Add support in core tools for running qualification on Dataproc GKE

core/docs/spark-qualification-tool.md

@@ -29,6 +29,7 @@ applicable environments.  Here are the cluster information for the ETL benchmark
 | Dataproc (T4)            | 4x n1-standard-32 | 4x n1-standard-32 + 8x T4 16GB |
 | Dataproc (L4)            | 8x n1-standard-16 | 8x g2-standard-16              |
 | Dataproc Serverless (L4) | 8x 16 cores       | 8x 16 cores + 8x L4 24GB       |
+| Dataproc GKE (T4)        | 8x n1-standard-32 | 8x n1-standard-32 + 8x T4 16GB |
 | EMR (T4)                 | 8x m5d.8xlarge    | 4x g4dn.12xlarge               |
 | EMR (A10)                | 8x m5d.8xlarge    | 8x g5.8xlarge                  |
 | Databricks AWS           | 8x m6gd.8xlage    | 8x g5.8xlarge                  |
@@ -248,8 +249,8 @@ Usage: java -cp rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*
   -p, --per-sql                      Report at the individual SQL query level.
       --platform  <arg>              Cluster platform where Spark CPU workloads were
                                      executed. Options include onprem, dataproc-t4,
-                                     dataproc-l4, dataproc-serverless-l4, emr-t4,
-                                     emr-a10, databricks-aws, and databricks-azure.
+                                     dataproc-l4, dataproc-serverless-l4, dataproc-gke-t4,
+                                     emr-t4, emr-a10, databricks-aws, and databricks-azure.
                                      Default is onprem.
   -r, --report-read-schema           Whether to output the read formats and
                                      datatypes to the CSV file. This can be very

