feat: Add additional metrics for shuffle write

common/src/main/scala/org/apache/comet/CometConf.scala

@@ -45,6 +45,9 @@ import org.apache.comet.shims.ShimCometConf
  */
 object CometConf extends ShimCometConf {
 
+  private val METRICS_GUIDE = "For more information, refer to the Comet Metrics " +
+    "Guide (https://datafusion.apache.org/comet/user-guide/metrics.html)"
+
   private val TUNING_GUIDE = "For more information, refer to the Comet Tuning " +
     "Guide (https://datafusion.apache.org/comet/user-guide/tuning.html)"
 
@@ -414,6 +417,12 @@ object CometConf extends ShimCometConf {
       .booleanConf
       .createWithDefault(false)
 
+  val COMET_ENABLE_DETAILED_METRICS: ConfigEntry[Boolean] =
+    conf("spark.comet.metrics.detailed")
+      .doc(s"Enable this option to see additional SQL metrics. $METRICS_GUIDE.")
+      .booleanConf
+      .createWithDefault(false)
+
   val COMET_EXPLAIN_FALLBACK_ENABLED: ConfigEntry[Boolean] =
     conf("spark.comet.explainFallback.enabled")
       .doc(

docs/source/index.rst

@@ -51,6 +51,7 @@ as a native runtime to achieve improvement in terms of query efficiency and quer
    Configuration Settings <user-guide/configs>
    Compatibility Guide <user-guide/compatibility>
    Tuning Guide <user-guide/tuning>
+   Metrics Guide <user-guide/metrics>
 
 .. _toc.contributor-guide-links:
 .. toctree::

docs/source/user-guide/configs.md

@@ -64,6 +64,7 @@ Comet provides the following configuration settings.
 | spark.comet.explainFallback.enabled | When this setting is enabled, Comet will provide logging explaining the reason(s) why a query stage cannot be executed natively. Set this to false to reduce the amount of logging. | false |
 | spark.comet.memory.overhead.factor | Fraction of executor memory to be allocated as additional non-heap memory per executor process for Comet. | 0.2 |
 | spark.comet.memory.overhead.min | Minimum amount of additional memory to be allocated per executor process for Comet, in MiB. | 402653184b |
+| spark.comet.metrics.detailed | Enable this option to see additional SQL metrics. For more information, refer to the Comet Metrics Guide (https://datafusion.apache.org/comet/user-guide/metrics.html). | false |
 | spark.comet.nativeLoadRequired | Whether to require Comet native library to load successfully when Comet is enabled. If not, Comet will silently fallback to Spark when it fails to load the native lib. Otherwise, an error will be thrown and the Spark job will be aborted. | false |
 | spark.comet.parquet.enable.directBuffer | Whether to use Java direct byte buffer when reading Parquet. | false |
 | spark.comet.parquet.read.io.adjust.readRange.skew | In the parallel reader, if the read ranges submitted are skewed in sizes, this option will cause the reader to break up larger read ranges into smaller ranges to reduce the skew. This will result in a slightly larger number of connections opened to the file system but may give improved performance. | false |

docs/source/user-guide/metrics.md

@@ -0,0 +1,64 @@
+<!---
+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.
+-->
+
+# Comet Metrics
+
+## Spark SQL Metrics
+
+Set `spark.comet.metrics.detailed=true` to see all available Comet metrics.
+
+### CometScanExec
+
+| Metric      | Description                                                                                                                                                                                                                                                                        |
+| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `scan time` | Total time to scan a Parquet file. This is not comparable to the same metric in Spark because Comet's scan metric is more accurate. Although both Comet and Spark measure the time in nanoseconds, Spark rounds this time to the nearest millisecond per batch and Comet does not. |
+
+### Exchange
+
+Comet adds some additional metrics:
+
+| Metric                          | Description                                                                               |
+| ------------------------------- | ----------------------------------------------------------------------------------------- |
+| `ipc time`                      | Time to encode batches in IPC format. Includes compression time.                          |
+| `native shuffle time`           | Total time spent in native shuffle writer, excluding the execution time of the input plan |
+| `native shuffle input time`     | Time spend executing the shuffle input plan and fetching batches.                         |
+| `shuffle wall time (inclusive)` | Total time executing the shuffle write, inclusive of executing the input plan.            |
+
+## Native Metrics
+
+Setting `spark.comet.explain.native.enabled=true` will cause native plans to be logged in each executor. Metrics are
+logged for each native plan (and there is one plan per task, so this is very verbose).
+
+Here is a guide to some of the native metrics.
+
+### ScanExec
+
+| Metric            | Description                                                                                         |
+| ----------------- | --------------------------------------------------------------------------------------------------- |
+| `elapsed_compute` | Total time spent in this operator, fetching batches from a JVM iterator.                            |
+| `jvm_fetch_time`  | Time spent in the JVM fetching input batches to be read by this `ScanExec` instance.                |
+| `arrow_ffi_time`  | Time spent using Arrow FFI to create Arrow batches from the memory addresses returned from the JVM. |
+
+### ShuffleWriterExec
+
+| Metric            | Description                                           |
+| ----------------- | ----------------------------------------------------- |
+| `elapsed_compute` | Total time excluding any child operators.             |
+| `input_time`      | Time spent executing input plan and fetching batches. |
+| `write_time`      | Time spent writing bytes to disk.                     |

docs/source/user-guide/tuning.md

@@ -102,28 +102,3 @@ native shuffle currently only supports `HashPartitioning` and `SinglePartitionin
 
