Add support for Photon-specific SQL Metrics

core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSparkMetricsAnalyzer.scala

@@ -16,12 +16,15 @@
 
 package com.nvidia.spark.rapids.tool.analysis
 
+import java.util.concurrent.TimeUnit
+
 import scala.collection.mutable
 
+import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper
 import com.nvidia.spark.rapids.tool.profiling.{IOAnalysisProfileResult, JobAggTaskMetricsProfileResult, ShuffleSkewProfileResult, SQLDurationExecutorTimeProfileResult, SQLMaxTaskInputSizes, SQLTaskAggMetricsProfileResult, StageAggTaskMetricsProfileResult}
 
 import org.apache.spark.sql.rapids.tool.{AppBase, ToolUtils}
-import org.apache.spark.sql.rapids.tool.store.TaskModel
+import org.apache.spark.sql.rapids.tool.store.{AccumInfo, TaskModel}
 
 /**
  * Does analysis on the DataFrames from object of AppBase.
@@ -324,12 +327,62 @@ class AppSparkMetricsAnalyzer(app: AppBase) extends AppAnalysisBase(app) {
    */
   private def aggregateSparkMetricsByStageInternal(index: Int): Unit = {
     // TODO: this has stage attempts. we should handle different attempts
+
+    // For Photon apps, peak memory and shuffle write time need to be calculated from accumulators
+    // instead of task metrics.
+    // Approach:
+    //   1. Collect accumulators for each metric type.
+    //   2. For each stage, retrieve the relevant accumulators and calculate aggregated values.
+    // Note:
+    //  - A HashMap could be used instead of separate mutable.ArrayBuffer for each metric type,
+    //    but avoiding it for readability.
+    val photonPeakMemoryAccumInfos = mutable.ArrayBuffer[AccumInfo]()
+    val photonShuffleWriteTimeAccumInfos = mutable.ArrayBuffer[AccumInfo]()
+
+    if (app.isPhoton) {
+      app.accumManager.applyToAccumInfoMap { accumInfo =>
+        accumInfo.infoRef.name.value match {
+          case name if name.contains(
+            DatabricksParseHelper.PHOTON_METRIC_PEAK_MEMORY_LABEL) =>
+              // Collect accumulators for peak memory
+              photonPeakMemoryAccumInfos += accumInfo
+          case name if name.contains(
+            DatabricksParseHelper.PHOTON_METRIC_SHUFFLE_WRITE_TIME_LABEL) =>
+              // Collect accumulators for shuffle write time
+              photonShuffleWriteTimeAccumInfos += accumInfo
+          case _ => // Ignore other accumulators
+        }
+      }
+    }
+
     app.stageManager.getAllStages.foreach { sm =>
       // TODO: Should we only consider successful tasks?
       val tasksInStage = app.taskManager.getTasks(sm.stageInfo.stageId,
         sm.stageInfo.attemptNumber())
       // count duplicate task attempts
       val numAttempts = tasksInStage.size
+
+      val (peakMemoryMax, shuffleWriteTimeSum) = if (app.isPhoton) {
+        // For max peak memory, we need to look at the accumulators at the task level.
+        val peakMemoryValues = tasksInStage.flatMap { taskModel =>
+          photonPeakMemoryAccumInfos.flatMap { accumInfo =>
+            accumInfo.taskUpdatesMap.get(taskModel.taskId)
+          }
+        }
+        // For sum of shuffle write time, we need to look at the accumulators at the stage level.
+        val shuffleWriteValues = photonShuffleWriteTimeAccumInfos.flatMap { accumInfo =>
+          accumInfo.stageValuesMap.get(sm.stageInfo.stageId)
+        }
+        (AppSparkMetricsAnalyzer.maxWithEmptyHandling(peakMemoryValues),
+          TimeUnit.NANOSECONDS.toMillis(shuffleWriteValues.sum))
+      } else {
+        // For non-Photon apps, use the task metrics directly.
+        val peakMemoryValues = tasksInStage.map(_.peakExecutionMemory)
+        val shuffleWriteTime = tasksInStage.map(_.sw_writeTime)
+        (AppSparkMetricsAnalyzer.maxWithEmptyHandling(peakMemoryValues),
+          shuffleWriteTime.sum)
+      }
+
       val (durSum, durMax, durMin, durAvg) = AppSparkMetricsAnalyzer.getDurations(tasksInStage)
       val stageRow = StageAggTaskMetricsProfileResult(index,
         sm.stageInfo.stageId,
@@ -350,7 +403,7 @@ class AppSparkMetricsAnalyzer(app: AppBase) extends AppAnalysisBase(app) {
         tasksInStage.map(_.memoryBytesSpilled).sum,
         tasksInStage.map(_.output_bytesWritten).sum,
         tasksInStage.map(_.output_recordsWritten).sum,
-        AppSparkMetricsAnalyzer.maxWithEmptyHandling(tasksInStage.map(_.peakExecutionMemory)),
+        peakMemoryMax,
         tasksInStage.map(_.resultSerializationTime).sum,
         AppSparkMetricsAnalyzer.maxWithEmptyHandling(tasksInStage.map(_.resultSize)),
         tasksInStage.map(_.sr_fetchWaitTime).sum,
@@ -362,7 +415,7 @@ class AppSparkMetricsAnalyzer(app: AppBase) extends AppAnalysisBase(app) {
         tasksInStage.map(_.sr_totalBytesRead).sum,
         tasksInStage.map(_.sw_bytesWritten).sum,
         tasksInStage.map(_.sw_recordsWritten).sum,
-        tasksInStage.map(_.sw_writeTime).sum
+        shuffleWriteTimeSum
       )
       stageLevelSparkMetrics(index).put(sm.stageInfo.stageId, stageRow)
     }

