spark DecimalType 源码

spark DecimalType 代码

文件路径：/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala

/*
 * 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.types

import java.util.Locale

import scala.annotation.tailrec
import scala.reflect.runtime.universe.typeTag

import org.apache.spark.annotation.Stable
import org.apache.spark.sql.catalyst.expressions.{Expression, Literal}
import org.apache.spark.sql.errors.QueryCompilationErrors
import org.apache.spark.sql.internal.SQLConf

/**
 * The data type representing `java.math.BigDecimal` values.
 * A Decimal that must have fixed precision (the maximum number of digits) and scale (the number
 * of digits on right side of dot).
 *
 * The precision can be up to 38, scale can also be up to 38 (less or equal to precision).
 *
 * The default precision and scale is (10, 0).
 *
 * Please use `DataTypes.createDecimalType()` to create a specific instance.
 *
 * @since 1.3.0
 */
@Stable
case class DecimalType(precision: Int, scale: Int) extends FractionalType {

  DecimalType.checkNegativeScale(scale)

  if (scale > precision) {
    throw QueryCompilationErrors.decimalCannotGreaterThanPrecisionError(scale, precision)
  }

  if (precision > DecimalType.MAX_PRECISION) {
    throw QueryCompilationErrors.decimalOnlySupportPrecisionUptoError(
      DecimalType.simpleString, DecimalType.MAX_PRECISION)
  }

  // default constructor for Java
  def this(precision: Int) = this(precision, 0)
  def this() = this(10)

  private[sql] type InternalType = Decimal
  @transient private[sql] lazy val tag = typeTag[InternalType]
  private[sql] val numeric = Decimal.DecimalIsFractional
  private[sql] val fractional = Decimal.DecimalIsFractional
  private[sql] val ordering = Decimal.DecimalIsFractional
  private[sql] val asIntegral = Decimal.DecimalAsIfIntegral

  override private[sql] def exactNumeric = DecimalExactNumeric

  override def typeName: String = s"decimal($precision,$scale)"

  override def toString: String = s"DecimalType($precision,$scale)"

  override def sql: String = typeName.toUpperCase(Locale.ROOT)

  /**
   * Returns whether this DecimalType is wider than `other`. If yes, it means `other`
   * can be casted into `this` safely without losing any precision or range.
   */
  private[sql] def isWiderThan(other: DataType): Boolean = isWiderThanInternal(other)

  @tailrec
  private def isWiderThanInternal(other: DataType): Boolean = other match {
    case dt: DecimalType =>
      (precision - scale) >= (dt.precision - dt.scale) && scale >= dt.scale
    case dt: IntegralType =>
      isWiderThanInternal(DecimalType.forType(dt))
    case _ => false
  }

  /**
   * Returns whether this DecimalType is tighter than `other`. If yes, it means `this`
   * can be casted into `other` safely without losing any precision or range.
   */
  private[sql] def isTighterThan(other: DataType): Boolean = other match {
    case dt: DecimalType =>
      (precision - scale) <= (dt.precision - dt.scale) && scale <= dt.scale
    case dt: IntegralType =>
      val integerAsDecimal = DecimalType.forType(dt)
      assert(integerAsDecimal.scale == 0)
      // If the precision equals `integerAsDecimal.precision`, there can be integer overflow
      // during casting.
      precision < integerAsDecimal.precision && scale == 0
    case _ => false
  }

  /**
   * The default size of a value of the DecimalType is 8 bytes when precision is at most 18,
   * and 16 bytes otherwise.
   */
  override def defaultSize: Int = if (precision <= Decimal.MAX_LONG_DIGITS) 8 else 16

  override def simpleString: String = s"decimal($precision,$scale)"

  private[spark] override def asNullable: DecimalType = this
}


/**
 * Extra factory methods and pattern matchers for Decimals.
 *
 * @since 1.3.0
 */
@Stable
object DecimalType extends AbstractDataType {
  import scala.math.min

  val MAX_PRECISION = 38
  val MAX_SCALE = 38
  val DEFAULT_SCALE = 18
  val SYSTEM_DEFAULT: DecimalType = DecimalType(MAX_PRECISION, DEFAULT_SCALE)
  val USER_DEFAULT: DecimalType = DecimalType(10, 0)
  val MINIMUM_ADJUSTED_SCALE = 6

