Update for Spark 3.0.0 compatibility (#191)

* Updating the build file to spark 3.0.0 and scala 2.12.10
* Fixing incompatibilities
* Adding default parameters to newer required functions
* Removing HiveTest

build.sbt

@@ -5,16 +5,16 @@ name := "spark-sql-perf"
 
 organization := "com.databricks"
 
-scalaVersion := "2.11.12"
+scalaVersion := "2.12.10"
 
-crossScalaVersions := Seq("2.11.12","2.12.8")
+crossScalaVersions := Seq("2.12.10", "2.11.12")
 
 sparkPackageName := "databricks/spark-sql-perf"
 
 // All Spark Packages need a license
 licenses := Seq("Apache-2.0" -> url("http://opensource.org/licenses/Apache-2.0"))
 
-sparkVersion := "2.4.0"
+sparkVersion := "3.0.0"
 
 sparkComponents ++= Seq("sql", "hive", "mllib")
 

src/main/scala/com/databricks/spark/sql/perf/Benchmark.scala

@@ -335,7 +335,7 @@ object Benchmark {
         .flatMap { query =>
           try {
             query.newDataFrame().queryExecution.logical.collect {
-              case UnresolvedRelation(t) => t.table
+              case r: UnresolvedRelation => r.tableName
             }
           } catch {
             // ignore the queries that can't be parsed

src/main/scala/com/databricks/spark/sql/perf/Benchmarkable.scala

@@ -77,7 +77,7 @@ trait Benchmarkable {
         try {
           result = doBenchmark(includeBreakdown, description, messages)
         } catch {
-          case NonFatal(e) =>
+          case e: Throwable =>
             logger.info(s"$that: failure in runBenchmark: $e")
             println(s"$that: failure in runBenchmark: $e")
             result = BenchmarkResult(

src/main/scala/com/databricks/spark/sql/perf/Query.scala

@@ -54,10 +54,7 @@ class Query(
   }
 
   lazy val tablesInvolved = buildDataFrame.queryExecution.logical collect {
-    case UnresolvedRelation(tableIdentifier) => {
-      // We are ignoring the database name.
-      tableIdentifier.table
-    }
+    case r: UnresolvedRelation => r.tableName
   }
 
   def newDataFrame() = buildDataFrame
@@ -88,10 +85,11 @@ class Query(
         val physicalOperators = (0 until depth).map(i => (i, queryExecution.executedPlan.p(i)))
         val indexMap = physicalOperators.map { case (index, op) => (op, index) }.toMap
         val timeMap = new mutable.HashMap[Int, Double]
+        val maxFields = 999 // Maximum number of fields that will be converted to strings
 
         physicalOperators.reverse.map {
           case (index, node) =>
-            messages += s"Breakdown: ${node.simpleString}"
+            messages += s"Breakdown: ${node.simpleString(maxFields)}"
             val newNode = buildDataFrame.queryExecution.executedPlan.p(index)
             val executionTime = measureTimeMs {
               newNode.execute().foreach((row: Any) => Unit)
@@ -104,7 +102,7 @@ class Query(
 
             BreakdownResult(
               node.nodeName,
-              node.simpleString.replaceAll("#\\d+", ""),
+              node.simpleString(maxFields).replaceAll("#\\d+", ""),
               index,
               childIndexes,
               executionTime,

src/main/scala/com/databricks/spark/sql/perf/mllib/classification/NaiveBayes.scala

@@ -51,6 +51,7 @@ object NaiveBayes extends BenchmarkAlgorithm
     // Initialize new Naive Bayes model
     val pi = Vectors.dense(piArray)
     val theta = new DenseMatrix(numClasses, numFeatures, thetaArray.flatten, true)
+
     ModelBuilderSSP.newNaiveBayesModel(pi, theta)
   }
 

src/main/scala/org/apache/spark/ml/ModelBuilderSSP.scala

@@ -59,7 +59,7 @@ object ModelBuilderSSP {
   }
 
   def newNaiveBayesModel(pi: Vector, theta: Matrix): NaiveBayesModel = {
-    val model = new NaiveBayesModel("naivebayes-uid", pi, theta)
+    val model = new NaiveBayesModel("naivebayes-uid", pi, theta, null)
     model.set(model.modelType, "multinomial")
   }
 
@@ -160,9 +160,9 @@ object TreeBuilder {
     labelGenerator.setSeed(rng.nextLong)
     // We use a dummy impurityCalculator for all nodes.
     val impurityCalculator = if (isRegression) {
-      ImpurityCalculator.getCalculator("variance", Array.fill[Double](3)(0.0))
+      ImpurityCalculator.getCalculator("variance", Array.fill[Double](3)(0.0), 0L)
     } else {
-      ImpurityCalculator.getCalculator("gini", Array.fill[Double](labelType)(0.0))
+      ImpurityCalculator.getCalculator("gini", Array.fill[Double](labelType)(0.0), 0L)
     }
 
     randomBalancedDecisionTreeHelper(depth, featureArity, impurityCalculator,

src/test/scala/com/databricks/spark/sql/perf/DatasetPerformanceSuite.scala

@@ -1,11 +1,9 @@
 package com.databricks.spark.sql.perf
 
-import org.apache.spark.sql.hive.test.TestHive
 import org.scalatest.FunSuite
 
 class DatasetPerformanceSuite extends FunSuite {
   ignore("run benchmark") {
-    TestHive // Init HiveContext
     val benchmark = new DatasetPerformance() {
       override val numLongs = 100
     }