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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
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
/**
* A Random distribution generating expression.
* TODO: This can be made generic to generate any type of random distribution, or any type of
* StructType.
*
* Since this expression is stateful, it cannot be a case object.
*/
abstract class RDG extends LeafExpression with Nondeterministic {
protected def seed: Long
/**
* Record ID within each partition. By being transient, the Random Number Generator is
* reset every time we serialize and deserialize and initialize it.
*/
@transient protected var rng: XORShiftRandom = _
override protected def initInternal(): Unit = {
rng = new XORShiftRandom(seed + TaskContext.getPartitionId)
}
override def nullable: Boolean = false
override def dataType: DataType = DoubleType
// NOTE: Even if the user doesn't provide a seed, Spark SQL adds a default seed.
override def sql: String = s"$prettyName($seed)"
}
/** Generate a random column with i.i.d. uniformly distributed values in [0, 1). */
@ExpressionDescription(
usage = "_FUNC_(a) - Returns a random column with i.i.d. uniformly distributed values in [0, 1).")
case class Rand(seed: Long) extends RDG {
override protected def evalInternal(input: InternalRow): Double = rng.nextDouble()
def this() = this(Utils.random.nextLong())
def this(seed: Expression) = this(seed match {
case IntegerLiteral(s) => s
case _ => throw new AnalysisException("Input argument to rand must be an integer literal.")
})
override def genCode(ctx: CodegenContext, ev: ExprCode): String = {
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.isNull = "false"
s"""
final ${ctx.javaType(dataType)} ${ev.value} = $rngTerm.nextDouble();
"""
}
}
/** Generate a random column with i.i.d. gaussian random distribution. */
@ExpressionDescription(
usage = "_FUNC_(a) - Returns a random column with i.i.d. gaussian random distribution.")
case class Randn(seed: Long) extends RDG {
override protected def evalInternal(input: InternalRow): Double = rng.nextGaussian()
def this() = this(Utils.random.nextLong())
def this(seed: Expression) = this(seed match {
case IntegerLiteral(s) => s
case _ => throw new AnalysisException("Input argument to randn must be an integer literal.")
})
override def genCode(ctx: CodegenContext, ev: ExprCode): String = {
val rngTerm = ctx.freshName("rng")
val className = classOf[XORShiftRandom].getName
ctx.addMutableState(className, rngTerm,
s"$rngTerm = new $className(${seed}L + org.apache.spark.TaskContext.getPartitionId());")
ev.isNull = "false"
s"""
final ${ctx.javaType(dataType)} ${ev.value} = $rngTerm.nextGaussian();
"""
}
}
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