core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala

@@ -17,15 +17,19 @@
 package com.nvidia.spark.rapids.tool.planparser
 
 import java.nio.file.Paths
+import java.util.concurrent.TimeUnit
 
 import scala.util.control.NonFatal
 import scala.util.matching.Regex
 
+import com.nvidia.spark.rapids.tool.profiling.SQLAccumProfileResults
+import com.nvidia.spark.rapids.tool.views.IoMetrics
 import org.json4s.{DefaultFormats, Formats}
 import org.json4s.jackson.JsonMethods
 import org.json4s.jackson.JsonMethods.parse
 
 import org.apache.spark.internal.Logging
+import org.apache.spark.sql.rapids.tool.UnsupportedMetricNameException
 import org.apache.spark.sql.rapids.tool.util.UTF8Source
 
 // Utilities used to handle Databricks and Photon Ops
@@ -48,6 +52,13 @@ object DatabricksParseHelper extends Logging {
   val SUB_PROP_JOB_ID = "JobId"
   val SUB_PROP_RUN_NAME = "RunName"
 
+  // scalastyle:off
+  // Photon metric labels that are used as alternatives to Spark metric labels
+  val PHOTON_METRIC_CUMULATIVE_TIME_LABEL = "cumulative time"               // Alternative for "scan time"
+  val PHOTON_METRIC_PEAK_MEMORY_LABEL = "peak memory usage"                 // Alternative for "peak execution memory"
+  val PHOTON_METRIC_SHUFFLE_WRITE_TIME_LABEL = "part of shuffle file write" // Alternative for "shuffle write time"
+  // scalastyle:on
+
   private val PHOTON_PATTERN: Regex = "Photon[a-zA-Z]*".r
   private val PHOTON_OPS_MAPPING_DIR = "photonOperatorMappings"
   // TODO: Create separate mapping file for different Photon/Databricks versions
@@ -147,4 +158,21 @@ object DatabricksParseHelper extends Logging {
   def mapPhotonToSpark(inputStr: String): String = {
     PHOTON_PATTERN.replaceAllIn(inputStr, m => photonToSparkMapping.getOrElse(m.matched, m.matched))
   }
+
+  /**
+   * Checks if 'accum' is a Photon I/O metric.
+   */
+  def isPhotonIoMetric(accum: SQLAccumProfileResults): Boolean =
+    accum.name == PHOTON_METRIC_CUMULATIVE_TIME_LABEL && accum.nodeName.contains("Scan")
+
+  /**
+   * Updates the I/O metrics for Photon apps based on the accumulator values.
+   */
+  def updatePhotonIoMetric(accum: SQLAccumProfileResults, ioMetrics: IoMetrics): Unit = {
+    accum.name match {
+      case PHOTON_METRIC_CUMULATIVE_TIME_LABEL if accum.nodeName.contains("Scan") =>
+        ioMetrics.scanTime = TimeUnit.NANOSECONDS.toMillis(accum.total)
+      case _ => throw UnsupportedMetricNameException(accum.name)
+    }
+  }
 }

