Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/10882#discussion_r50617303
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala
---
@@ -0,0 +1,261 @@
+/*
+ * 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.analysis
+
+import org.apache.spark.sql.catalyst.expressions.Literal._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.types._
+
+
+// scalastyle:off
+/**
+ * Calculates and propagates precision for fixed-precision decimals. Hive
has a number of
+ * rules for this based on the SQL standard and MS SQL:
+ *
https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf
+ * https://msdn.microsoft.com/en-us/library/ms190476.aspx
+ *
+ * In particular, if we have expressions e1 and e2 with precision/scale
p1/s2 and p2/s2
+ * respectively, then the following operations have the following
precision / scale:
+ *
+ * Operation Result Precision Result Scale
+ *
------------------------------------------------------------------------
+ * e1 + e2 max(s1, s2) + max(p1-s1, p2-s2) + 1 max(s1, s2)
+ * e1 - e2 max(s1, s2) + max(p1-s1, p2-s2) + 1 max(s1, s2)
+ * e1 * e2 p1 + p2 + 1 s1 + s2
+ * e1 / e2 p1 - s1 + s2 + max(6, s1 + p2 + 1) max(6, s1 + p2 +
1)
+ * e1 % e2 min(p1-s1, p2-s2) + max(s1, s2) max(s1, s2)
+ * e1 union e2 max(s1, s2) + max(p1-s1, p2-s2) max(s1, s2)
+ * sum(e1) p1 + 10 s1
+ * avg(e1) p1 + 4 s1 + 4
+ *
+ * Catalyst also has unlimited-precision decimals. For those, all ops
return unlimited precision.
+ *
+ * To implement the rules for fixed-precision types, we introduce casts to
turn them to unlimited
+ * precision, do the math on unlimited-precision numbers, then introduce
casts back to the
+ * required fixed precision. This allows us to do all rounding and
overflow handling in the
+ * cast-to-fixed-precision operator.
+ *
+ * In addition, when mixing non-decimal types with decimals, we use the
following rules:
+ * - BYTE gets turned into DECIMAL(3, 0)
+ * - SHORT gets turned into DECIMAL(5, 0)
+ * - INT gets turned into DECIMAL(10, 0)
+ * - LONG gets turned into DECIMAL(20, 0)
+ * - FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE
+ */
+// scalastyle:on
+object DecimalPrecision extends Rule[LogicalPlan] {
+ import scala.math.{max, min}
+
+ private def isFloat(t: DataType): Boolean = t == FloatType || t ==
DoubleType
+
+ // Returns the wider decimal type that's wider than both of them
+ def widerDecimalType(d1: DecimalType, d2: DecimalType): DecimalType = {
+ widerDecimalType(d1.precision, d1.scale, d2.precision, d2.scale)
+ }
+ // max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2)
+ def widerDecimalType(p1: Int, s1: Int, p2: Int, s2: Int): DecimalType = {
+ val scale = max(s1, s2)
+ val range = max(p1 - s1, p2 - s2)
+ DecimalType.bounded(range + scale, scale)
+ }
+
+ private def promotePrecision(e: Expression, dataType: DataType):
Expression = {
+ PromotePrecision(Cast(e, dataType))
+ }
+
+ def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+ // fix decimal precision for expressions
+ case q => q.transformExpressions(
+
decimalAndDecimal.orElse(integralAndDecimalLiteral).orElse(nondecimalAndDecimal))
+ }
+
+ /** Decimal precision promotion for +, -, *, /, %, pmod, and binary
comparison. */
+ private val decimalAndDecimal: PartialFunction[Expression, Expression] =
{
+ // Skip nodes whose children have not been resolved yet
+ case e if !e.childrenResolved => e
+
+ // Skip nodes who is already promoted
+ case e: BinaryArithmetic if e.left.isInstanceOf[PromotePrecision] => e
+
+ case Add(e1 @ DecimalType.Expression(p1, s1), e2 @
DecimalType.Expression(p2, s2)) =>
+ val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) +
1, max(s1, s2))
+ CheckOverflow(Add(promotePrecision(e1, dt), promotePrecision(e2,