core/src/main/resources/operatorsScore-dataproc-gke-t4.csv

@@ -0,0 +1,268 @@
+CPUOperator,Score
+CoalesceExec,3.65
+CollectLimitExec,3.65
+ExpandExec,3.76
+FileSourceScanExec,2.84
+FilterExec,3.79
+GenerateExec,3.65
+GlobalLimitExec,3.65
+LocalLimitExec,3.65
+ProjectExec,3.65
+RangeExec,3.65
+SampleExec,3.65
+SortExec,3.65
+TakeOrderedAndProjectExec,3.65
+HashAggregateExec,4.1
+ObjectHashAggregateExec,4.1
+SortAggregateExec,4.1
+DataWritingCommandExec,3.65
+ExecutedCommandExec,3.65
+BatchScanExec,2.84
+ShuffleExchangeExec,3.69
+BroadcastHashJoinExec,3.72
+BroadcastNestedLoopJoinExec,1.66
+CartesianProductExec,3.65
+ShuffledHashJoinExec,3.65
+SortMergeJoinExec,5.64
+WindowExec,3.65
+Abs,3.65
+Acos,3.65
+Acosh,3.65
+Add,3.65
+AggregateExpression,3.65
+Alias,3.65
+And,3.65
+ApproximatePercentile,3.65
+ArrayContains,3.65
+ArrayExcept,3.65
+ArrayExists,3.65
+ArrayIntersect,3.65
+ArrayMax,3.65
+ArrayMin,3.65
+ArrayRemove,3.65
+ArrayRepeat,3.65
+ArrayTransform,3.65
+ArrayUnion,3.65
+ArraysOverlap,3.65
+ArraysZip,3.65
+Asin,3.65
+Asinh,3.65
+AtLeastNNonNulls,3.65
+Atan,3.65
+Atanh,3.65
+AttributeReference,3.65
+Average,3.65
+BRound,3.65
+BitLength,3.65
+BitwiseAnd,3.65
+BitwiseNot,3.65
+BitwiseOr,3.65
+BitwiseXor,3.65
+CaseWhen,3.65
+Cbrt,3.65
+Ceil,3.65
+CheckOverflow,3.65
+Coalesce,3.65
+CollectList,3.65
+CollectSet,3.65
+Concat,3.65
+ConcatWs,3.65
+Contains,3.65
+Conv,3.65
+Cos,3.65
+Cosh,3.65
+Cot,3.65
+Count,3.65
+CreateArray,3.65
+CreateMap,3.65
+CreateNamedStruct,3.65
+CurrentRow$,3.65
+DateAdd,3.65
+DateAddInterval,3.65
+DateDiff,3.65
+DateFormatClass,3.65
+DateSub,3.65
+DayOfMonth,3.65
+DayOfWeek,3.65
+DayOfYear,3.65
+DenseRank,3.65
+Divide,3.65
+DynamicPruningExpression,3.65
+ElementAt,3.65
+EndsWith,3.65
+EqualNullSafe,3.65
+EqualTo,3.65
+Exp,3.65
+Explode,3.65
+Expm1,3.65
+First,3.65
+Flatten,3.65
+Floor,3.65
+FromUTCTimestamp,3.65
+FromUnixTime,3.65
+GetArrayItem,3.65
+GetArrayStructFields,3.65
+GetJsonObject,3.65
+GetMapValue,3.65
+GetStructField,3.65
+GetTimestamp,3.65
+GreaterThan,3.65
+GreaterThanOrEqual,3.65
+Greatest,3.65
+HiveGenericUDF,3.65
+HiveSimpleUDF,3.65
+Hour,3.65
+Hypot,3.65
+If,3.65
+In,3.65
+InSet,3.65
+InitCap,3.65
+InputFileBlockLength,3.65
+InputFileBlockStart,3.65
+InputFileName,3.65
+IntegralDivide,3.65
+IsNaN,3.65
+IsNotNull,3.65
+IsNull,3.65
+JsonToStructs,3.65
+JsonTuple,3.65
+KnownFloatingPointNormalized,3.65
+KnownNotNull,3.65
+Lag,3.65
+LambdaFunction,3.65
+Last,3.65
+LastDay,3.65
+Lead,3.65
+Least,3.65
+Length,3.65
+LessThan,3.65
+LessThanOrEqual,3.65
+Like,3.65
+Literal,3.65
+Log,3.65
+Log10,3.65
+Log1p,3.65
+Log2,3.65
+Logarithm,3.65
+Lower,3.65
+MakeDecimal,3.65
+MapConcat,3.65
+MapEntries,3.65
+MapFilter,3.65
+MapKeys,3.65
+MapValues,3.65
+Max,3.65
+Md5,3.65
+MicrosToTimestamp,3.65
+MillisToTimestamp,3.65
+Min,3.65
+Minute,3.65
+MonotonicallyIncreasingID,3.65
+Month,3.65
+Multiply,3.65
+Murmur3Hash,3.65
+NaNvl,3.65
+NamedLambdaVariable,3.65
+NormalizeNaNAndZero,3.65
+Not,3.65
+NthValue,3.65
+OctetLength,3.65
+Or,3.65
+PercentRank,3.65
+PivotFirst,3.65
+Pmod,3.65
+PosExplode,3.65
+Pow,3.65
+PreciseTimestampConversion,3.65
+PromotePrecision,3.65
+PythonUDF,3.65
+Quarter,3.65
+RLike,3.65
+RaiseError,3.65
+Rand,3.65
+Rank,3.65
+RegExpExtract,3.65
+RegExpExtractAll,3.65
+RegExpReplace,3.65
+Remainder,3.65
+ReplicateRows,3.65
+Reverse,3.65
+Rint,3.65
+Round,3.65
+RowNumber,3.65
+ScalaUDF,3.65
+ScalarSubquery,3.65
+Second,3.65
+SecondsToTimestamp,3.65
+Sequence,3.65
+ShiftLeft,3.65
+ShiftRight,3.65
+ShiftRightUnsigned,3.65
+Signum,3.65
+Sin,3.65
+Sinh,3.65
+Size,3.65
+SortArray,3.65
+SortOrder,3.65
+SparkPartitionID,3.65
+SpecifiedWindowFrame,3.65
+Sqrt,3.65
+StartsWith,3.65
+StddevPop,3.65
+StddevSamp,3.65
+StringInstr,3.65
+StringLPad,3.65
+StringLocate,3.65
+StringRPad,3.65
+StringRepeat,3.65
+StringReplace,3.65
+StringSplit,3.65
+StringToMap,3.65
+StringTranslate,3.65
+StringTrim,3.65
+StringTrimLeft,3.65
+StringTrimRight,3.65
+Substring,3.65
+SubstringIndex,3.65
+Subtract,3.65
+Sum,3.65
+Tan,3.65
+Tanh,3.65
+TimeAdd,3.65
+ToDegrees,3.65
+ToRadians,3.65
+ToUnixTimestamp,3.65
+TransformKeys,3.65
+TransformValues,3.65
+UnaryMinus,3.65
+UnaryPositive,3.65
+UnboundedFollowing$,3.65
+UnboundedPreceding$,3.65
+UnixTimestamp,3.65
+UnscaledValue,3.65
+Upper,3.65
+VariancePop,3.65
+VarianceSamp,3.65
+WeekDay,3.65
+WindowExpression,3.65
+WindowSpecDefinition,3.65
+XxHash64,3.65
+Year,3.65
+AggregateInPandasExec,1.2
+ArrowEvalPythonExec,1.2
+FlatMapGroupsInPandasExec,1.2
+FlatMapCoGroupsInPandasExec,1.2
+MapInPandasExec,1.2
+WindowInPandasExec,1.2
+KMeans-pyspark,8.86
+KMeans-scala,1.0
+PCA-pyspark,2.24
+PCA-scala,2.69
+LinearRegression-pyspark,2.0
+LinearRegression-scala,1.0
+RandomForestClassifier-pyspark,6.31
+RandomForestClassifier-scala,1.0
+RandomForestRegressor-pyspark,3.66
+RandomForestRegressor-scala,1.0
+XGBoost-pyspark,1.0
+XGBoost-scala,3.31

core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/PluginTypeChecker.scala

