org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext Scala Examples

package com.hortonworks.spark.registry.avro

import java.io.ByteArrayInputStream

import com.hortonworks.registries.schemaregistry.{SchemaVersionInfo, SchemaVersionKey}
import com.hortonworks.registries.schemaregistry.client.SchemaRegistryClient
import com.hortonworks.registries.schemaregistry.serdes.avro.AvroSnapshotDeserializer
import org.apache.avro.Schema
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{BinaryType, DataType}

import scala.collection.JavaConverters._


case class AvroDataToCatalyst(child: Expression, schemaName: String, version: Option[Int], config: Map[String, Object])
  extends UnaryExpression with ExpectsInputTypes {

  override def inputTypes = Seq(BinaryType)

  @transient private lazy val srDeser: AvroSnapshotDeserializer = {
    val obj = new AvroSnapshotDeserializer()
    obj.init(config.asJava)
    obj
  }

  @transient private lazy val srSchema = fetchSchemaVersionInfo(schemaName, version)

  @transient private lazy val avroSchema = new Schema.Parser().parse(srSchema.getSchemaText)

  override lazy val dataType: DataType = SchemaConverters.toSqlType(avroSchema).dataType

  @transient private lazy val avroDeser= new AvroDeserializer(avroSchema, dataType)

  override def nullable: Boolean = true

  override def nullSafeEval(input: Any): Any = {
    val binary = input.asInstanceOf[Array[Byte]]
    val row = avroDeser.deserialize(srDeser.deserialize(new ByteArrayInputStream(binary), srSchema.getVersion))
    val result = row match {
      case r: InternalRow => r.copy()
      case _ => row
    }
    result
  }

  override def simpleString: String = {
    s"from_sr(${child.sql}, ${dataType.simpleString})"
  }

  override def sql: String = {
    s"from_sr(${child.sql}, ${dataType.catalogString})"
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val expr = ctx.addReferenceObj("this", this)
    defineCodeGen(ctx, ev, input =>
      s"(${ctx.boxedType(dataType)})$expr.nullSafeEval($input)")
  }

  private def fetchSchemaVersionInfo(schemaName: String, version: Option[Int]): SchemaVersionInfo = {
    val srClient = new SchemaRegistryClient(config.asJava)
    version.map(v => srClient.getSchemaVersionInfo(new SchemaVersionKey(schemaName, v)))
      .getOrElse(srClient.getLatestSchemaVersionInfo(schemaName))
  }

} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.expressions.objects.LambdaVariable
import org.apache.spark.sql.types.DataType


case class ReferenceToExpressions(result: Expression, children: Seq[Expression])
  extends Expression {

  override def nullable: Boolean = result.nullable
  override def dataType: DataType = result.dataType

  override def checkInputDataTypes(): TypeCheckResult = {
    if (result.references.nonEmpty) {
      return TypeCheckFailure("The result expression cannot reference to any attributes.")
    }

    var maxOrdinal = -1
    result foreach {
      case b: BoundReference if b.ordinal > maxOrdinal => maxOrdinal = b.ordinal
      case _ =>
    }
    if (maxOrdinal > children.length) {
      return TypeCheckFailure(s"The result expression need $maxOrdinal input expressions, but " +
        s"there are only ${children.length} inputs.")
    }

    TypeCheckSuccess
  }

  private lazy val projection = UnsafeProjection.create(children)

  override def eval(input: InternalRow): Any = {
    result.eval(projection(input))
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val childrenGen = children.map(_.genCode(ctx))
    val (classChildrenVars, initClassChildrenVars) = childrenGen.zip(children).map {
      case (childGen, child) =>
        // SPARK-18125: The children vars are local variables. If the result expression uses
        // splitExpression, those variables cannot be accessed so compilation fails.
        // To fix it, we use class variables to hold those local variables.
        val classChildVarName = ctx.freshName("classChildVar")
        val classChildVarIsNull = ctx.freshName("classChildVarIsNull")
        ctx.addMutableState(ctx.javaType(child.dataType), classChildVarName, "")
        ctx.addMutableState("boolean", classChildVarIsNull, "")

        val classChildVar =
          LambdaVariable(classChildVarName, classChildVarIsNull, child.dataType)

        val initCode = s"${classChildVar.value} = ${childGen.value};\n" +
          s"${classChildVar.isNull} = ${childGen.isNull};"

        (classChildVar, initCode)
    }.unzip

    val resultGen = result.transform {
      case b: BoundReference => classChildrenVars(b.ordinal)
    }.genCode(ctx)

    ExprCode(code = childrenGen.map(_.code).mkString("\n") + initClassChildrenVars.mkString("\n") +
      resultGen.code, isNull = resultGen.isNull, value = resultGen.value)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.errors.attachTree
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
  extends LeafExpression {

  override def toString: String = s"input[$ordinal, ${dataType.simpleString}, $nullable]"

  // Use special getter for primitive types (for UnsafeRow)
  override def eval(input: InternalRow): Any = {
    if (input.isNullAt(ordinal)) {
      null
    } else {
      dataType match {
        case BooleanType => input.getBoolean(ordinal)
        case ByteType => input.getByte(ordinal)
        case ShortType => input.getShort(ordinal)
        case IntegerType | DateType => input.getInt(ordinal)
        case LongType | TimestampType => input.getLong(ordinal)
        case FloatType => input.getFloat(ordinal)
        case DoubleType => input.getDouble(ordinal)
        case StringType => input.getUTF8String(ordinal)
        case BinaryType => input.getBinary(ordinal)
        case CalendarIntervalType => input.getInterval(ordinal)
        case t: DecimalType => input.getDecimal(ordinal, t.precision, t.scale)
        case t: StructType => input.getStruct(ordinal, t.size)
        case _: ArrayType => input.getArray(ordinal)
        case _: MapType => input.getMap(ordinal)
        case _ => input.get(ordinal, dataType)
      }
    }
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val javaType = ctx.javaType(dataType)
    val value = ctx.getValue(ctx.INPUT_ROW, dataType, ordinal.toString)
    if (ctx.currentVars != null && ctx.currentVars(ordinal) != null) {
      val oev = ctx.currentVars(ordinal)
      ev.isNull = oev.isNull
      ev.value = oev.value
      val code = oev.code
      oev.code = ""
      ev.copy(code = code)
    } else if (nullable) {
      ev.copy(code = s"""
        boolean ${ev.isNull} = ${ctx.INPUT_ROW}.isNullAt($ordinal);
        $javaType ${ev.value} = ${ev.isNull} ? ${ctx.defaultValue(dataType)} : ($value);""")
    } else {
      ev.copy(code = s"""$javaType ${ev.value} = $value;""", isNull = "false")
    }
  }
}

object BindReferences extends Logging {

  def bindReference[A <: Expression](
      expression: A,
      input: AttributeSeq,
      allowFailures: Boolean = false): A = {
    expression.transform { case a: AttributeReference =>
      attachTree(a, "Binding attribute") {
        val ordinal = input.indexOf(a.exprId)
        if (ordinal == -1) {
          if (allowFailures) {
            a
          } else {
            sys.error(s"Couldn't find $a in ${input.attrs.mkString("[", ",", "]")}")
          }
        } else {
          BoundReference(ordinal, a.dataType, input(ordinal).nullable)
        }
      }
    }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {

  override def nullable: Boolean = true

  override def nullSafeEval(input: Any): Any = {
    val d = input.asInstanceOf[Decimal].clone()
    if (d.changePrecision(dataType.precision, dataType.scale)) {
      d
    } else {
      null
    }
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    nullSafeCodeGen(ctx, ev, eval => {
      val tmp = ctx.freshName("tmp")
      s"""
         | Decimal $tmp = $eval.clone();
         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
         |   ${ev.value} = $tmp;
         | } else {
         |   ${ev.isNull} = true;
         | }
       """.stripMargin
    })
  }

  override def toString: String = s"CheckOverflow($child, $dataType)"

  override def sql: String = child.sql
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.expressions.objects.LambdaVariable
import org.apache.spark.sql.types.DataType


case class ReferenceToExpressions(result: Expression, children: Seq[Expression])
  extends Expression {

  override def nullable: Boolean = result.nullable
  override def dataType: DataType = result.dataType

  override def checkInputDataTypes(): TypeCheckResult = {
    if (result.references.nonEmpty) {
      return TypeCheckFailure("The result expression cannot reference to any attributes.")
    }

    var maxOrdinal = -1
    result foreach {
      case b: BoundReference if b.ordinal > maxOrdinal => maxOrdinal = b.ordinal
      case _ =>
    }
    if (maxOrdinal > children.length) {
      return TypeCheckFailure(s"The result expression need $maxOrdinal input expressions, but " +
        s"there are only ${children.length} inputs.")
    }

