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2 changes: 2 additions & 0 deletions native/core/src/execution/operators/mod.rs
Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,9 @@ pub use parquet_writer::ParquetWriterExec;
mod csv_scan;
pub mod projection;
mod scan;
mod scan_memory_log;
pub use csv_scan::init_csv_datasource_exec;
pub use scan_memory_log::ScanMemoryLogExec;

/// Error returned during executing operators.
#[derive(thiserror::Error, Debug)]
Expand Down
292 changes: 292 additions & 0 deletions native/core/src/execution/operators/scan_memory_log.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,292 @@
// 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.

//! A transparent wrapper around a scan ExecutionPlan that tracks per-partition
//! memory allocation using jemalloc's per-thread counters.
//!
//! Uses `thread.allocatedp` / `thread.deallocatedp` which are thread-local
//! monotonic counters. By measuring deltas around each `poll_next()` call,
//! we get accurate per-partition allocation/deallocation numbers even with
//! concurrent partitions on different threads.

use arrow::array::RecordBatch;
use arrow::datatypes::SchemaRef;
use datafusion::common::Result as DataFusionResult;
use datafusion::execution::TaskContext;
use datafusion::physical_expr::EquivalenceProperties;
use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
use datafusion::physical_plan::metrics::{BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet};
use datafusion::physical_plan::{
DisplayAs, DisplayFormatType, ExecutionPlan, ExecutionPlanProperties, PlanProperties,
RecordBatchStream, SendableRecordBatchStream,
};
use futures::Stream;
use std::any::Any;
use std::pin::Pin;
use std::sync::atomic::{AtomicU64, Ordering};
use std::sync::Arc;
use std::task::{Context, Poll};

/// Global counters to aggregate stats across all partitions.
static AGG_THREAD_ALLOCATED: AtomicU64 = AtomicU64::new(0);
static AGG_THREAD_DEALLOCATED: AtomicU64 = AtomicU64::new(0);
static AGG_PEAK_PROCESS_ALLOCATED: AtomicU64 = AtomicU64::new(0);
static AGG_COMPLETED_PARTITIONS: AtomicU64 = AtomicU64::new(0);
static AGG_TOTAL_ROWS: AtomicU64 = AtomicU64::new(0);

/// Per-thread allocated/deallocated from jemalloc.
/// These are monotonically increasing counters scoped to the calling thread.
#[cfg(feature = "jemalloc")]
fn jemalloc_thread_stats() -> (u64, u64) {
use tikv_jemalloc_ctl::thread;
let allocated = thread::allocatedp::read().map(|p| p.get()).unwrap_or(0);
let deallocated = thread::deallocatedp::read().map(|p| p.get()).unwrap_or(0);
(allocated, deallocated)
}

#[cfg(not(feature = "jemalloc"))]
fn jemalloc_thread_stats() -> (u64, u64) {
(0, 0)
}

/// Process-wide jemalloc allocated bytes.
#[cfg(feature = "jemalloc")]
fn jemalloc_process_allocated() -> u64 {
use tikv_jemalloc_ctl::{epoch, stats};
epoch::advance().ok();
stats::allocated::read().unwrap_or(0) as u64
}

#[cfg(not(feature = "jemalloc"))]
fn jemalloc_process_allocated() -> u64 {
0
}

fn update_atomic_max(atomic: &AtomicU64, value: u64) {
let mut current = atomic.load(Ordering::Relaxed);
while value > current {
match atomic.compare_exchange_weak(current, value, Ordering::Relaxed, Ordering::Relaxed) {
Ok(_) => break,
Err(actual) => current = actual,
}
}
}

/// Wraps a child ExecutionPlan and tracks memory allocated/deallocated
/// during scan execution using jemalloc per-thread counters.
#[derive(Debug)]
pub struct ScanMemoryLogExec {
child: Arc<dyn ExecutionPlan>,
spark_partition: i32,
cache: PlanProperties,
metrics: ExecutionPlanMetricsSet,
}

impl ScanMemoryLogExec {
pub fn new(child: Arc<dyn ExecutionPlan>, spark_partition: i32) -> Self {
let cache = PlanProperties::new(
EquivalenceProperties::new(child.schema()),
child.output_partitioning().clone(),
EmissionType::Final,
Boundedness::Bounded,
);
Self {
child,
spark_partition,
cache,
metrics: ExecutionPlanMetricsSet::default(),
}
}
}

impl DisplayAs for ScanMemoryLogExec {
fn fmt_as(&self, t: DisplayFormatType, f: &mut std::fmt::Formatter) -> std::fmt::Result {
match t {
DisplayFormatType::Default | DisplayFormatType::Verbose => {
write!(
f,
"ScanMemoryLogExec: spark_partition={}",
self.spark_partition
)
}
DisplayFormatType::TreeRender => unimplemented!(),
}
}
}

impl ExecutionPlan for ScanMemoryLogExec {
fn as_any(&self) -> &dyn Any {
self
}

fn schema(&self) -> SchemaRef {
self.child.schema()
}

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

fn with_new_children(
self: Arc<Self>,
mut children: Vec<Arc<dyn ExecutionPlan>>,
) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
Ok(Arc::new(ScanMemoryLogExec::new(
children.remove(0),
self.spark_partition,
)))
}

fn execute(
&self,
partition: usize,
context: Arc<TaskContext>,
) -> DataFusionResult<SendableRecordBatchStream> {
// Capture thread-local counters before execute()
let (thread_alloc_before, thread_dealloc_before) = jemalloc_thread_stats();

