema::SchemaRef;
+use datafusion::common::record_batch;
+use datafusion::datasource::{memory::MemTable, DefaultTableSource};
+use datafusion::error::{DataFusionError, Result};
+use datafusion::execution::memory_pool::{MemoryConsumer, MemoryReservation};
+use datafusion::execution::runtime_env::RuntimeEnvBuilder;
+use datafusion::execution::{SendableRecordBatchStream, TaskContext};
+use datafusion::logical_expr::LogicalPlanBuilder;
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::{
+ DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, Statistics,
+};
+use datafusion::prelude::*;
+use futures::stream::{StreamExt, TryStreamExt};
+use std::any::Any;
+use std::fmt;
+use std::sync::Arc;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+ println!("=== DataFusion ExecutionPlan Memory Tracking Example ===\n");
+
+ // Set up a runtime with memory tracking
+ // Set a low memory limit to trigger spilling on the second batch
+ let runtime = RuntimeEnvBuilder::new()
+ .with_memory_limit(15000, 1.0) // Allow only enough for 1 batch at once
+ .build_arc()?;
+
+ let config = SessionConfig::new().with_coalesce_batches(false);
+ let ctx = SessionContext::new_with_config_rt(config, runtime.clone());
+
+ // Create smaller batches to ensure we get multiple RecordBatches from the
scan
+ // Make each batch smaller than the memory limit to force multiple batches
+ let batch1 = record_batch!(
+ ("id", Int32, vec![1; 800]),
+ ("name", Utf8, vec!["Alice"; 800])
+ )?;
+
+ let batch2 = record_batch!(
+ ("id", Int32, vec![2; 800]),
+ ("name", Utf8, vec!["Bob"; 800])
+ )?;
+
+ let batch3 = record_batch!(
+ ("id", Int32, vec![3; 800]),
+ ("name", Utf8, vec!["Charlie"; 800])
+ )?;
+
+ let batch4 = record_batch!(
+ ("id", Int32, vec![4; 800]),
+ ("name", Utf8, vec!["David"; 800])
+ )?;
+
+ let schema = batch1.schema();
+
+ // Create a single MemTable with all batches in one partition to preserve
order but ensure streaming
+ let mem_table = Arc::new(MemTable::try_new(
+ Arc::clone(&schema),
+ vec![vec![batch1, batch2, batch3, batch4]], // Single partition with
multiple batches
+ )?);
+
+ // Build logical plan with a single scan that will yield multiple batches
+ let table_source = Arc::new(DefaultTableSource::new(mem_table));
+ let logical_plan =
+ LogicalPlanBuilder::scan("multi_batch_table", table_source,
None)?.build()?;
+
+ // Convert to physical plan
+ let physical_plan = ctx.state().create_physical_plan(&logical_plan).await?;
+
+ println!("Example: Custom Memory-Aware BufferingExecutionPlan");
+ println!("---------------------------------------------------");
+
+ // Wrap our input plan with our custom BufferingExecutionPlan
+ let buffering_plan = Arc::new(BufferingExecutionPlan::new(schema,
physical_plan));
+
+ // Create a task context from our runtime
+ let task_ctx = Arc::new(TaskContext::default().with_runtime(runtime));
+
+ // Execute the plan directly to demonstrate memory tracking
+ println!("Executing BufferingExecutionPlan with memory tracking...");
+ println!("Memory limit: 15000 bytes - should trigger spill on later
batches\n");
+
+ let stream = buffering_plan.execute(0, task_ctx.clone())?;
+ let _results: Vec<RecordBatch> = stream.try_collect().await?;
+
+ println!("\nSuccessfully executed BufferingExecutionPlan!");
+
+ println!("\nThe BufferingExecutionPlan processed 4 input batches and");
+ println!("demonstrated memory tracking with spilling behavior when the");
+ println!("memory limit was exceeded by later batches.");
+ println!("Check the console output above to see the spill messages.");
+
+ Ok(())
+}
+
+/// Example of an external batch bufferer that uses memory reservation.
+///
+/// It's a simple example which spills all existing data to disk
+/// whenever the memory limit is reached.