    TypeCheckSuccess
  }

  private lazy val projection = UnsafeProjection.create(children)

  override def eval(input: InternalRow): Any = {
    result.eval(projection(input))
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val childrenGen = children.map(_.genCode(ctx))
    val (classChildrenVars, initClassChildrenVars) = childrenGen.zip(children).map {
      case (childGen, child) =>
        // SPARK-18125: The children vars are local variables. If the result expression uses
        // splitExpression, those variables cannot be accessed so compilation fails.
        // To fix it, we use class variables to hold those local variables.
        val classChildVarName = ctx.freshName("classChildVar")
        val classChildVarIsNull = ctx.freshName("classChildVarIsNull")
        ctx.addMutableState(ctx.javaType(child.dataType), classChildVarName, "")
        ctx.addMutableState("boolean", classChildVarIsNull, "")

        val classChildVar =
          LambdaVariable(classChildVarName, classChildVarIsNull, child.dataType)

        val initCode = s"${classChildVar.value} = ${childGen.value};\n" +
          s"${classChildVar.isNull} = ${childGen.isNull};"

        (classChildVar, initCode)
    }.unzip

    val resultGen = result.transform {
      case b: BoundReference => classChildrenVars(b.ordinal)
    }.genCode(ctx)

    ExprCode(code = childrenGen.map(_.code).mkString("\n") + initClassChildrenVars.mkString("\n") +
      resultGen.code, isNull = resultGen.isNull, value = resultGen.value)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, IntegerType}


case class BadCodegenExpression() extends LeafExpression {
  override def nullable: Boolean = false
  override def eval(input: InternalRow): Any = 10
  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ev.copy(code =
      s"""
        |int some_variable = 11;
        |int ${ev.value} = 10;
      """.stripMargin)
  }
  override def dataType: DataType = IntegerType
} 

package org.apache.spark.sql.delta.schema

import org.apache.spark.sql.delta.schema.Invariants.{ArbitraryExpression, NotNull}

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
import org.apache.spark.sql.catalyst.expressions.{Expression, NonSQLExpression, UnaryExpression}
import org.apache.spark.sql.catalyst.expressions.codegen.{Block, CodegenContext, ExprCode, JavaCode, TrueLiteral}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types.{DataType, NullType}


case class CheckDeltaInvariant(
    child: Expression,
    invariant: Invariant) extends UnaryExpression with NonSQLExpression {

  override def dataType: DataType = NullType
  override def foldable: Boolean = false
  override def nullable: Boolean = true

  override def flatArguments: Iterator[Any] = Iterator(child)

  private def assertRule(input: InternalRow): Unit = invariant.rule match {
    case NotNull if child.eval(input) == null =>
      throw InvariantViolationException(invariant, "")
    case ArbitraryExpression(expr) =>
      val resolvedExpr = expr.transform {
        case _: UnresolvedAttribute => child
      }
      val result = resolvedExpr.eval(input)
      if (result == null || result == false) {
        throw InvariantViolationException(
          invariant, s"Value ${child.eval(input)} violates requirement.")
      }
  }

  override def eval(input: InternalRow): Any = {
    assertRule(input)
    null
  }

  private def generateNotNullCode(ctx: CodegenContext): Block = {
    val childGen = child.genCode(ctx)
    val invariantField = ctx.addReferenceObj("errMsg", invariant)
    code"""${childGen.code}
       |
       |if (${childGen.isNull}) {
       |  throw org.apache.spark.sql.delta.schema.InvariantViolationException.apply(
       |    $invariantField, "");
       |}
     """.stripMargin
  }

  private def generateExpressionValidationCode(expr: Expression, ctx: CodegenContext): Block = {
    val resolvedExpr = expr.transform {
      case _: UnresolvedAttribute => child
    }
    val elementValue = child.genCode(ctx)
    val childGen = resolvedExpr.genCode(ctx)
    val invariantField = ctx.addReferenceObj("errMsg", invariant)
    val eValue = ctx.freshName("elementResult")
    code"""${elementValue.code}
       |${childGen.code}
       |
       |if (${childGen.isNull} || ${childGen.value} == false) {
       |  Object $eValue = "null";
       |  if (!${elementValue.isNull}) {
       |    $eValue = (Object) ${elementValue.value};
       |  }
       |  throw org.apache.spark.sql.delta.schema.InvariantViolationException.apply(
       |     $invariantField, "Value " + $eValue + " violates requirement.");
       |}
     """.stripMargin
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val code = invariant.rule match {
      case NotNull => generateNotNullCode(ctx)
      case ArbitraryExpression(expr) => generateExpressionValidationCode(expr, ctx)
    }
    ev.copy(code = code, isNull = TrueLiteral, value = JavaCode.literal("null", NullType))
  }
} 

package org.opencypher.morpheus.impl.expressions

import java.io.ByteArrayOutputStream

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.Expression
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode, _}
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
import org.opencypher.morpheus.impl.expressions.EncodeLong.encodeLong
import org.opencypher.morpheus.impl.expressions.Serialize._
import org.opencypher.okapi.impl.exception


case class Serialize(children: Seq[Expression]) extends Expression {

  override def dataType: DataType = BinaryType

  override def nullable: Boolean = false

  // TODO: Only write length if more than one column is serialized
  override def eval(input: InternalRow): Any = {
    // TODO: Reuse from a pool instead of allocating a new one for each serialization
    val out = new ByteArrayOutputStream()
    children.foreach { child =>
      child.dataType match {
        case BinaryType => write(child.eval(input).asInstanceOf[Array[Byte]], out)
        case StringType => write(child.eval(input).asInstanceOf[UTF8String], out)
        case IntegerType => write(child.eval(input).asInstanceOf[Int], out)
        case LongType => write(child.eval(input).asInstanceOf[Long], out)
        case other => throw exception.UnsupportedOperationException(s"Cannot serialize Spark data type $other.")
      }
    }
    out.toByteArray
  }

  override protected def doGenCode(
    ctx: CodegenContext,
    ev: ExprCode
  ): ExprCode = {
    ev.isNull = FalseLiteral
    val out = ctx.freshName("out")
    val serializeChildren = children.map { child =>
      val childEval = child.genCode(ctx)
      s"""|${childEval.code}
          |if (!${childEval.isNull}) {
          |  ${Serialize.getClass.getName.dropRight(1)}.write(${childEval.value}, $out);
          |}""".stripMargin
    }.mkString("\n")
    val baos = classOf[ByteArrayOutputStream].getName
    ev.copy(
      code = code"""|$baos $out = new $baos();
          |$serializeChildren
          |byte[] ${ev.value} = $out.toByteArray();""".stripMargin)
  }

}

object Serialize {

  val supportedTypes: Set[DataType] = Set(BinaryType, StringType, IntegerType, LongType)

  @inline final def write(value: Array[Byte], out: ByteArrayOutputStream): Unit = {
    out.write(encodeLong(value.length))
    out.write(value)
  }

  @inline final def write(
    value: Boolean,
    out: ByteArrayOutputStream
  ): Unit = write(if (value) 1.toLong else 0.toLong, out)

  @inline final def write(value: Byte, out: ByteArrayOutputStream): Unit = write(value.toLong, out)

  @inline final def write(value: Int, out: ByteArrayOutputStream): Unit = write(value.toLong, out)

  @inline final def write(value: Long, out: ByteArrayOutputStream): Unit = write(encodeLong(value), out)

  @inline final def write(value: UTF8String, out: ByteArrayOutputStream): Unit = write(value.getBytes, out)

  @inline final def write(value: String, out: ByteArrayOutputStream): Unit = write(value.getBytes, out)

} 

package org.opencypher.morpheus.impl.expressions

import org.apache.spark.sql.Column
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, NullIntolerant, UnaryExpression}
import org.apache.spark.sql.types.{BinaryType, DataType, LongType}
import org.opencypher.morpheus.api.value.MorpheusElement._


case class EncodeLong(child: Expression) extends UnaryExpression with NullIntolerant with ExpectsInputTypes {

  override val dataType: DataType = BinaryType

  override val inputTypes: Seq[LongType] = Seq(LongType)

  override protected def nullSafeEval(input: Any): Any =
    EncodeLong.encodeLong(input.asInstanceOf[Long])

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode =
    defineCodeGen(ctx, ev, c => s"(byte[])(${EncodeLong.getClass.getName.dropRight(1)}.encodeLong($c))")
}

object EncodeLong {

  private final val moreBytesBitMask: Long = Integer.parseInt("10000000", 2)
  private final val varLength7BitMask: Long = Integer.parseInt("01111111", 2)
  private final val otherBitsMask = ~varLength7BitMask
  private final val maxBytesForLongVarEncoding = 10