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

// Capture after execute() to measure setup cost
let (thread_alloc_after, thread_dealloc_after) = jemalloc_thread_stats();
let execute_allocated = thread_alloc_after - thread_alloc_before;
let execute_deallocated = thread_dealloc_after - thread_dealloc_before;

log::info!(
"ScanMemoryLogExec spark_partition={}: execute() setup: \
thread_allocated={}, thread_deallocated={}, net={}",
self.spark_partition,
execute_allocated,
execute_deallocated,
execute_allocated as i64 - execute_deallocated as i64,
);

let baseline_metrics = BaselineMetrics::new(&self.metrics, partition);
Ok(Box::pin(ScanMemoryLogStream {
child: child_stream,
context,
spark_partition: self.spark_partition,
logged: false,
baseline_metrics,
// Accumulate total thread-level alloc/dealloc across all polls
thread_total_allocated: execute_allocated,
thread_total_deallocated: execute_deallocated,
batch_count: 0,
total_rows: 0,
}))
}

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

fn name(&self) -> &str {
"ScanMemoryLogExec"
}

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

struct ScanMemoryLogStream {
child: SendableRecordBatchStream,
context: Arc<TaskContext>,
spark_partition: i32,
logged: bool,
baseline_metrics: BaselineMetrics,
/// Accumulated bytes allocated on the thread during this scan
thread_total_allocated: u64,
/// Accumulated bytes deallocated on the thread during this scan
thread_total_deallocated: u64,
batch_count: usize,
total_rows: usize,
}

impl Stream for ScanMemoryLogStream {
type Item = DataFusionResult<RecordBatch>;

fn poll_next(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Option<Self::Item>> {
// Snapshot thread counters before polling child
let (alloc_before, dealloc_before) = jemalloc_thread_stats();

let result = Pin::new(&mut self.child).poll_next(cx);

// Snapshot after — delta is what this poll allocated/freed
let (alloc_after, dealloc_after) = jemalloc_thread_stats();
self.thread_total_allocated += alloc_after - alloc_before;
self.thread_total_deallocated += dealloc_after - dealloc_before;

match &result {
Poll::Ready(Some(Ok(batch))) => {
self.batch_count += 1;
self.total_rows += batch.num_rows();
self.baseline_metrics.record_output(batch.num_rows());
}
Poll::Ready(None) if !self.logged => {
self.logged = true;
let pool = self.context.memory_pool();
let process_allocated = jemalloc_process_allocated();
let net = self.thread_total_allocated as i64
- self.thread_total_deallocated as i64;

// Accumulate into global aggregates
AGG_THREAD_ALLOCATED
.fetch_add(self.thread_total_allocated, Ordering::Relaxed);
AGG_THREAD_DEALLOCATED
.fetch_add(self.thread_total_deallocated, Ordering::Relaxed);
update_atomic_max(&AGG_PEAK_PROCESS_ALLOCATED, process_allocated);
AGG_TOTAL_ROWS.fetch_add(self.total_rows as u64, Ordering::Relaxed);
let completed =
AGG_COMPLETED_PARTITIONS.fetch_add(1, Ordering::Relaxed) + 1;

log::info!(
"ScanMemoryLogExec spark_partition={}: scan complete, \
batches={}, rows={}, \
memory_pool_reserved={}, \
thread: allocated={}, deallocated={}, net={}, \
process_allocated={} | \
aggregate(n={}): thread_allocated={}, thread_deallocated={}, \
thread_net={}, max_process_allocated={}, total_rows={}",
self.spark_partition,
self.batch_count,
self.total_rows,
pool.reserved(),
self.thread_total_allocated,
self.thread_total_deallocated,
net,
process_allocated,
completed,
AGG_THREAD_ALLOCATED.load(Ordering::Relaxed),
AGG_THREAD_DEALLOCATED.load(Ordering::Relaxed),
AGG_THREAD_ALLOCATED.load(Ordering::Relaxed) as i64
- AGG_THREAD_DEALLOCATED.load(Ordering::Relaxed) as i64,
AGG_PEAK_PROCESS_ALLOCATED.load(Ordering::Relaxed),
AGG_TOTAL_ROWS.load(Ordering::Relaxed),
);
}
_ => {}
}
result
}
}

impl RecordBatchStream for ScanMemoryLogStream {
fn schema(&self) -> SchemaRef {
self.child.schema()
}
}
3 changes: 3 additions & 0 deletions native/core/src/execution/planner.rs
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@ pub mod operator_registry;

use crate::execution::operators::init_csv_datasource_exec;
use crate::execution::operators::IcebergScanExec;
use crate::execution::operators::ScanMemoryLogExec;
use crate::{
errors::ExpressionError,
execution::{
Expand Down Expand Up @@ -1185,6 +1186,8 @@ impl PhysicalPlanner {
self.session_ctx(),
common.encryption_enabled,
)?;
let scan: Arc<dyn ExecutionPlan> =
Arc::new(ScanMemoryLogExec::new(scan, self.partition));
Ok((
vec![],
Arc::new(SparkPlan::new(spark_plan.plan_id, scan, vec![])),
Expand Down
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