 To enable native shuffle, set `spark.comet.exec.shuffle.mode` to `native`. If this mode is explicitly set,
 then any shuffle operations that cannot be supported in this mode will fall back to Spark.
-
-##  Metrics
-
-### Spark SQL Metrics
-
-Some Comet metrics are not directly comparable to Spark metrics in some cases:
-
-- `CometScanExec` uses nanoseconds for total scan time. Spark also measures scan time in nanoseconds but converts to
-  milliseconds _per batch_ which can result in a large loss of precision, making it difficult to compare scan times
-  between Spark and Comet.
-
-### Native Metrics
-
-Setting `spark.comet.explain.native.enabled=true` will cause native plans to be logged in each executor. Metrics are
-logged for each native plan (and there is one plan per task, so this is very verbose).
-
-Here is a guide to some of the native metrics.
-
-### ScanExec
-
-| Metric            | Description                                                                                         |
-| ----------------- | --------------------------------------------------------------------------------------------------- |
-| `elapsed_compute` | Total time spent in this operator, fetching batches from a JVM iterator.                            |
-| `jvm_fetch_time`  | Time spent in the JVM fetching input batches to be read by this `ScanExec` instance.                |
-| `arrow_ffi_time`  | Time spent using Arrow FFI to create Arrow batches from the memory addresses returned from the JVM. |

native/core/benches/row_columnar.rs

@@ -20,6 +20,7 @@ use comet::execution::shuffle::row::{
     process_sorted_row_partition, SparkUnsafeObject, SparkUnsafeRow,
 };
 use criterion::{criterion_group, criterion_main, Criterion};
+use datafusion::physical_plan::metrics::Time;
 use tempfile::Builder;
 
 const NUM_ROWS: usize = 10000;
@@ -63,6 +64,8 @@ fn benchmark(c: &mut Criterion) {
         let row_size_ptr = row_sizes.as_mut_ptr();
         let schema = vec![ArrowDataType::Int64; NUM_COLS];
 
+        let ipc_time = Time::default();
+
         b.iter(|| {
             let tempfile = Builder::new().tempfile().unwrap();
 
@@ -77,6 +80,7 @@ fn benchmark(c: &mut Criterion) {
                 false,
                 0,
                 None,
+                &ipc_time,
             )
             .unwrap();
         });

native/core/src/execution/jni_api.rs

@@ -17,8 +17,10 @@
 
 //! Define JNI APIs which can be called from Java/Scala.
 
+use super::{serde, utils::SparkArrowConvert, CometMemoryPool};
 use arrow::datatypes::DataType as ArrowDataType;
 use arrow_array::RecordBatch;
+use datafusion::physical_plan::metrics::Time;
 use datafusion::{
     execution::{
         disk_manager::DiskManagerConfig,
@@ -40,8 +42,6 @@ use jni::{
 use std::time::{Duration, Instant};
 use std::{collections::HashMap, sync::Arc, task::Poll};
 
-use super::{serde, utils::SparkArrowConvert, CometMemoryPool};
-
 use crate::{
     errors::{try_unwrap_or_throw, CometError, CometResult},
     execution::{
@@ -455,6 +455,7 @@ pub unsafe extern "system" fn Java_org_apache_comet_Native_writeSortedFileNative
     checksum_enabled: jboolean,
     checksum_algo: jint,
     current_checksum: jlong,
+    ipc_time: &Time,
 ) -> jlongArray {
     try_unwrap_or_throw(&e, |mut env| unsafe {
         let data_types = convert_datatype_arrays(&mut env, serialized_datatypes)?;
@@ -493,6 +494,7 @@ pub unsafe extern "system" fn Java_org_apache_comet_Native_writeSortedFileNative
             checksum_enabled,
             checksum_algo,
             current_checksum,
+            ipc_time,
         )?;
 
         let checksum = if let Some(checksum) = checksum {

native/core/src/execution/operators/scan.rs

@@ -75,7 +75,7 @@ pub struct ScanExec {
     /// Metrics collector
     metrics: ExecutionPlanMetricsSet,
     /// Baseline metrics
-    baseline_metrics: BaselineMetrics,
+    pub(crate) baseline_metrics: BaselineMetrics,
     /// Time waiting for JVM input plan to execute and return batches
     jvm_fetch_time: Time,
     /// Time spent in FFI

native/core/src/execution/shuffle/row.rs

@@ -40,6 +40,7 @@ use arrow_array::{
     Array, ArrayRef, RecordBatch, RecordBatchOptions,
 };
 use arrow_schema::{ArrowError, DataType, Field, Schema, TimeUnit};
+use datafusion::physical_plan::metrics::Time;
 use jni::sys::{jint, jlong};
 use std::{
     fs::OpenOptions,
@@ -3295,6 +3296,7 @@ pub fn process_sorted_row_partition(
     // this is the initial checksum for this method, as it also gets updated iteratively
     // inside the loop within the method across batches.
     initial_checksum: Option<u32>,
+    ipc_time: &Time,
 ) -> Result<(i64, Option<u32>), CometError> {
     // TODO: We can tune this parameter automatically based on row size and cache size.
     let row_step = 10;
@@ -3354,7 +3356,7 @@ pub fn process_sorted_row_partition(
         let mut frozen: Vec<u8> = vec![];
         let mut cursor = Cursor::new(&mut frozen);
         cursor.seek(SeekFrom::End(0))?;
-        written += write_ipc_compressed(&batch, &mut cursor)?;
+        written += write_ipc_compressed(&batch, &mut cursor, ipc_time)?;
 
         if let Some(checksum) = &mut current_checksum {
             checksum.update(&mut cursor)?;