  // Same encoding as as Base 128 Varints @ https://developers.google.com/protocol-buffers/docs/encoding
  @inline
  final def encodeLong(l: Long): Array[Byte] = {
    val tempResult = new Array[Byte](maxBytesForLongVarEncoding)

    var remainder = l
    var index = 0

    while ((remainder & otherBitsMask) != 0) {
      tempResult(index) = ((remainder & varLength7BitMask) | moreBytesBitMask).toByte
      remainder >>>= 7
      index += 1
    }
    tempResult(index) = remainder.toByte

    val result = new Array[Byte](index + 1)
    System.arraycopy(tempResult, 0, result, 0, index + 1)
    result
  }

  // Same encoding as as Base 128 Varints @ https://developers.google.com/protocol-buffers/docs/encoding
  @inline
  final def decodeLong(input: Array[Byte]): Long = {
    assert(input.nonEmpty, "`decodeLong` requires a non-empty array as its input")
    var index = 0
    var currentByte = input(index)
    var decoded = currentByte & varLength7BitMask
    var nextLeftShift = 7

    while ((currentByte & moreBytesBitMask) != 0) {
      index += 1
      currentByte = input(index)
      decoded |= (currentByte & varLength7BitMask) << nextLeftShift
      nextLeftShift += 7
    }
    assert(index == input.length - 1,
      s"`decodeLong` received an input array ${input.toSeq.toHex} with extra bytes that could not be decoded.")
    decoded
  }

  implicit class ColumnLongOps(val c: Column) extends AnyVal {

    def encodeLongAsMorpheusId(name: String): Column = encodeLongAsMorpheusId.as(name)

    def encodeLongAsMorpheusId: Column = new Column(EncodeLong(c.expr))

  }

} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.TaskContext
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, LongType}


  @transient private[this] var count: Long = _

  @transient private[this] var partitionMask: Long = _

  override protected def initializeInternal(partitionIndex: Int): Unit = {
    count = 0L
    partitionMask = partitionIndex.toLong << 33
  }

  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override protected def evalInternal(input: InternalRow): Long = {
    val currentCount = count
    count += 1
    partitionMask + currentCount
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val countTerm = ctx.freshName("count")
    val partitionMaskTerm = ctx.freshName("partitionMask")
    ctx.addMutableState(ctx.JAVA_LONG, countTerm, "")
    ctx.addMutableState(ctx.JAVA_LONG, partitionMaskTerm, "")
    ctx.addPartitionInitializationStatement(s"$countTerm = 0L;")
    ctx.addPartitionInitializationStatement(s"$partitionMaskTerm = ((long) partitionIndex) << 33;")

    ev.copy(code = s"""
      final ${ctx.javaType(dataType)} ${ev.value} = $partitionMaskTerm + $countTerm;
      $countTerm++;""", isNull = "false")
  }

  override def prettyName: String = "monotonically_increasing_id"

  override def sql: String = s"$prettyName()"
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.errors.attachTree
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
  extends LeafExpression {

  override def toString: String = s"input[$ordinal, ${dataType.simpleString}, $nullable]"

  // Use special getter for primitive types (for UnsafeRow)
  override def eval(input: InternalRow): Any = {
    if (input.isNullAt(ordinal)) {
      null
    } else {
      dataType match {
        case BooleanType => input.getBoolean(ordinal)
        case ByteType => input.getByte(ordinal)
        case ShortType => input.getShort(ordinal)
        case IntegerType | DateType => input.getInt(ordinal)
        case LongType | TimestampType => input.getLong(ordinal)
        case FloatType => input.getFloat(ordinal)
        case DoubleType => input.getDouble(ordinal)
        case StringType => input.getUTF8String(ordinal)
        case BinaryType => input.getBinary(ordinal)
        case CalendarIntervalType => input.getInterval(ordinal)
        case t: DecimalType => input.getDecimal(ordinal, t.precision, t.scale)
        case t: StructType => input.getStruct(ordinal, t.size)
        case _: ArrayType => input.getArray(ordinal)
        case _: MapType => input.getMap(ordinal)
        case _ => input.get(ordinal, dataType)
      }
    }
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val javaType = ctx.javaType(dataType)
    val value = ctx.getValue(ctx.INPUT_ROW, dataType, ordinal.toString)
    if (ctx.currentVars != null && ctx.currentVars(ordinal) != null) {
      val oev = ctx.currentVars(ordinal)
      ev.isNull = oev.isNull
      ev.value = oev.value
      val code = oev.code
      oev.code = ""
      ev.copy(code = code)
    } else if (nullable) {
      ev.copy(code = s"""
        boolean ${ev.isNull} = ${ctx.INPUT_ROW}.isNullAt($ordinal);
        $javaType ${ev.value} = ${ev.isNull} ? ${ctx.defaultValue(dataType)} : ($value);""")
    } else {
      ev.copy(code = s"""$javaType ${ev.value} = $value;""", isNull = "false")
    }
  }
}

object BindReferences extends Logging {

  def bindReference[A <: Expression](
      expression: A,
      input: AttributeSeq,
      allowFailures: Boolean = false): A = {
    expression.transform { case a: AttributeReference =>
      attachTree(a, "Binding attribute") {
        val ordinal = input.indexOf(a.exprId)
        if (ordinal == -1) {
          if (allowFailures) {
            a
          } else {
            sys.error(s"Couldn't find $a in ${input.attrs.mkString("[", ",", "]")}")
          }
        } else {
          BoundReference(ordinal, a.dataType, input(ordinal).nullable)
        }
      }
    }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {

  override def nullable: Boolean = true

  override def nullSafeEval(input: Any): Any = {
    val d = input.asInstanceOf[Decimal].clone()
    if (d.changePrecision(dataType.precision, dataType.scale)) {
      d
    } else {
      null
    }
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    nullSafeCodeGen(ctx, ev, eval => {
      val tmp = ctx.freshName("tmp")
      s"""
         | Decimal $tmp = $eval.clone();
         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
         |   ${ev.value} = $tmp;
         | } else {
         |   ${ev.isNull} = true;
         | }
       """.stripMargin
    })
  }

  override def toString: String = s"CheckOverflow($child, $dataType)"

  override def sql: String = child.sql
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.TaskContext
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, LongType}


  @transient private[this] var count: Long = _

  @transient private[this] var partitionMask: Long = _

  override protected def initializeInternal(partitionIndex: Int): Unit = {
    count = 0L
    partitionMask = partitionIndex.toLong << 33
  }

  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override protected def evalInternal(input: InternalRow): Long = {
    val currentCount = count
    count += 1
    partitionMask + currentCount
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val countTerm = ctx.freshName("count")
    val partitionMaskTerm = ctx.freshName("partitionMask")
    ctx.addMutableState(ctx.JAVA_LONG, countTerm, "")
    ctx.addMutableState(ctx.JAVA_LONG, partitionMaskTerm, "")
    ctx.addPartitionInitializationStatement(s"$countTerm = 0L;")
    ctx.addPartitionInitializationStatement(s"$partitionMaskTerm = ((long) partitionIndex) << 33;")

    ev.copy(code = s"""
      final ${ctx.javaType(dataType)} ${ev.value} = $partitionMaskTerm + $countTerm;
      $countTerm++;""", isNull = "false")
  }

  override def prettyName: String = "monotonically_increasing_id"

  override def sql: String = s"$prettyName()"
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, IntegerType}


case class BadCodegenExpression() extends LeafExpression {
  override def nullable: Boolean = false
  override def eval(input: InternalRow): Any = 10
  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ev.copy(code =
      s"""
        |int some_variable = 11;
        |int ${ev.value} = 10;
      """.stripMargin)
  }
  override def dataType: DataType = IntegerType
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, LongType}


  @transient private[this] var count: Long = _

  @transient private[this] var partitionMask: Long = _

  override protected def initializeInternal(partitionIndex: Int): Unit = {
    count = 0L
    partitionMask = partitionIndex.toLong << 33
  }

  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override protected def evalInternal(input: InternalRow): Long = {
    val currentCount = count
    count += 1
    partitionMask + currentCount
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val countTerm = ctx.addMutableState(ctx.JAVA_LONG, "count")
    val partitionMaskTerm = "partitionMask"
    ctx.addImmutableStateIfNotExists(ctx.JAVA_LONG, partitionMaskTerm)
    ctx.addPartitionInitializationStatement(s"$countTerm = 0L;")
    ctx.addPartitionInitializationStatement(s"$partitionMaskTerm = ((long) partitionIndex) << 33;")

    ev.copy(code = s"""
      final ${ctx.javaType(dataType)} ${ev.value} = $partitionMaskTerm + $countTerm;
      $countTerm++;""", isNull = "false")
  }

  override def prettyName: String = "monotonically_increasing_id"

  override def sql: String = s"$prettyName()"
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.errors.attachTree
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
  extends LeafExpression {

  override def toString: String = s"input[$ordinal, ${dataType.simpleString}, $nullable]"