@@ -48,6 +48,7 @@ class PluginTypeChecker(platform: String = "onprem",
   private val OPERATORS_SCORE_FILE_DATAPROC_T4 = "operatorsScore-dataproc-t4.csv"
   private val OPERATORS_SCORE_FILE_DATAPROC_L4 = "operatorsScore-dataproc-l4.csv"
   private val OPERATORS_SCORE_FILE_DATAPROC_SL_L4 = "operatorsScore-dataproc-serverless-l4.csv"
+  private val OPERATORS_SCORE_FILE_DATAPROC_GKE_T4 = "operatorsScore-dataproc-gke-t4.csv"
   private val OPERATORS_SCORE_FILE_EMR_T4 = "operatorsScore-emr-t4.csv"
   private val OPERATORS_SCORE_FILE_EMR_A10 = "operatorsScore-emr-a10.csv"
   private val OPERATORS_SCORE_FILE_DATABRICKS_AWS = "operatorsScore-databricks-aws.csv"
@@ -104,6 +105,7 @@ class PluginTypeChecker(platform: String = "onprem",
           case "dataproc-t4" | "dataproc" => OPERATORS_SCORE_FILE_DATAPROC_T4
           case "dataproc-l4" => OPERATORS_SCORE_FILE_DATAPROC_L4
           case "dataproc-serverless-l4" => OPERATORS_SCORE_FILE_DATAPROC_SL_L4
+          case "dataproc-gke-t4" => OPERATORS_SCORE_FILE_DATAPROC_GKE_T4
           // if no GPU specified, then default to emr-t4 for backward compatibility
           case "emr-t4" | "emr" => OPERATORS_SCORE_FILE_EMR_T4
           case "emr-a10" => OPERATORS_SCORE_FILE_EMR_A10

core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualificationArgs.scala

@@ -148,9 +148,8 @@ Usage: java -cp rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*
   val platform: ScallopOption[String] =
     opt[String](required = false,
       descr = "Cluster platform where Spark CPU workloads were executed. Options include " +
-        "onprem, dataproc-t4, dataproc-l4, dataproc-serverless-l4, emr-t4, emr-a10, " +
-        "databricks-aws, and databricks-azure. " +
-        "Default is onprem.",
+        "onprem, dataproc-t4, dataproc-l4, dataproc-serverless-l4, dataproc-gke-t4, emr-t4, " +
+        "emr-a10, databricks-aws, and databricks-azure. Default is onprem.",
       default = Some("onprem"))
   val speedupFactorFile: ScallopOption[String] =
     opt[String](required = false,

core/src/test/scala/com/nvidia/spark/rapids/tool/qualification/PluginTypeCheckerSuite.scala

@@ -188,12 +188,19 @@ class PluginTypeCheckerSuite extends FunSuite with Logging {
     assert(checker.getSpeedupFactor("WindowExec") == 4.25)
     assert(checker.getSpeedupFactor("Ceil") == 4.25)
   }
+
   test("supported operator score from dataproc-l4") {
     val checker = new PluginTypeChecker("dataproc-l4")
     assert(checker.getSpeedupFactor("UnionExec") == 4.16)
     assert(checker.getSpeedupFactor("Ceil") == 4.16)
   }
 
+  test("supported operator score from dataproc-gke-t4") {
+    val checker = new PluginTypeChecker("dataproc-gke-t4")
+    assert(checker.getSpeedupFactor("WindowExec") == 3.65)
+    assert(checker.getSpeedupFactor("Ceil") == 3.65)
+  }
+
   test("supported operator score from emr-a10") {
     val checker = new PluginTypeChecker("emr-a10")
     assert(checker.getSpeedupFactor("UnionExec") == 2.59)

core/src/test/scala/com/nvidia/spark/rapids/tool/qualification/QualificationSuite.scala

@@ -1529,6 +1529,30 @@ class QualificationSuite extends BaseTestSuite {
         assert(outputActual.collect().size == 1)
       }
 
+      // run the qualification tool for dataproc-gke-t4
+      TrampolineUtil.withTempDir { outpath =>
+        val appArgs = new QualificationArgs(Array(
+          "--output-directory",
+          outpath.getAbsolutePath,
+          "--platform",
+          "dataproc-gke-t4",
+          eventLog))
+
+        val (exit, _) =
+          QualificationMain.mainInternal(appArgs)
+        assert(exit == 0)
+
+        // the code above that runs the Spark query stops the Sparksession
+        // so create a new one to read in the csv file
+        createSparkSession()
+
+        // validate that the SQL description in the csv file escapes commas properly
+        val outputResults = s"$outpath/rapids_4_spark_qualification_output/" +
+          s"rapids_4_spark_qualification_output.csv"
+        val outputActual = readExpectedFile(new File(outputResults))
+        assert(outputActual.collect().size == 1)
+      }
+
       // run the qualification tool for databricks-aws
       TrampolineUtil.withTempDir { outpath =>
         val appArgs = new QualificationArgs(Array(