+struct ExternalBatchBufferer {
+ buffer: Vec<u8>,
+ reservation: MemoryReservation,
+ spill_count: usize,
+}
+
+impl ExternalBatchBufferer {
+ fn new(reservation: MemoryReservation) -> Self {
+ Self {
+ buffer: Vec::new(),
+ reservation,
+ spill_count: 0,
+ }
+ }
+
+ fn add_batch(&mut self, batch_data: Vec<u8>) -> Result<()> {
+ let additional_memory = batch_data.len();
+
+ // Try to reserve memory before allocating
+ if self.reservation.try_grow(additional_memory).is_err() {
+ // Memory limit reached - handle by spilling
+ println!(
+ "Memory limit reached, spilling {} bytes to disk",
+ self.buffer.len()
+ );
+ self.spill_to_disk()?;
+
+ // Try again after spilling
+ self.reservation.try_grow(additional_memory)?;
+ }
+
+ self.buffer.extend_from_slice(&batch_data);
+ println!(
+ "Added batch of {} bytes, total buffered: {} bytes",
+ additional_memory,
+ self.buffer.len()
+ );
+ Ok(())
+ }
+
+ fn spill_to_disk(&mut self) -> Result<()> {
+ // Simulate writing buffer to disk
+ self.spill_count += 1;
+ println!(
+ "Spill #{}: Writing {} bytes to disk",
+ self.spill_count,
+ self.buffer.len()
+ );
+
+ // Free the memory after spilling
+ let freed_bytes = self.buffer.len();
+ self.buffer.clear();
+ self.reservation.shrink(freed_bytes);
+
+ Ok(())
+ }
+
+ fn finish(&mut self) -> Vec<u8> {
+ let result = std::mem::take(&mut self.buffer);
+ // Free the memory when done
+ self.reservation.free();
+ println!("Finished processing, released {} bytes", result.len());
+ result
+ }
+}
+
+/// Example of an ExecutionPlan that uses the ExternalBatchBufferer.
+#[derive(Debug)]
+struct BufferingExecutionPlan {
+ schema: SchemaRef,
+ input: Arc<dyn ExecutionPlan>,
+ properties: PlanProperties,
+}
+
+impl BufferingExecutionPlan {
+ fn new(schema: SchemaRef, input: Arc<dyn ExecutionPlan>) -> Self {
+ let properties = input.properties().clone();
+
+ Self {
+ schema,
+ input,
+ properties,
+ }
+ }
+}
+
+impl DisplayAs for BufferingExecutionPlan {
+ fn fmt_as(&self, _t: DisplayFormatType, f: &mut fmt::Formatter) ->
fmt::Result {
+ write!(f, "BufferingExecutionPlan")
+ }
+}
+
+impl ExecutionPlan for BufferingExecutionPlan {
+ fn name(&self) -> &'static str {
+ "BufferingExecutionPlan"
+ }
+
+ fn as_any(&self) -> &dyn Any {
+ self
+ }
+
+ fn schema(&self) -> SchemaRef {
+ self.schema.clone()
+ }
+
+ fn properties(&self) -> &PlanProperties {
+ &self.properties
+ }
+
+ fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
+ vec![&self.input]
+ }
+
+ fn with_new_children(
+ self: Arc<Self>,
+ children: Vec<Arc<dyn ExecutionPlan>>,
+ ) -> Result<Arc<dyn ExecutionPlan>> {
+ if children.len() == 1 {
+ Ok(Arc::new(BufferingExecutionPlan::new(
+ self.schema.clone(),
+ children[0].clone(),
+ )))
+ } else {
+ Err(DataFusionError::Internal(
+ "BufferingExecutionPlan must have exactly one
child".to_string(),
+ ))
+ }
+ }
+
+ fn execute(
+ &self,
+ partition: usize,
+ context: Arc<TaskContext>,
+ ) -> Result<SendableRecordBatchStream> {
+ // Register memory consumer with the context's memory pool
+ let reservation = MemoryConsumer::new("MyExternalBatchBufferer")
+ .with_can_spill(true)
+ .register(context.memory_pool());
+