  // The decimal types compatible with other numeric types
  private[sql] val BooleanDecimal = DecimalType(1, 0)
  private[sql] val ByteDecimal = DecimalType(3, 0)
  private[sql] val ShortDecimal = DecimalType(5, 0)
  private[sql] val IntDecimal = DecimalType(10, 0)
  private[sql] val LongDecimal = DecimalType(20, 0)
  private[sql] val FloatDecimal = DecimalType(14, 7)
  private[sql] val DoubleDecimal = DecimalType(30, 15)
  private[sql] val BigIntDecimal = DecimalType(38, 0)

  private[sql] def forType(dataType: DataType): DecimalType = dataType match {
    case ByteType => ByteDecimal
    case ShortType => ShortDecimal
    case IntegerType => IntDecimal
    case LongType => LongDecimal
    case FloatType => FloatDecimal
    case DoubleType => DoubleDecimal
  }

  private[sql] def fromLiteral(literal: Literal): DecimalType = literal.value match {
    case v: Short => fromDecimal(Decimal(BigDecimal(v)))
    case v: Int => fromDecimal(Decimal(BigDecimal(v)))
    case v: Long => fromDecimal(Decimal(BigDecimal(v)))
    case _ => forType(literal.dataType)
  }

  private[sql] def fromDecimal(d: Decimal): DecimalType = DecimalType(d.precision, d.scale)

  private[sql] def bounded(precision: Int, scale: Int): DecimalType = {
    DecimalType(min(precision, MAX_PRECISION), min(scale, MAX_SCALE))
  }

  private[sql] def checkNegativeScale(scale: Int): Unit = {
    if (scale < 0 && !SQLConf.get.allowNegativeScaleOfDecimalEnabled) {
      throw QueryCompilationErrors.negativeScaleNotAllowedError(scale)
    }
  }

  /**
   * Scale adjustment implementation is based on Hive's one, which is itself inspired to
   * SQLServer's one. In particular, when a result precision is greater than
   * {@link #MAX_PRECISION}, the corresponding scale is reduced to prevent the integral part of a
   * result from being truncated.
   *
   * This method is used only when `spark.sql.decimalOperations.allowPrecisionLoss` is set to true.
   */
  private[sql] def adjustPrecisionScale(precision: Int, scale: Int): DecimalType = {
    // Assumptions:
    checkNegativeScale(scale)
    assert(precision >= scale)

    if (precision <= MAX_PRECISION) {
      // Adjustment only needed when we exceed max precision
      DecimalType(precision, scale)
    } else if (scale < 0) {
      // Decimal can have negative scale (SPARK-24468). In this case, we cannot allow a precision
      // loss since we would cause a loss of digits in the integer part.
      // In this case, we are likely to meet an overflow.
      DecimalType(MAX_PRECISION, scale)
    } else {
      // Precision/scale exceed maximum precision. Result must be adjusted to MAX_PRECISION.
      val intDigits = precision - scale
      // If original scale is less than MINIMUM_ADJUSTED_SCALE, use original scale value; otherwise
      // preserve at least MINIMUM_ADJUSTED_SCALE fractional digits
      val minScaleValue = Math.min(scale, MINIMUM_ADJUSTED_SCALE)
      // The resulting scale is the maximum between what is available without causing a loss of
      // digits for the integer part of the decimal and the minimum guaranteed scale, which is
      // computed above
      val adjustedScale = Math.max(MAX_PRECISION - intDigits, minScaleValue)

      DecimalType(MAX_PRECISION, adjustedScale)
    }
  }

  override private[sql] def defaultConcreteType: DataType = SYSTEM_DEFAULT

  override private[sql] def acceptsType(other: DataType): Boolean = {
    other.isInstanceOf[DecimalType]
  }

  override private[sql] def simpleString: String = "decimal"

  private[sql] object Fixed {
    def unapply(t: DecimalType): Option[(Int, Int)] = Some((t.precision, t.scale))
  }

  private[sql] object Expression {
    def unapply(e: Expression): Option[(Int, Int)] = e.dataType match {
      case t: DecimalType => Some((t.precision, t.scale))
      case _ => None
    }
  }

  /**
   * Returns if dt is a DecimalType that fits inside an int
   */
  def is32BitDecimalType(dt: DataType): Boolean = {
    dt match {
      case t: DecimalType =>
        t.precision <= Decimal.MAX_INT_DIGITS
      case _ => false
    }
  }

  /**
   * Returns if dt is a DecimalType that fits inside a long
   */
  def is64BitDecimalType(dt: DataType): Boolean = {
    dt match {
      case t: DecimalType =>
        t.precision <= Decimal.MAX_LONG_DIGITS
      case _ => false
    }
  }

  /**
   * Returns if dt is a DecimalType that doesn't fit inside a long
   */
  def isByteArrayDecimalType(dt: DataType): Boolean = {
    dt match {
      case t: DecimalType =>
        t.precision > Decimal.MAX_LONG_DIGITS
      case _ => false
    }
  }

  def unapply(t: DataType): Boolean = t.isInstanceOf[DecimalType]

  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[DecimalType]
}

相关信息

spark 源码目录

相关文章

spark AbstractDataType 源码

spark ArrayType 源码

spark BinaryType 源码

spark BooleanType 源码

spark ByteType 源码

spark CalendarIntervalType 源码

spark CharType 源码

spark DataType 源码

spark DateType 源码

spark DayTimeIntervalType 源码