  // Use special getter for primitive types (for UnsafeRow)
  override def eval(input: InternalRow): Any = {
    if (input.isNullAt(ordinal)) {
      null
    } else {
      dataType match {
        case BooleanType => input.getBoolean(ordinal)
        case ByteType => input.getByte(ordinal)
        case ShortType => input.getShort(ordinal)
        case IntegerType | DateType => input.getInt(ordinal)
        case LongType | TimestampType => input.getLong(ordinal)
        case FloatType => input.getFloat(ordinal)
        case DoubleType => input.getDouble(ordinal)
        case StringType => input.getUTF8String(ordinal)
        case BinaryType => input.getBinary(ordinal)
        case CalendarIntervalType => input.getInterval(ordinal)
        case t: DecimalType => input.getDecimal(ordinal, t.precision, t.scale)
        case t: StructType => input.getStruct(ordinal, t.size)
        case _: ArrayType => input.getArray(ordinal)
        case _: MapType => input.getMap(ordinal)
        case _ => input.get(ordinal, dataType)
      }
    }
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    if (ctx.currentVars != null && ctx.currentVars(ordinal) != null) {
      val oev = ctx.currentVars(ordinal)
      ev.isNull = oev.isNull
      ev.value = oev.value
      ev.copy(code = oev.code)
    } else {
      assert(ctx.INPUT_ROW != null, "INPUT_ROW and currentVars cannot both be null.")
      val javaType = ctx.javaType(dataType)
      val value = ctx.getValue(ctx.INPUT_ROW, dataType, ordinal.toString)
      if (nullable) {
        ev.copy(code =
          s"""
             |boolean ${ev.isNull} = ${ctx.INPUT_ROW}.isNullAt($ordinal);
             |$javaType ${ev.value} = ${ev.isNull} ? ${ctx.defaultValue(dataType)} : ($value);
           """.stripMargin)
      } else {
        ev.copy(code = s"$javaType ${ev.value} = $value;", isNull = "false")
      }
    }
  }
}

object BindReferences extends Logging {

  def bindReference[A <: Expression](
      expression: A,
      input: AttributeSeq,
      allowFailures: Boolean = false): A = {
    expression.transform { case a: AttributeReference =>
      attachTree(a, "Binding attribute") {
        val ordinal = input.indexOf(a.exprId)
        if (ordinal == -1) {
          if (allowFailures) {
            a
          } else {
            sys.error(s"Couldn't find $a in ${input.attrs.mkString("[", ",", "]")}")
          }
        } else {
          BoundReference(ordinal, a.dataType, input(ordinal).nullable)
        }
      }
    }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {

  override def nullable: Boolean = true

  override def nullSafeEval(input: Any): Any =
    input.asInstanceOf[Decimal].toPrecision(dataType.precision, dataType.scale)

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    nullSafeCodeGen(ctx, ev, eval => {
      val tmp = ctx.freshName("tmp")
      s"""
         | Decimal $tmp = $eval.clone();
         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
         |   ${ev.value} = $tmp;
         | } else {
         |   ${ev.isNull} = true;
         | }
       """.stripMargin
    })
  }

  override def toString: String = s"CheckOverflow($child, $dataType)"

  override def sql: String = child.sql
} 

package org.apache.spark.sql.catalyst.expressions

import java.util.Locale

import org.apache.spark.{SparkException, SparkFunSuite}
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
import org.apache.spark.sql.types.{IntegerType, StringType}

class ScalaUDFSuite extends SparkFunSuite with ExpressionEvalHelper {

  test("basic") {
    val intUdf = ScalaUDF((i: Int) => i + 1, IntegerType, Literal(1) :: Nil)
    checkEvaluation(intUdf, 2)

    val stringUdf = ScalaUDF((s: String) => s + "x", StringType, Literal("a") :: Nil)
    checkEvaluation(stringUdf, "ax")
  }

  test("better error message for NPE") {
    val udf = ScalaUDF(
      (s: String) => s.toLowerCase(Locale.ROOT),
      StringType,
      Literal.create(null, StringType) :: Nil)

    val e1 = intercept[SparkException](udf.eval())
    assert(e1.getMessage.contains("Failed to execute user defined function"))

    val e2 = intercept[SparkException] {
      checkEvalutionWithUnsafeProjection(udf, null)
    }
    assert(e2.getMessage.contains("Failed to execute user defined function"))
  }

  test("SPARK-22695: ScalaUDF should not use global variables") {
    val ctx = new CodegenContext
    ScalaUDF((s: String) => s + "x", StringType, Literal("a") :: Nil).genCode(ctx)
    assert(ctx.inlinedMutableStates.isEmpty)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, IntegerType}


case class BadCodegenExpression() extends LeafExpression {
  override def nullable: Boolean = false
  override def eval(input: InternalRow): Any = 10
  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ev.copy(code =
      s"""
        |int some_variable = 11;
        |int ${ev.value} = 10;
      """.stripMargin)
  }
  override def dataType: DataType = IntegerType
} 

package com.hortonworks.spark.registry.avro

import com.hortonworks.registries.schemaregistry.{SchemaCompatibility, SchemaMetadata}
import com.hortonworks.registries.schemaregistry.avro.AvroSchemaProvider
import com.hortonworks.registries.schemaregistry.client.SchemaRegistryClient
import com.hortonworks.registries.schemaregistry.serdes.avro.AvroSnapshotSerializer
import org.apache.spark.sql.catalyst.expressions.{Expression, UnaryExpression}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{BinaryType, DataType}

import scala.collection.JavaConverters._


case class CatalystDataToAvro(
    child: Expression,
    schemaName: String,
    recordName: String,
    nameSpace: String,
    config: Map[String, Object]
    ) extends UnaryExpression {

  override def dataType: DataType = BinaryType

  private val topLevelRecordName = if (recordName == "") schemaName else recordName

  @transient private lazy val avroType =
    SchemaConverters.toAvroType(child.dataType, child.nullable, topLevelRecordName, nameSpace)

  @transient private lazy val avroSer =
    new AvroSerializer(child.dataType, avroType, child.nullable)

  @transient private lazy val srSer: AvroSnapshotSerializer = {
    val obj = new AvroSnapshotSerializer()
    obj.init(config.asJava)
    obj
  }

  @transient private lazy val srClient = new SchemaRegistryClient(config.asJava)

  @transient private lazy val schemaMetadata = {
    var schemaMetadataInfo = srClient.getSchemaMetadataInfo(schemaName)
    if (schemaMetadataInfo == null) {
      val generatedSchemaMetadata = new SchemaMetadata.Builder(schemaName).
        `type`(AvroSchemaProvider.TYPE)
        .schemaGroup("Autogenerated group")
        .description("Autogenerated schema")
        .compatibility(SchemaCompatibility.BACKWARD).build
      srClient.addSchemaMetadata(generatedSchemaMetadata)
      generatedSchemaMetadata
    } else {
      schemaMetadataInfo.getSchemaMetadata
    }
  }

  override def nullSafeEval(input: Any): Any = {
    val avroData = avroSer.serialize(input)
    srSer.serialize(avroData.asInstanceOf[Object], schemaMetadata)
  }

  override def simpleString: String = {
    s"to_sr(${child.sql}, ${child.dataType.simpleString})"
  }

  override def sql: String = {
    s"to_sr(${child.sql}, ${child.dataType.catalogString})"
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val expr = ctx.addReferenceObj("this", this)
    defineCodeGen(ctx, ev, input =>
      s"(byte[]) $expr.nullSafeEval($input)")
  }
} 

package io.projectglow.sql.expressions

import org.apache.spark.sql.SQLUtils
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate.Average
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.util.ArrayData
import org.apache.spark.sql.types.{ArrayType, NumericType, StringType, StructType}
import org.apache.spark.unsafe.types.UTF8String

import io.projectglow.sql.dsl._
import io.projectglow.sql.util.RewriteAfterResolution


case class MeanSubstitute(array: Expression, missingValue: Expression)
    extends RewriteAfterResolution {

  override def children: Seq[Expression] = Seq(array, missingValue)

  def this(array: Expression) = {
    this(array, Literal(-1))
  }

  private lazy val arrayElementType = array.dataType.asInstanceOf[ArrayType].elementType