+ // Incoming stream of batches
+ let mut input_stream = self.input.execute(partition, context)?;
+
+ // Process the stream and collect all batches
+ Ok(Box::pin(RecordBatchStreamAdapter::new(
+ self.schema(),
+ futures::stream::once(async move {
+ let mut operator = ExternalBatchBufferer::new(reservation);
+
+ while let Some(batch) = input_stream.next().await {
+ let batch = batch?;
+
+ // Convert RecordBatch to bytes for this example
+ let batch_data = vec![1u8; batch.get_array_memory_size()];
+
+ operator.add_batch(batch_data)?;
+ }
+
+ // Finish processing and get results
+ let _final_result = operator.finish();
+ // In a real implementation, you would convert final_result
back to RecordBatches
+
+ // Since this is a simplified example, return an empty batch
+ // In a real implementation, you would create a batch stream
from the processed results
+ record_batch!(("id", Int32, vec![5]), ("name", Utf8,
vec!["Eve"]))
+ .map_err(|e| {
+ DataFusionError::Execution(format!(
+ "Failed to create final RecordBatch: {e}",
+ ))
+ })
+ }),
+ )))
+ }
+
+ fn statistics(&self) -> Result<Statistics> {
+ Ok(Statistics::new_unknown(&self.schema))
+ }
+}
diff --git a/datafusion-examples/examples/memory_pool_tracking.rs
b/datafusion-examples/examples/memory_pool_tracking.rs
new file mode 100644
index 0000000000..287a1d25c8
--- /dev/null
+++ b/datafusion-examples/examples/memory_pool_tracking.rs
@@ -0,0 +1,127 @@
+// 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.
+
+//! This example demonstrates how to use TrackConsumersPool for memory
tracking and debugging.
+//!
+//! The TrackConsumersPool provides enhanced error messages that show the top
memory consumers
+//! when memory allocation fails, making it easier to debug memory issues in
DataFusion queries.
+//!
+//! # Examples
+//!
+//! * [`automatic_usage_example`]: Shows how to use RuntimeEnvBuilder to
automatically enable memory tracking
+
+use datafusion::execution::runtime_env::RuntimeEnvBuilder;
+use datafusion::prelude::*;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+ println!("=== DataFusion Memory Pool Tracking Example ===\n");
+
+ // Example 1: Automatic Usage with RuntimeEnvBuilder
+ automatic_usage_example().await?;
+
+ Ok(())
+}
+
+/// Example 1: Automatic Usage with RuntimeEnvBuilder
+///
+/// This shows the recommended way to use TrackConsumersPool through
RuntimeEnvBuilder,
+/// which automatically creates a TrackConsumersPool with sensible defaults.
+async fn automatic_usage_example() -> datafusion::error::Result<()> {
+ println!("Example 1: Automatic Usage with RuntimeEnvBuilder");
+ println!("------------------------------------------------");
+
+ // Success case: Create a runtime with reasonable memory limit
+ println!("Success case: Normal operation with sufficient memory");
+ let runtime = RuntimeEnvBuilder::new()
+ .with_memory_limit(5_000_000, 1.0) // 5MB, 100% utilization
+ .build_arc()?;
+
+ let config = SessionConfig::new();
+ let ctx = SessionContext::new_with_config_rt(config, runtime);
+
+ // Create a simple table for demonstration
+ ctx.sql("CREATE TABLE test AS VALUES (1, 'a'), (2, 'b'), (3, 'c')")
+ .await?