core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala

@@ -19,6 +19,7 @@ package com.nvidia.spark.rapids.tool.views
 import scala.collection.Seq
 
 import com.nvidia.spark.rapids.tool.analysis.{ProfAppIndexMapperTrait, QualAppIndexMapperTrait}
+import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper
 import com.nvidia.spark.rapids.tool.profiling.{DataSourceProfileResult, SQLAccumProfileResults}
 import com.nvidia.spark.rapids.tool.qualification.QualSQLPlanAnalyzer
 
@@ -40,6 +41,12 @@ object IoMetrics {
   val DECODE_TIME_LABEL = "GPU decode time"
 
   val EMPTY_IO_METRICS: IoMetrics = IoMetrics(0, 0, 0, 0)
+
+  /**
+   * Get all labels for IoMetrics
+   */
+  def getAllLabels: Seq[String] = Seq(
+    BUFFER_TIME_LABEL, SCAN_TIME_LABEL, DATA_SIZE_LABEL, DECODE_TIME_LABEL)
 }
 
 trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] {
@@ -48,11 +55,13 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] {
   private def getIoMetrics(sqlAccums: Seq[SQLAccumProfileResults]): IoMetrics = {
     val finalRes = IoMetrics(0, 0, 0, 0)
     try {
-      sqlAccums.map(accum => accum.name match {
+      sqlAccums.foreach(accum => accum.name match {
         case IoMetrics.BUFFER_TIME_LABEL => finalRes.bufferTime = accum.total
         case IoMetrics.SCAN_TIME_LABEL => finalRes.scanTime = accum.total
         case IoMetrics.DATA_SIZE_LABEL => finalRes.dataSize = accum.total
         case IoMetrics.DECODE_TIME_LABEL => finalRes.decodeTime = accum.total
+        case _ if DatabricksParseHelper.isPhotonIoMetric(accum) =>
+          DatabricksParseHelper.updatePhotonIoMetric(accum, finalRes)
         case _ => throw UnsupportedMetricNameException(accum.name)
       })
     } catch {
@@ -98,11 +107,9 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] {
       index: Int,
       appSqlAccums: Seq[SQLAccumProfileResults]): Seq[DataSourceProfileResult] = {
     // Filter appSqlAccums to get only required metrics
-    val dataSourceMetrics = appSqlAccums.filter(
-      sqlAccum => sqlAccum.name.contains(IoMetrics.BUFFER_TIME_LABEL)
-        || sqlAccum.name.contains(IoMetrics.SCAN_TIME_LABEL)
-        || sqlAccum.name.contains(IoMetrics.DECODE_TIME_LABEL)
-        || sqlAccum.name.equals(IoMetrics.DATA_SIZE_LABEL))
+    val dataSourceMetrics = appSqlAccums.filter(sqlAccum =>
+      IoMetrics.getAllLabels.contains(sqlAccum.name) ||
+        app.isPhoton && DatabricksParseHelper.isPhotonIoMetric(sqlAccum))
 
     val dsFromLastPlan = app.dataSourceInfo.map { ds =>
       val sqlIdtoDs = dataSourceMetrics.filter(

core/src/main/scala/org/apache/spark/sql/rapids/tool/store/AccumManager.scala

@@ -66,4 +66,11 @@ class AccumManager {
   def getMaxStageValue(id: Long): Option[Long] = {
     accumInfoMap.get(id).map(_.getMaxStageValue.get)
   }
+
+  /**
+   * Applies the function `f` to each AccumInfo in the accumInfoMap.
+   */
+  def applyToAccumInfoMap(f: AccumInfo => Unit): Unit = {
+    accumInfoMap.values.foreach(f)
+  }
 }

core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_job_metrics_agg_expectation.csv