  // A value is considered missing if it is NaN, null or equal to the missing value parameter
  def isMissing(arrayElement: Expression): Predicate =
    IsNaN(arrayElement) || IsNull(arrayElement) || arrayElement === missingValue

  def createNamedStruct(sumValue: Expression, countValue: Expression): Expression = {
    val sumName = Literal(UTF8String.fromString("sum"), StringType)
    val countName = Literal(UTF8String.fromString("count"), StringType)
    namedStruct(sumName, sumValue, countName, countValue)
  }

  // Update sum and count with array element if not missing
  def updateSumAndCountConditionally(
      stateStruct: Expression,
      arrayElement: Expression): Expression = {
    If(
      isMissing(arrayElement),
      // If value is missing, do not update sum and count
      stateStruct,
      // If value is not missing, add to sum and increment count
      createNamedStruct(
        stateStruct.getField("sum") + arrayElement,
        stateStruct.getField("count") + 1)
    )
  }

  // Calculate mean for imputation
  def calculateMean(stateStruct: Expression): Expression = {
    If(
      stateStruct.getField("count") > 0,
      // If non-missing values were found, calculate the average
      stateStruct.getField("sum") / stateStruct.getField("count"),
      // If all values were missing, substitute with missing value
      missingValue
    )
  }

  lazy val arrayMean: Expression = {
    // Sum and count of non-missing values
    array.aggregate(
      createNamedStruct(Literal(0d), Literal(0L)),
      updateSumAndCountConditionally,
      calculateMean
    )
  }

  def substituteWithMean(arrayElement: Expression): Expression = {
    If(isMissing(arrayElement), arrayMean, arrayElement)
  }

  override def rewrite: Expression = {
    if (!array.dataType.isInstanceOf[ArrayType] || !arrayElementType.isInstanceOf[NumericType]) {
      throw SQLUtils.newAnalysisException(
        s"Can only perform mean substitution on numeric array; provided type is ${array.dataType}.")
    }

    if (!missingValue.dataType.isInstanceOf[NumericType]) {
      throw SQLUtils.newAnalysisException(
        s"Missing value must be of numeric type; provided type is ${missingValue.dataType}.")
    }

    // Replace missing values with the provided strategy
    array.arrayTransform(substituteWithMean(_))
  }
} 

package io.projectglow.sql.expressions

import breeze.linalg.DenseVector
import org.apache.spark.TaskContext
import org.apache.spark.sql.SQLUtils
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.expressions.{Expression, ImplicitCastInputTypes, TernaryExpression}
import org.apache.spark.sql.catalyst.util.ArrayData
import org.apache.spark.sql.types._

object LinearRegressionExpr {
  private val matrixUDT = SQLUtils.newMatrixUDT()
  private val state = new ThreadLocal[CovariateQRContext]

  def doLinearRegression(genotypes: Any, phenotypes: Any, covariates: Any): InternalRow = {

    if (state.get() == null) {
      // Save the QR factorization of the covariate matrix since it's the same for every row
      state.set(CovariateQRContext.computeQR(matrixUDT.deserialize(covariates).toDense))
      TaskContext.get().addTaskCompletionListener[Unit](_ => state.remove())
    }

    LinearRegressionGwas.linearRegressionGwas(
      new DenseVector[Double](genotypes.asInstanceOf[ArrayData].toDoubleArray()),
      new DenseVector[Double](phenotypes.asInstanceOf[ArrayData].toDoubleArray()),
      state.get()
    )
  }
}

case class LinearRegressionExpr(
    genotypes: Expression,
    phenotypes: Expression,
    covariates: Expression)
    extends TernaryExpression
    with ImplicitCastInputTypes {

  private val matrixUDT = SQLUtils.newMatrixUDT()

  override def dataType: DataType =
    StructType(
      Seq(
        StructField("beta", DoubleType),
        StructField("standardError", DoubleType),
        StructField("pValue", DoubleType)))

  override def inputTypes: Seq[DataType] =
    Seq(ArrayType(DoubleType), ArrayType(DoubleType), matrixUDT)

  override def children: Seq[Expression] = Seq(genotypes, phenotypes, covariates)

  override protected def nullSafeEval(genotypes: Any, phenotypes: Any, covariates: Any): Any = {
    LinearRegressionExpr.doLinearRegression(genotypes, phenotypes, covariates)
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    nullSafeCodeGen(
      ctx,
      ev,
      (genotypes, phenotypes, covariates) => {
        s"""
         |${ev.value} = io.projectglow.sql.expressions.LinearRegressionExpr.doLinearRegression($genotypes, $phenotypes, $covariates);
       """.stripMargin
      }
    )
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.rdd.InputFileBlockHolder
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodeGenerator, ExprCode, FalseLiteral}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types.{DataType, LongType, StringType}
import org.apache.spark.unsafe.types.UTF8String


@ExpressionDescription(
  usage = "_FUNC_() - Returns the name of the file being read, or empty string if not available.")
case class InputFileName() extends LeafExpression with Nondeterministic {

  override def nullable: Boolean = false

  override def dataType: DataType = StringType

  override def prettyName: String = "input_file_name"

  override protected def initializeInternal(partitionIndex: Int): Unit = {}

  override protected def evalInternal(input: InternalRow): UTF8String = {
    InputFileBlockHolder.getInputFilePath
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val className = InputFileBlockHolder.getClass.getName.stripSuffix("$")
    val typeDef = s"final ${CodeGenerator.javaType(dataType)}"
    ev.copy(code = code"$typeDef ${ev.value} = $className.getInputFilePath();",
      isNull = FalseLiteral)
  }
}


@ExpressionDescription(
  usage = "_FUNC_() - Returns the start offset of the block being read, or -1 if not available.")
case class InputFileBlockStart() extends LeafExpression with Nondeterministic {
  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override def prettyName: String = "input_file_block_start"

  override protected def initializeInternal(partitionIndex: Int): Unit = {}

  override protected def evalInternal(input: InternalRow): Long = {
    InputFileBlockHolder.getStartOffset
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val className = InputFileBlockHolder.getClass.getName.stripSuffix("$")
    val typeDef = s"final ${CodeGenerator.javaType(dataType)}"
    ev.copy(code = code"$typeDef ${ev.value} = $className.getStartOffset();", isNull = FalseLiteral)
  }
}


@ExpressionDescription(
  usage = "_FUNC_() - Returns the length of the block being read, or -1 if not available.")
case class InputFileBlockLength() extends LeafExpression with Nondeterministic {
  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override def prettyName: String = "input_file_block_length"

  override protected def initializeInternal(partitionIndex: Int): Unit = {}

  override protected def evalInternal(input: InternalRow): Long = {
    InputFileBlockHolder.getLength
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val className = InputFileBlockHolder.getClass.getName.stripSuffix("$")
    val typeDef = s"final ${CodeGenerator.javaType(dataType)}"
    ev.copy(code = code"$typeDef ${ev.value} = $className.getLength();", isNull = FalseLiteral)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.TaskContext
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, LongType}


  @transient private[this] var count: Long = _

  @transient private[this] var partitionMask: Long = _

  override protected def initInternal(): Unit = {
    count = 0L
    partitionMask = TaskContext.getPartitionId().toLong << 33
  }

  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override protected def evalInternal(input: InternalRow): Long = {
    val currentCount = count
    count += 1
    partitionMask + currentCount
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val countTerm = ctx.freshName("count")
    val partitionMaskTerm = ctx.freshName("partitionMask")
    ctx.addMutableState(ctx.JAVA_LONG, countTerm, s"$countTerm = 0L;")
    ctx.addMutableState(ctx.JAVA_LONG, partitionMaskTerm,
      s"$partitionMaskTerm = ((long) org.apache.spark.TaskContext.getPartitionId()) << 33;")

    ev.copy(code = s"""
      final ${ctx.javaType(dataType)} ${ev.value} = $partitionMaskTerm + $countTerm;
      $countTerm++;""", isNull = "false")
  }

  override def prettyName: String = "monotonically_increasing_id"

  override def sql: String = s"$prettyName()"
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.TaskContext
import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, DoubleType}
import org.apache.spark.util.Utils
import org.apache.spark.util.random.XORShiftRandom


@ExpressionDescription(
  usage = "_FUNC_(a) - Returns a random column with i.i.d. gaussian random distribution.")
case class Randn(seed: Long) extends RDG {
  override protected def evalInternal(input: InternalRow): Double = rng.nextGaussian()

  def this() = this(Utils.random.nextLong())

  def this(seed: Expression) = this(seed match {
    case IntegerLiteral(s) => s
    case _ => throw new AnalysisException("Input argument to randn must be an integer literal.")
  })