+ .collect()
+ .await?;
+
+ println!("✓ Created table with memory tracking enabled");
+
+ // Run a simple query to show it works
+ let results = ctx.sql("SELECT * FROM test").await?.collect().await?;
+ println!(
+ "✓ Query executed successfully. Found {} rows",
+ results.len()
+ );
+
+ println!("\n{}", "-".repeat(50));
+
+ // Error case: Create a runtime with low memory limit to trigger errors
+ println!("Error case: Triggering memory limit error with detailed error
messages");
+
+ // Use a WITH query that generates data and then processes it to trigger
memory usage
+ match ctx.sql("
+ WITH large_dataset AS (
+ SELECT
+ column1 as id,
+ column1 * 2 as doubled,
+ repeat('data_', 20) || column1 as text_field,
+ column1 * column1 as squared
+ FROM generate_series(1, 2000) as t(column1)
+ ),
+ aggregated AS (
+ SELECT
+ id,
+ doubled,
+ text_field,
+ squared,
+ sum(doubled) OVER (ORDER BY id ROWS BETWEEN 100 PRECEDING AND
CURRENT ROW) as running_sum
+ FROM large_dataset
+ )
+ SELECT
+ a1.id,
+ a1.text_field,
+ a2.text_field as text_field2,
+ a1.running_sum + a2.running_sum as combined_sum
+ FROM aggregated a1
+ JOIN aggregated a2 ON a1.id = a2.id - 1
+ ORDER BY a1.id
+ ").await?.collect().await {
+ Ok(results) => panic!("Should not succeed! Yet got {} batches",
results.len()),
+ Err(e) => {
+ println!("✓ Expected memory limit error during data processing:");
+ println!("Error: {e}");
+ /* Example error message:
+ Error: Not enough memory to continue external sort. Consider
increasing the memory limit, or decreasing sort_spill_reservation_bytes
+ caused by
+ Resources exhausted: Additional allocation failed with top
memory consumers (across reservations) as:
+ ExternalSorterMerge[3]#112(can spill: false) consumed 10.0
MB,
+ ExternalSorterMerge[10]#147(can spill: false) consumed
10.0 MB,
+ ExternalSorter[1]#93(can spill: true) consumed 69.0 KB,
+ ExternalSorter[13]#155(can spill: true) consumed 67.6 KB,
+ ExternalSorter[8]#140(can spill: true) consumed 67.2 KB.
+ Error: Failed to allocate additional 10.0 MB for
ExternalSorterMerge[0] with 0.0 B already allocated for this reservation - 7.1
MB remain available for the total pool
+ */
+ }
+ }
+
+ println!("\nNote: The error message above shows which memory consumers");
+ println!("were using the most memory when the limit was exceeded.");
+
+ Ok(())
+}
diff --git a/datafusion/execution/src/memory_pool/pool.rs
b/datafusion/execution/src/memory_pool/pool.rs
index 11467f69be..8ad61ba9f8 100644
--- a/datafusion/execution/src/memory_pool/pool.rs
+++ b/datafusion/execution/src/memory_pool/pool.rs
@@ -295,9 +295,25 @@ impl TrackedConsumer {
///
/// By tracking memory reservations more carefully this pool
/// can provide better error messages on the largest memory users
+/// when memory allocation fails.
///
/// Tracking is per hashed [`MemoryConsumer`], not per [`MemoryReservation`].
/// The same consumer can have multiple reservations.
+///
+/// # Automatic Usage via [`RuntimeEnvBuilder`]
+///
+/// The easiest way to use `TrackConsumersPool` is via
+/// [`RuntimeEnvBuilder::with_memory_limit()`].
+///
+/// [`RuntimeEnvBuilder`]: crate::runtime_env::RuntimeEnvBuilder
+/// [`RuntimeEnvBuilder::with_memory_limit()`]:
crate::runtime_env::RuntimeEnvBuilder::with_memory_limit
+///
+/// # Usage Examples
+///
+/// For more examples of using `TrackConsumersPool`, see the
[memory_pool_tracking.rs] example
+///
+/// [memory_pool_tracking.rs]:
https://github.com/apache/datafusion/blob/main/datafusion-examples/examples/memory_pool_tracking.rs
+/// [memory_pool_execution_plan.rs]:
https://github.com/apache/datafusion/blob/main/datafusion-examples/examples/memory_pool_execution_plan.rs
#[derive(Debug)]
pub struct TrackConsumersPool<I> {
/// The wrapped memory pool that actually handles reservation logic
@@ -311,6 +327,36 @@ pub struct TrackConsumersPool<I> {
impl<I: MemoryPool> TrackConsumersPool<I> {
/// Creates a new [`TrackConsumersPool`].