@@ -0,0 +1,58 @@
+appIndex,jobId,numTasks,Duration,diskBytesSpilled_sum,duration_sum,duration_max,duration_min,duration_avg,executorCPUTime_sum,executorDeserializeCPUTime_sum,executorDeserializeTime_sum,executorRunTime_sum,input_bytesRead_sum,input_recordsRead_sum,jvmGCTime_sum,memoryBytesSpilled_sum,output_bytesWritten_sum,output_recordsWritten_sum,peakExecutionMemory_max,resultSerializationTime_sum,resultSize_max,sr_fetchWaitTime_sum,sr_localBlocksFetched_sum,sr_localBytesRead_sum,sr_remoteBlocksFetched_sum,sr_remoteBytesRead_sum,sr_remoteBytesReadToDisk_sum,sr_totalBytesRead_sum,sw_bytesWritten_sum,sw_recordsWritten_sum,sw_writeTime_sum
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+1,47,432,214657,0,376777,1133,499,872.2,346230,1272,1310,373271,14007280,8639936081,144,0,0,0,159530057,11,15577,4,3,15657,3,15657,0,31314,19008,432,20
+1,46,433,191440,0,457364,3323,391,1056.3,352763,1508,2509,451358,5242628040,8639936081,1912,0,0,0,250840493,9,16203,551,3,15657,3,15657,0,31314,19052,433,16
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+1,53,1,46297,0,136,136,136,136.0,57,1,1,131,0,0,0,0,0,0,138414192,0,5344,0,214,9416,219,9636,0,19052,44,1,0
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+1,23,200,5479,0,84240,490,355,421.2,5290,200,214,82784,0,0,136,0,0,0,0,0,6214,0,0,0,0,0,0,0,0,0,0
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+1,12,1,105,0,85,85,85,85.0,4,3,3,72,0,0,0,0,0,0,0,0,3369,0,0,0,0,0,0,0,0,0,0
+1,17,1,103,0,97,97,97,97.0,2,2,2,89,0,0,0,0,0,0,0,0,3003,0,0,0,0,0,0,0,0,0,0
+1,8,1,102,0,95,95,95,95.0,4,3,3,82,0,0,0,0,0,0,0,0,3142,0,0,0,0,0,0,0,0,0,0
+1,30,1,73,0,67,67,67,67.0,2,1,1,62,0,0,0,0,0,0,0,0,3199,0,0,0,0,0,0,0,0,0,0
+1,24,1,72,0,59,59,59,59.0,2,1,1,51,0,0,0,0,0,0,0,0,3288,0,0,0,0,0,0,0,0,0,0
+1,22,1,70,0,65,65,65,65.0,2,1,1,59,0,0,0,0,0,0,0,0,3436,0,0,0,0,0,0,0,0,0,0
+1,55,1,65,0,54,54,54,54.0,27,1,1,49,0,0,0,0,0,0,138414192,0,5343,0,216,9504,215,9460,0,18964,44,1,0
+1,15,1,64,0,58,58,58,58.0,2,1,1,50,0,0,0,0,0,0,0,0,2306,0,0,0,0,0,0,0,0,0,0

core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_sql_metrics_agg_expectation.csv

@@ -0,0 +1,2 @@
+appIndex,appID,sqlID,description,numTasks,Duration,executorCPUTime,executorRunTime,executorCPURatio,diskBytesSpilled_sum,duration_sum,duration_max,duration_min,duration_avg,executorCPUTime_sum,executorDeserializeCPUTime_sum,executorDeserializeTime_sum,executorRunTime_sum,input_bytesRead_sum,input_recordsRead_sum,jvmGCTime_sum,memoryBytesSpilled_sum,output_bytesWritten_sum,output_recordsWritten_sum,peakExecutionMemory_max,resultSerializationTime_sum,resultSize_max,sr_fetchWaitTime_sum,sr_localBlocksFetched_sum,sr_localBytesRead_sum,sr_remoteBlocksFetched_sum,sr_remoteBytesRead_sum,sr_remoteBytesReadToDisk_sum,sr_totalBytesRead_sum,sw_bytesWritten_sum,sw_recordsWritten_sum,sw_writeTime_sum
+1,"app-20240919162642-0000",26,"query88",3472,250542,2883837,3818106,75.53,0,3858136,6743,54,1111.2,2883837,12349,18186,3818106,52997115316,69120188398,16100,0,0,0,250840493,181,16203,1394,1759,201596,1750,201200,0,402796,218614,19946,154