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val rngTerm = ctx.freshName("rng")
    val className = classOf[XORShiftRandom].getName
    ctx.addMutableState(className, rngTerm,
      s"$rngTerm = new $className(${seed}L + org.apache.spark.TaskContext.getPartitionId());")
    ev.copy(code = s"""
      final ${ctx.javaType(dataType)} ${ev.value} = $rngTerm.nextGaussian();""", isNull = "false")
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.commons.lang3.StringUtils

import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.TypeCheckFailure
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.CalendarInterval

case class TimeWindow(
    timeColumn: Expression,
    windowDuration: Long,
    slideDuration: Long,
    startTime: Long) extends UnaryExpression
  with ImplicitCastInputTypes
  with Unevaluable
  with NonSQLExpression {

  //////////////////////////
  // SQL Constructors
  //////////////////////////

  def this(
      timeColumn: Expression,
      windowDuration: Expression,
      slideDuration: Expression,
      startTime: Expression) = {
    this(timeColumn, TimeWindow.parseExpression(windowDuration),
      TimeWindow.parseExpression(slideDuration), TimeWindow.parseExpression(startTime))
  }

  def this(timeColumn: Expression, windowDuration: Expression, slideDuration: Expression) = {
    this(timeColumn, TimeWindow.parseExpression(windowDuration),
      TimeWindow.parseExpression(slideDuration), 0)
  }

  def this(timeColumn: Expression, windowDuration: Expression) = {
    this(timeColumn, windowDuration, windowDuration)
  }

  override def child: Expression = timeColumn
  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
  override def dataType: DataType = new StructType()
    .add(StructField("start", TimestampType))
    .add(StructField("end", TimestampType))

  // This expression is replaced in the analyzer.
  override lazy val resolved = false

  
case class PreciseTimestamp(child: Expression) extends UnaryExpression with ExpectsInputTypes {
  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
  override def dataType: DataType = LongType
  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val eval = child.genCode(ctx)
    ev.copy(code = eval.code +
      s"""boolean ${ev.isNull} = ${eval.isNull};
         |${ctx.javaType(dataType)} ${ev.value} = ${eval.value};
       """.stripMargin)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.errors.attachTree
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
  extends LeafExpression {

  override def toString: String = s"input[$ordinal, ${dataType.simpleString}, $nullable]"

  // Use special getter for primitive types (for UnsafeRow)
  override def eval(input: InternalRow): Any = {
    if (input.isNullAt(ordinal)) {
      null
    } else {
      dataType match {
        case BooleanType => input.getBoolean(ordinal)
        case ByteType => input.getByte(ordinal)
        case ShortType => input.getShort(ordinal)
        case IntegerType | DateType => input.getInt(ordinal)
        case LongType | TimestampType => input.getLong(ordinal)
        case FloatType => input.getFloat(ordinal)
        case DoubleType => input.getDouble(ordinal)
        case StringType => input.getUTF8String(ordinal)
        case BinaryType => input.getBinary(ordinal)
        case CalendarIntervalType => input.getInterval(ordinal)
        case t: DecimalType => input.getDecimal(ordinal, t.precision, t.scale)
        case t: StructType => input.getStruct(ordinal, t.size)
        case _: ArrayType => input.getArray(ordinal)
        case _: MapType => input.getMap(ordinal)
        case _ => input.get(ordinal, dataType)
      }
    }
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val javaType = ctx.javaType(dataType)
    val value = ctx.getValue(ctx.INPUT_ROW, dataType, ordinal.toString)
    if (ctx.currentVars != null && ctx.currentVars(ordinal) != null) {
      val oev = ctx.currentVars(ordinal)
      ev.isNull = oev.isNull
      ev.value = oev.value
      val code = oev.code
      oev.code = ""
      ev.copy(code = code)
    } else if (nullable) {
      ev.copy(code = s"""
        boolean ${ev.isNull} = ${ctx.INPUT_ROW}.isNullAt($ordinal);
        $javaType ${ev.value} = ${ev.isNull} ? ${ctx.defaultValue(dataType)} : ($value);""")
    } else {
      ev.copy(code = s"""$javaType ${ev.value} = $value;""", isNull = "false")
    }
  }
}

object BindReferences extends Logging {

  def bindReference[A <: Expression](
      expression: A,
      input: AttributeSeq,
      allowFailures: Boolean = false): A = {
    expression.transform { case a: AttributeReference =>
      attachTree(a, "Binding attribute") {
        val ordinal = input.indexOf(a.exprId)
        if (ordinal == -1) {
          if (allowFailures) {
            a
          } else {
            sys.error(s"Couldn't find $a in ${input.attrs.mkString("[", ",", "]")}")
          }
        } else {
          BoundReference(ordinal, a.dataType, input(ordinal).nullable)
        }
      }
    }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types._


case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {

  override def nullable: Boolean = true

  override def nullSafeEval(input: Any): Any = {
    val d = input.asInstanceOf[Decimal].clone()
    if (d.changePrecision(dataType.precision, dataType.scale)) {
      d
    } else {
      null
    }
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    nullSafeCodeGen(ctx, ev, eval => {
      val tmp = ctx.freshName("tmp")
      s"""
         | Decimal $tmp = $eval.clone();
         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
         |   ${ev.value} = $tmp;
         | } else {
         |   ${ev.isNull} = true;
         | }
       """.stripMargin
    })
  }

  override def toString: String = s"CheckOverflow($child, $dataType)"

  override def sql: String = child.sql
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.expressions.objects.LambdaVariable
import org.apache.spark.sql.types.DataType


case class ReferenceToExpressions(result: Expression, children: Seq[Expression])
  extends Expression {

  override def nullable: Boolean = result.nullable
  override def dataType: DataType = result.dataType

  override def checkInputDataTypes(): TypeCheckResult = {
    if (result.references.nonEmpty) {
      return TypeCheckFailure("The result expression cannot reference to any attributes.")
    }

    var maxOrdinal = -1
    result foreach {
      case b: BoundReference if b.ordinal > maxOrdinal => maxOrdinal = b.ordinal
      case _ =>
    }
    if (maxOrdinal > children.length) {
      return TypeCheckFailure(s"The result expression need $maxOrdinal input expressions, but " +
        s"there are only ${children.length} inputs.")
    }

    TypeCheckSuccess
  }

  private lazy val projection = UnsafeProjection.create(children)

  override def eval(input: InternalRow): Any = {
    result.eval(projection(input))
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val childrenGen = children.map(_.genCode(ctx))
    val childrenVars = childrenGen.zip(children).map {
      case (childGen, child) => LambdaVariable(childGen.value, childGen.isNull, child.dataType)
    }

    val resultGen = result.transform {
      case b: BoundReference => childrenVars(b.ordinal)
    }.genCode(ctx)

    ExprCode(code = childrenGen.map(_.code).mkString("\n") + "\n" + resultGen.code,
      isNull = resultGen.isNull, value = resultGen.value)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, IntegerType}


case class BadCodegenExpression() extends LeafExpression {
  override def nullable: Boolean = false
  override def eval(input: InternalRow): Any = 10
  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ev.copy(code =
      s"""
        |int some_variable = 11;
        |int ${ev.value} = 10;
      """.stripMargin)
  }
  override def dataType: DataType = IntegerType
} 

package org.apache.spark.sql.execution

import java.util.Collections

import scala.collection.JavaConverters._

import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.Attribute
import org.apache.spark.sql.catalyst.expressions.codegen.{CodeFormatter, CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.plans.physical.Partitioning
import org.apache.spark.sql.catalyst.trees.TreeNodeRef
import org.apache.spark.util.{AccumulatorV2, LongAccumulator}


    case class ColumnMetrics() {
      val elementTypes = new SetAccumulator[String]
      sparkContext.register(elementTypes)
    }

    val tupleCount: LongAccumulator = sparkContext.longAccumulator

    val numColumns: Int = child.output.size
    val columnStats: Array[ColumnMetrics] = Array.fill(child.output.size)(new ColumnMetrics())

    def dumpStats(): Unit = {
      debugPrint(s"== ${child.simpleString} ==")
      debugPrint(s"Tuples output: ${tupleCount.value}")
      child.output.zip(columnStats).foreach { case (attr, metric) =>
        // This is called on driver. All accumulator updates have a fixed value. So it's safe to use
        // `asScala` which accesses the internal values using `java.util.Iterator`.
        val actualDataTypes = metric.elementTypes.value.asScala.mkString("{", ",", "}")
        debugPrint(s" ${attr.name} ${attr.dataType}: $actualDataTypes")
      }
    }

    protected override def doExecute(): RDD[InternalRow] = {
      child.execute().mapPartitions { iter =>
        new Iterator[InternalRow] {
          def hasNext: Boolean = iter.hasNext

