Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/20023#discussion_r161656633 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala --- @@ -136,10 +137,52 @@ object DecimalType extends AbstractDataType { case DoubleType => DoubleDecimal } + private[sql] def forLiteral(literal: Literal): DecimalType = literal.value match { + case v: Short => fromBigDecimal(BigDecimal(v)) + case v: Int => fromBigDecimal(BigDecimal(v)) + case v: Long => fromBigDecimal(BigDecimal(v)) + case _ => forType(literal.dataType) + } + + private[sql] def fromBigDecimal(d: BigDecimal): DecimalType = { + DecimalType(Math.max(d.precision, d.scale), d.scale) + } + private[sql] def bounded(precision: Int, scale: Int): DecimalType = { DecimalType(min(precision, MAX_PRECISION), min(scale, MAX_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. + * + * @param precision + * @param scale + * @return + */ + private[sql] def adjustPrecisionScale(precision: Int, scale: Int): DecimalType = { --- End diff -- So the rule in document is ``` val resultPrecision = 38 if (intDigits < 32) { // This means scale > 6, as iniDigits = precision - scale and precision > 38 val maxScale = 38 - intDigits val resultScale = min(scale, maxScale) } else { if (scale < 6) { // can't round as scale is already small val resultScale = scale } else { val resltScale = 6 } } ``` I think this is a little different from the current rule ``` val minScaleValue = Math.min(scale, 6) val resultScale = max(38 - intDigits, minScaleValue) ``` Think aboout the case `iniDigits < 32`, SQL server is `min(scale, 38 - intDigits)`, we are `38 - intDigits`
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