dt)), dt)
+
+ case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @
DecimalType.Expression(p2, s2)) =>
+ val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) +
1, max(s1, s2))
+ CheckOverflow(Subtract(promotePrecision(e1, dt),
promotePrecision(e2, dt)), dt)
+
+ case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @
DecimalType.Expression(p2, s2)) =>
+ val resultType = DecimalType.bounded(p1 + p2 + 1, s1 + s2)
+ val widerType = widerDecimalType(p1, s1, p2, s2)
+ CheckOverflow(Multiply(promotePrecision(e1, widerType),
promotePrecision(e2, widerType)),
+ resultType)
+
+ case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @
DecimalType.Expression(p2, s2)) =>
+ var intDig = min(DecimalType.MAX_SCALE, p1 - s1 + s2)
+ var decDig = min(DecimalType.MAX_SCALE, max(6, s1 + p2 + 1))
+ val diff = (intDig + decDig) - DecimalType.MAX_SCALE
+ if (diff > 0) {
+ decDig -= diff / 2 + 1
+ intDig = DecimalType.MAX_SCALE - decDig
+ }
+ val resultType = DecimalType.bounded(intDig + decDig, decDig)
+ val widerType = widerDecimalType(p1, s1, p2, s2)
+ CheckOverflow(Divide(promotePrecision(e1, widerType),
promotePrecision(e2, widerType)),
+ resultType)
+
+ case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @
DecimalType.Expression(p2, s2)) =>
+ val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1,
s2), max(s1, s2))
+ // resultType may have lower precision, so we cast them into wider
type first.
+ val widerType = widerDecimalType(p1, s1, p2, s2)
+ CheckOverflow(Remainder(promotePrecision(e1, widerType),
promotePrecision(e2, widerType)),
+ resultType)
+
+ case Pmod(e1 @ DecimalType.Expression(p1, s1), e2 @
DecimalType.Expression(p2, s2)) =>
+ val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1,
s2), max(s1, s2))
+ // resultType may have lower precision, so we cast them into wider
type first.
+ val widerType = widerDecimalType(p1, s1, p2, s2)
+ CheckOverflow(Pmod(promotePrecision(e1, widerType),
promotePrecision(e2, widerType)),
+ resultType)
+
+ case b @ BinaryComparison(e1 @ DecimalType.Expression(p1, s1),
+ e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
+ val resultType = widerDecimalType(p1, s1, p2, s2)
+ b.makeCopy(Array(Cast(e1, resultType), Cast(e2, resultType)))
+
+ // TODO: MaxOf, MinOf, etc might want other rules
+ // SUM and AVERAGE are handled by the implementations of those
expressions
+ }
+
+ /**
+ * Strength reduction for comparing integral expressions with decimal
literals.
+ * 1. int_col > decimal_literal => int_col > floor(decimal_literal)
+ * 2. int_col >= decimal_literal => int_col >= ceil(decimal_literal)
+ * 3. int_col < decimal_literal => int_col < ceil(decimal_literal)
+ * 4. int_col <= decimal_literal => int_col <= floor(decimal_literal)
+ * 5. decimal_literal > int_col => ceil(decimal_literal) > int_col
+ * 6. decimal_literal >= int_col => floor(decimal_literal) >= int_col
+ * 7. decimal_literal < int_col => floor(decimal_literal) < int_col
+ * 8. decimal_literal <= int_col => ceil(decimal_literal) <= int_col
+ *
+ * Note that technically this is an "optimization" and should go into
the optimizer. However,
+ * by the time the optimizer runs, these comparison expressions would be
pretty hard to pattern
+ * match because there are multuple (at least 2) levels of casts
involved.
+ *
+ * There are a lot more possible rules we can implement, but we don't do
them
+ * because we are not sure how common they are.
+ */
+ private val integralAndDecimalLiteral: PartialFunction[Expression,
Expression] = {
--- End diff --
this is the only function in this file that is new. everything else was
simply copied over from the existing DeicmalPrecision.
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