          def next(): InternalRow = {
            val currentRow = iter.next()
            tupleCount.add(1)
            var i = 0
            while (i < numColumns) {
              val value = currentRow.get(i, output(i).dataType)
              if (value != null) {
                columnStats(i).elementTypes.add(value.getClass.getName)
              }
              i += 1
            }
            currentRow
          }
        }
      }
    }

    override def outputPartitioning: Partitioning = child.outputPartitioning

    override def inputRDDs(): Seq[RDD[InternalRow]] = {
      child.asInstanceOf[CodegenSupport].inputRDDs()
    }

    override def doProduce(ctx: CodegenContext): String = {
      child.asInstanceOf[CodegenSupport].produce(ctx, this)
    }

    override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
      consume(ctx, input)
    }
  }
} 

package org.apache.spark.sql.execution

import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.{expressions, InternalRow}
import org.apache.spark.sql.catalyst.expressions.{Expression, ExprId, InSet, Literal, PlanExpression}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{BooleanType, DataType, StructType}


case class ReuseSubquery(conf: SQLConf) extends Rule[SparkPlan] {

  def apply(plan: SparkPlan): SparkPlan = {
    if (!conf.exchangeReuseEnabled) {
      return plan
    }
    // Build a hash map using schema of exchanges to avoid O(N*N) sameResult calls.
    val subqueries = mutable.HashMap[StructType, ArrayBuffer[SubqueryExec]]()
    plan transformAllExpressions {
      case sub: ExecSubqueryExpression =>
        val sameSchema = subqueries.getOrElseUpdate(sub.plan.schema, ArrayBuffer[SubqueryExec]())
        val sameResult = sameSchema.find(_.sameResult(sub.plan))
        if (sameResult.isDefined) {
          sub.withNewPlan(sameResult.get)
        } else {
          sameSchema += sub.plan
          sub
        }
    }
  }
} 

package org.apache.spark.sql

import org.apache.spark.sql.catalyst.expressions.codegen.{ CodegenContext, ExprCode, CodeGenerator, JavaCode, Block }
import org.apache.spark.sql.catalyst.expressions.{ Expression, NullIntolerant, UnaryExpression }
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types.{ DataType, LongType, TimestampType }

case class TimestampToNanos(child: Expression) extends TimestampCast {
  val dataType: DataType = LongType
  protected def cast(childPrim: String): String =
    s"$childPrim * 1000L"
  override protected def nullSafeEval(input: Any): Any =
    input.asInstanceOf[Long] * 1000L
}

case class NanosToTimestamp(child: Expression) extends TimestampCast {
  val dataType: DataType = TimestampType
  protected def cast(childPrim: String): String =
    s"$childPrim / 1000L"
  override protected def nullSafeEval(input: Any): Any =
    input.asInstanceOf[Long] / 1000L
}

object TimestampToNanos {
  
  private[this] def castCode(ctx: CodegenContext, childPrim: String, childNull: String,
    resultPrim: String, resultNull: String, resultType: DataType): Block = {
    code"""
      boolean $resultNull = $childNull;
      ${CodeGenerator.javaType(resultType)} $resultPrim = ${CodeGenerator.defaultValue(resultType)};
      if (!${childNull}) {
        $resultPrim = (long) ${cast(childPrim)};
      }
    """
  }

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val eval = child.genCode(ctx)
    ev.copy(code = eval.code +
      castCode(ctx, eval.value, eval.isNull, ev.value, ev.isNull, dataType))
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.rdd.InputFileNameHolder
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.types.{DataType, StringType}
import org.apache.spark.unsafe.types.UTF8String


@ExpressionDescription(
  usage = "_FUNC_() - Returns the name of the current file being read if available",
  extended = "> SELECT _FUNC_();\n ''")
case class InputFileName() extends LeafExpression with Nondeterministic {

  override def nullable: Boolean = true

  override def dataType: DataType = StringType

  override def prettyName: String = "input_file_name"

  override protected def initInternal(): Unit = {}

  override protected def evalInternal(input: InternalRow): UTF8String = {
    InputFileNameHolder.getInputFileName()
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ev.copy(code = s"final ${ctx.javaType(dataType)} ${ev.value} = " +
      "org.apache.spark.rdd.InputFileNameHolder.getInputFileName();", isNull = "false")
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodeGenerator, ExprCode, FalseLiteral}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types.{DataType, LongType}


  @transient private[this] var count: Long = _

  @transient private[this] var partitionMask: Long = _

  override protected def initializeInternal(partitionIndex: Int): Unit = {
    count = 0L
    partitionMask = partitionIndex.toLong << 33
  }

  override def nullable: Boolean = false

  override def dataType: DataType = LongType

  override protected def evalInternal(input: InternalRow): Long = {
    val currentCount = count
    count += 1
    partitionMask + currentCount
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val countTerm = ctx.addMutableState(CodeGenerator.JAVA_LONG, "count")
    val partitionMaskTerm = "partitionMask"
    ctx.addImmutableStateIfNotExists(CodeGenerator.JAVA_LONG, partitionMaskTerm)
    ctx.addPartitionInitializationStatement(s"$countTerm = 0L;")
    ctx.addPartitionInitializationStatement(s"$partitionMaskTerm = ((long) partitionIndex) << 33;")

    ev.copy(code = code"""
      final ${CodeGenerator.javaType(dataType)} ${ev.value} = $partitionMaskTerm + $countTerm;
      $countTerm++;""", isNull = FalseLiteral)
  }

  override def prettyName: String = "monotonically_increasing_id"

  override def sql: String = s"$prettyName()"

  override def freshCopy(): MonotonicallyIncreasingID = MonotonicallyIncreasingID()
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodeGenerator, ExprCode, FalseLiteral}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types._
import org.apache.spark.util.Utils
import org.apache.spark.util.random.XORShiftRandom


// scalastyle:off line.size.limit
@ExpressionDescription(
  usage = """_FUNC_([seed]) - Returns a random value with independent and identically distributed (i.i.d.) values drawn from the standard normal distribution.""",
  examples = """
    Examples:
      > SELECT _FUNC_();
       -0.3254147983080288
      > SELECT _FUNC_(0);
       1.1164209726833079
      > SELECT _FUNC_(null);
       1.1164209726833079
  """,
  note = "The function is non-deterministic in general case.")
// scalastyle:on line.size.limit
case class Randn(child: Expression) extends RDG with ExpressionWithRandomSeed {

  def this() = this(Literal(Utils.random.nextLong(), LongType))

  override def withNewSeed(seed: Long): Randn = Randn(Literal(seed, LongType))

  override protected def evalInternal(input: InternalRow): Double = rng.nextGaussian()

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val className = classOf[XORShiftRandom].getName
    val rngTerm = ctx.addMutableState(className, "rng")
    ctx.addPartitionInitializationStatement(
      s"$rngTerm = new $className(${seed}L + partitionIndex);")
    ev.copy(code = code"""
      final ${CodeGenerator.javaType(dataType)} ${ev.value} = $rngTerm.nextGaussian();""",
      isNull = FalseLiteral)
  }

  override def freshCopy(): Randn = Randn(child)
}

object Randn {
  def apply(seed: Long): Randn = Randn(Literal(seed, LongType))
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.commons.lang3.StringUtils

import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.TypeCheckFailure
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodeGenerator, ExprCode}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.CalendarInterval

case class TimeWindow(
    timeColumn: Expression,
    windowDuration: Long,
    slideDuration: Long,
    startTime: Long) extends UnaryExpression
  with ImplicitCastInputTypes
  with Unevaluable
  with NonSQLExpression {

  //////////////////////////
  // SQL Constructors
  //////////////////////////

  def this(
      timeColumn: Expression,
      windowDuration: Expression,
      slideDuration: Expression,
      startTime: Expression) = {
    this(timeColumn, TimeWindow.parseExpression(windowDuration),
      TimeWindow.parseExpression(slideDuration), TimeWindow.parseExpression(startTime))
  }

  def this(timeColumn: Expression, windowDuration: Expression, slideDuration: Expression) = {
    this(timeColumn, TimeWindow.parseExpression(windowDuration),
      TimeWindow.parseExpression(slideDuration), 0)
  }

  def this(timeColumn: Expression, windowDuration: Expression) = {
    this(timeColumn, windowDuration, windowDuration)
  }

  override def child: Expression = timeColumn
  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
  override def dataType: DataType = new StructType()
    .add(StructField("start", TimestampType))
    .add(StructField("end", TimestampType))