///
+ /// # Arguments
+ /// * `inner` - The underlying memory pool that handles actual memory
allocation
+ /// * `top` - The number of top memory consumers to include in error
messages
+ ///
+ /// # Note
+ /// In most cases, you should use
[`RuntimeEnvBuilder::with_memory_limit()`](crate::runtime_env::RuntimeEnvBuilder::with_memory_limit)
+ /// instead of creating this pool manually, as it automatically sets up
tracking with
+ /// sensible defaults (top 5 consumers).
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use std::num::NonZeroUsize;
+ /// use datafusion_execution::memory_pool::{TrackConsumersPool,
GreedyMemoryPool, FairSpillPool};
+ ///
+ /// // Create with a greedy pool backend, reporting top 3 consumers in
error messages
+ /// let tracked_greedy = TrackConsumersPool::new(
+ /// GreedyMemoryPool::new(1024 * 1024), // 1MB limit
+ /// NonZeroUsize::new(3).unwrap(),
+ /// );
+ ///
+ /// // Create with a fair spill pool backend, reporting top 5 consumers in
error messages
+ /// let tracked_fair = TrackConsumersPool::new(
+ /// FairSpillPool::new(2 * 1024 * 1024), // 2MB limit
+ /// NonZeroUsize::new(5).unwrap(),
+ /// );
+ /// ```
+ ///
+ /// # Impact on Error Messages
+ ///
/// The `top` determines how many Top K [`MemoryConsumer`]s to include
/// in the reported [`DataFusionError::ResourcesExhausted`].
pub fn new(inner: I, top: NonZeroUsize) -> Self {
@@ -321,7 +367,7 @@ impl<I: MemoryPool> TrackConsumersPool<I> {
}
}
- /// The top consumers in a report string.
+ /// Returns a formatted string with the top memory consumers.
pub fn report_top(&self, top: usize) -> String {
let mut consumers = self
.tracked_consumers
diff --git a/datafusion/execution/src/runtime_env.rs
b/datafusion/execution/src/runtime_env.rs
index 9c5de42bcd..db045a8b7e 100644
--- a/datafusion/execution/src/runtime_env.rs
+++ b/datafusion/execution/src/runtime_env.rs
@@ -258,7 +258,8 @@ impl RuntimeEnvBuilder {
/// Specify the total memory to use while running the DataFusion
/// plan to `max_memory * memory_fraction` in bytes.
///
- /// This defaults to using [`GreedyMemoryPool`]
+ /// This defaults to using [`GreedyMemoryPool`] wrapped in the
+ /// [`TrackConsumersPool`] with a maximum of 5 consumers.
///
/// Note DataFusion does not yet respect this limit in all cases.
pub fn with_memory_limit(self, max_memory: usize, memory_fraction: f64) ->
Self {
diff --git a/datafusion/physical-plan/src/execution_plan.rs
b/datafusion/physical-plan/src/execution_plan.rs
index d4e0fe82bd..e708bbd3ec 100644
--- a/datafusion/physical-plan/src/execution_plan.rs
+++ b/datafusion/physical-plan/src/execution_plan.rs
@@ -74,6 +74,15 @@ use futures::stream::{StreamExt, TryStreamExt};
/// [`execute`]: ExecutionPlan::execute
/// [`required_input_distribution`]: ExecutionPlan::required_input_distribution
/// [`required_input_ordering`]: ExecutionPlan::required_input_ordering
+///
+/// # Examples
+///
+/// See [`datafusion-examples`] for examples, including
+/// [`memory_pool_execution_plan.rs`] which shows how to implement a custom
+/// `ExecutionPlan` with memory tracking and spilling support.
+///
+/// [`datafusion-examples`]:
https://github.com/apache/datafusion/tree/main/datafusion-examples
+/// [`memory_pool_execution_plan.rs`]:
https://github.com/apache/datafusion/blob/main/datafusion-examples/examples/memory_pool_execution_plan.rs
pub trait ExecutionPlan: Debug + DisplayAs + Send + Sync {
/// Short name for the ExecutionPlan, such as 'DataSourceExec'.
///
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