  // This expression is replaced in the analyzer.
  override lazy val resolved = false

  
case class PreciseTimestampConversion(
    child: Expression,
    fromType: DataType,
    toType: DataType) extends UnaryExpression with ExpectsInputTypes {
  override def inputTypes: Seq[AbstractDataType] = Seq(fromType)
  override def dataType: DataType = toType
  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    val eval = child.genCode(ctx)
    ev.copy(code = eval.code +
      code"""boolean ${ev.isNull} = ${eval.isNull};
         |${CodeGenerator.javaType(dataType)} ${ev.value} = ${eval.value};
       """.stripMargin)
  }
  override def nullSafeEval(input: Any): Any = input
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode, FalseLiteral}
import org.apache.spark.sql.types.DataType

case class KnownNotNull(child: Expression) extends UnaryExpression {
  override def nullable: Boolean = false
  override def dataType: DataType = child.dataType

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    child.genCode(ctx).copy(isNull = FalseLiteral)
  }

  override def eval(input: InternalRow): Any = {
    child.eval(input)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.errors.attachTree
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodeGenerator, ExprCode, FalseLiteral, JavaCode}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types._


case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
  extends LeafExpression {

  override def toString: String = s"input[$ordinal, ${dataType.simpleString}, $nullable]"

  private val accessor: (InternalRow, Int) => Any = InternalRow.getAccessor(dataType)

  // Use special getter for primitive types (for UnsafeRow)
  override def eval(input: InternalRow): Any = {
    if (nullable && input.isNullAt(ordinal)) {
      null
    } else {
      accessor(input, ordinal)
    }
  }

  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    if (ctx.currentVars != null && ctx.currentVars(ordinal) != null) {
      val oev = ctx.currentVars(ordinal)
      ev.isNull = oev.isNull
      ev.value = oev.value
      ev.copy(code = oev.code)
    } else {
      assert(ctx.INPUT_ROW != null, "INPUT_ROW and currentVars cannot both be null.")
      val javaType = JavaCode.javaType(dataType)
      val value = CodeGenerator.getValue(ctx.INPUT_ROW, dataType, ordinal.toString)
      if (nullable) {
        ev.copy(code =
          code"""
             |boolean ${ev.isNull} = ${ctx.INPUT_ROW}.isNullAt($ordinal);
             |$javaType ${ev.value} = ${ev.isNull} ?
             |  ${CodeGenerator.defaultValue(dataType)} : ($value);
           """.stripMargin)
      } else {
        ev.copy(code = code"$javaType ${ev.value} = $value;", isNull = FalseLiteral)
      }
    }
  }
}

object BindReferences extends Logging {

  def bindReference[A <: Expression](
      expression: A,
      input: AttributeSeq,
      allowFailures: Boolean = false): A = {
    expression.transform { case a: AttributeReference =>
      attachTree(a, "Binding attribute") {
        val ordinal = input.indexOf(a.exprId)
        if (ordinal == -1) {
          if (allowFailures) {
            a
          } else {
            sys.error(s"Couldn't find $a in ${input.attrs.mkString("[", ",", "]")}")
          }
        } else {
          BoundReference(ordinal, a.dataType, input(ordinal).nullable)
        }
      }
    }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, EmptyBlock, ExprCode}
import org.apache.spark.sql.types._


case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {

  override def nullable: Boolean = true

  override def nullSafeEval(input: Any): Any =
    input.asInstanceOf[Decimal].toPrecision(dataType.precision, dataType.scale)

  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    nullSafeCodeGen(ctx, ev, eval => {
      val tmp = ctx.freshName("tmp")
      s"""
         | Decimal $tmp = $eval.clone();
         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
         |   ${ev.value} = $tmp;
         | } else {
         |   ${ev.isNull} = true;
         | }
       """.stripMargin
    })
  }

  override def toString: String = s"CheckOverflow($child, $dataType)"

  override def sql: String = child.sql
} 

package org.apache.spark.sql.catalyst.expressions

import java.util.Locale

import org.apache.spark.{SparkException, SparkFunSuite}
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
import org.apache.spark.sql.types.{IntegerType, StringType}

class ScalaUDFSuite extends SparkFunSuite with ExpressionEvalHelper {

  test("basic") {
    val intUdf = ScalaUDF((i: Int) => i + 1, IntegerType, Literal(1) :: Nil, true :: Nil)
    checkEvaluation(intUdf, 2)

    val stringUdf = ScalaUDF((s: String) => s + "x", StringType, Literal("a") :: Nil, true :: Nil)
    checkEvaluation(stringUdf, "ax")
  }

  test("better error message for NPE") {
    val udf = ScalaUDF(
      (s: String) => s.toLowerCase(Locale.ROOT),
      StringType,
      Literal.create(null, StringType) :: Nil,
      true :: Nil)

    val e1 = intercept[SparkException](udf.eval())
    assert(e1.getMessage.contains("Failed to execute user defined function"))

    val e2 = intercept[SparkException] {
      checkEvaluationWithUnsafeProjection(udf, null)
    }
    assert(e2.getMessage.contains("Failed to execute user defined function"))
  }

  test("SPARK-22695: ScalaUDF should not use global variables") {
    val ctx = new CodegenContext
    ScalaUDF((s: String) => s + "x", StringType, Literal("a") :: Nil, true :: Nil).genCode(ctx)
    assert(ctx.inlinedMutableStates.isEmpty)
  }
} 

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.types.{DataType, IntegerType}


case class BadCodegenExpression() extends LeafExpression {
  override def nullable: Boolean = false
  override def eval(input: InternalRow): Any = 10
  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ev.copy(code =
      code"""
        |int some_variable = 11;
        |int ${ev.value} = 10;
      """.stripMargin)
  }
  override def dataType: DataType = IntegerType
} 

package org.apache.spark.sql.execution

import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.{expressions, InternalRow}
import org.apache.spark.sql.catalyst.expressions.{Expression, ExprId, InSet, Literal, PlanExpression}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{BooleanType, DataType, StructType}


case class ReuseSubquery(conf: SQLConf) extends Rule[SparkPlan] {

  def apply(plan: SparkPlan): SparkPlan = {
    if (!conf.exchangeReuseEnabled) {
      return plan
    }
    // Build a hash map using schema of subqueries to avoid O(N*N) sameResult calls.
    val subqueries = mutable.HashMap[StructType, ArrayBuffer[SubqueryExec]]()
    plan transformAllExpressions {
      case sub: ExecSubqueryExpression =>
        val sameSchema = subqueries.getOrElseUpdate(sub.plan.schema, ArrayBuffer[SubqueryExec]())
        val sameResult = sameSchema.find(_.sameResult(sub.plan))
        if (sameResult.isDefined) {
          sub.withNewPlan(sameResult.get)
        } else {
          sameSchema += sub.plan
          sub
        }
    }
  }
} 

package com.intel.sparkColumnarPlugin.expression

import org.apache.arrow.gandiva.evaluator._
import org.apache.arrow.gandiva.exceptions.GandivaException
import org.apache.arrow.gandiva.expression._
import org.apache.arrow.vector.types.pojo.ArrowType
import org.apache.arrow.vector.types.pojo.Field

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.{expressions, InternalRow}
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
import org.apache.spark.sql.execution.BaseSubqueryExec
import org.apache.spark.sql.execution.ExecSubqueryExpression
import org.apache.spark.sql.execution.ScalarSubquery
import org.apache.spark.sql.types._

import scala.collection.mutable.ListBuffer

class ColumnarScalarSubquery(
  query: ScalarSubquery)
  extends Expression with ColumnarExpression {

  override def dataType: DataType = query.dataType
  override def children: Seq[Expression] = Nil
  override def nullable: Boolean = true
  override def toString: String = query.toString
  override def eval(input: InternalRow): Any = query.eval(input)
  override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = query.doGenCode(ctx, ev)
  override def canEqual(that: Any): Boolean = query.canEqual(that)
  override def productArity: Int = query.productArity
  override def productElement(n: Int): Any = query.productElement(n)
  override def doColumnarCodeGen(args: java.lang.Object): (TreeNode, ArrowType) = {
    val value = query.eval(null)
    val resultType = CodeGeneration.getResultType(query.dataType)
    query.dataType match {
      case t: StringType =>
        (TreeBuilder.makeStringLiteral(value.toString().asInstanceOf[String]), resultType)
      case t: IntegerType =>
        (TreeBuilder.makeLiteral(value.asInstanceOf[Integer]), resultType)
      case t: LongType =>
        (TreeBuilder.makeLiteral(value.asInstanceOf[java.lang.Long]), resultType)
      case t: DoubleType =>
        (TreeBuilder.makeLiteral(value.asInstanceOf[java.lang.Double]), resultType)
      case d: DecimalType =>
        val v = value.asInstanceOf[Decimal]
        (TreeBuilder.makeDecimalLiteral(v.toString, v.precision, v.scale), resultType)
      case d: DateType =>
        throw new UnsupportedOperationException(s"DateType is not supported yet.")
    }
  }
}