/
TypeCoercion.scala
708 lines (623 loc) · 31.4 KB
/
TypeCoercion.scala
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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.analysis
import javax.annotation.Nullable
import scala.annotation.tailrec
import scala.collection.mutable
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.types._
/**
* A collection of [[Rule]] that can be used to coerce differing types that participate in
* operations into compatible ones.
*
* Notes about type widening / tightest common types: Broadly, there are two cases when we need
* to widen data types (e.g. union, binary comparison). In case 1, we are looking for a common
* data type for two or more data types, and in this case no loss of precision is allowed. Examples
* include type inference in JSON (e.g. what's the column's data type if one row is an integer
* while the other row is a long?). In case 2, we are looking for a widened data type with
* some acceptable loss of precision (e.g. there is no common type for double and decimal because
* double's range is larger than decimal, and yet decimal is more precise than double, but in
* union we would cast the decimal into double).
*/
object TypeCoercion {
val typeCoercionRules =
PropagateTypes ::
InConversion ::
WidenSetOperationTypes ::
PromoteStrings ::
DecimalPrecision ::
BooleanEquality ::
FunctionArgumentConversion ::
CaseWhenCoercion ::
IfCoercion ::
Division ::
PropagateTypes ::
ImplicitTypeCasts ::
DateTimeOperations ::
Nil
// See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
// The conversion for integral and floating point types have a linear widening hierarchy:
val numericPrecedence =
IndexedSeq(
ByteType,
ShortType,
IntegerType,
LongType,
FloatType,
DoubleType)
/**
* Case 1 type widening (see the classdoc comment above for TypeCoercion).
*
* Find the tightest common type of two types that might be used in a binary expression.
* This handles all numeric types except fixed-precision decimals interacting with each other or
* with primitive types, because in that case the precision and scale of the result depends on
* the operation. Those rules are implemented in [[DecimalPrecision]].
*/
val findTightestCommonTypeOfTwo: (DataType, DataType) => Option[DataType] = {
case (t1, t2) if t1 == t2 => Some(t1)
case (NullType, t1) => Some(t1)
case (t1, NullType) => Some(t1)
case (t1: IntegralType, t2: DecimalType) if t2.isWiderThan(t1) =>
Some(t2)
case (t1: DecimalType, t2: IntegralType) if t1.isWiderThan(t2) =>
Some(t1)
// Promote numeric types to the highest of the two
case (t1: NumericType, t2: NumericType)
if !t1.isInstanceOf[DecimalType] && !t2.isInstanceOf[DecimalType] =>
val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2)
Some(numericPrecedence(index))
case (_: TimestampType, _: DateType) | (_: DateType, _: TimestampType) =>
Some(TimestampType)
case _ => None
}
/** Similar to [[findTightestCommonType]], but can promote all the way to StringType. */
def findTightestCommonTypeToString(left: DataType, right: DataType): Option[DataType] = {
findTightestCommonTypeOfTwo(left, right).orElse((left, right) match {
case (StringType, t2: AtomicType) if t2 != BinaryType && t2 != BooleanType => Some(StringType)
case (t1: AtomicType, StringType) if t1 != BinaryType && t1 != BooleanType => Some(StringType)
case _ => None
})
}
/**
* Find the tightest common type of a set of types by continuously applying
* `findTightestCommonTypeOfTwo` on these types.
*/
private def findTightestCommonType(types: Seq[DataType]): Option[DataType] = {
types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match {
case None => None
case Some(d) => findTightestCommonTypeOfTwo(d, c)
})
}
/**
* Case 2 type widening (see the classdoc comment above for TypeCoercion).
*
* i.e. the main difference with [[findTightestCommonTypeOfTwo]] is that here we allow some
* loss of precision when widening decimal and double.
*/
private def findWiderTypeForTwo(t1: DataType, t2: DataType): Option[DataType] = (t1, t2) match {
case (t1: DecimalType, t2: DecimalType) =>
Some(DecimalPrecision.widerDecimalType(t1, t2))
case (t: IntegralType, d: DecimalType) =>
Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
case (d: DecimalType, t: IntegralType) =>
Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
case (_: FractionalType, _: DecimalType) | (_: DecimalType, _: FractionalType) =>
Some(DoubleType)
case _ =>
findTightestCommonTypeToString(t1, t2)
}
private def findWiderCommonType(types: Seq[DataType]) = {
types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match {
case Some(d) => findWiderTypeForTwo(d, c)
case None => None
})
}
/**
* Similar to [[findWiderCommonType]], but can't promote to string. This is also similar to
* [[findTightestCommonType]], but can handle decimal types. If the wider decimal type exceeds
* system limitation, this rule will truncate the decimal type before return it.
*/
def findWiderTypeWithoutStringPromotion(types: Seq[DataType]): Option[DataType] = {
types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match {
case Some(d) => findTightestCommonTypeOfTwo(d, c).orElse((d, c) match {
case (t1: DecimalType, t2: DecimalType) =>
Some(DecimalPrecision.widerDecimalType(t1, t2))
case (t: IntegralType, d: DecimalType) =>
Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
case (d: DecimalType, t: IntegralType) =>
Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
case (_: FractionalType, _: DecimalType) | (_: DecimalType, _: FractionalType) =>
Some(DoubleType)
case _ => None
})
case None => None
})
}
private def haveSameType(exprs: Seq[Expression]): Boolean =
exprs.map(_.dataType).distinct.length == 1
/**
* Applies any changes to [[AttributeReference]] data types that are made by other rules to
* instances higher in the query tree.
*/
object PropagateTypes extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
// No propagation required for leaf nodes.
case q: LogicalPlan if q.children.isEmpty => q
// Don't propagate types from unresolved children.
case q: LogicalPlan if !q.childrenResolved => q
case q: LogicalPlan =>
val inputMap = q.inputSet.toSeq.map(a => (a.exprId, a)).toMap
q transformExpressions {
case a: AttributeReference =>
inputMap.get(a.exprId) match {
// This can happen when an Attribute reference is born in a non-leaf node, for
// example due to a call to an external script like in the Transform operator.
// TODO: Perhaps those should actually be aliases?
case None => a
// Leave the same if the dataTypes match.
case Some(newType) if a.dataType == newType.dataType => a
case Some(newType) =>
logDebug(s"Promoting $a to $newType in ${q.simpleString}")
newType
}
}
}
}
/**
* Widens numeric types and converts strings to numbers when appropriate.
*
* Loosely based on rules from "Hadoop: The Definitive Guide" 2nd edition, by Tom White
*
* The implicit conversion rules can be summarized as follows:
* - Any integral numeric type can be implicitly converted to a wider type.
* - All the integral numeric types, FLOAT, and (perhaps surprisingly) STRING can be implicitly
* converted to DOUBLE.
* - TINYINT, SMALLINT, and INT can all be converted to FLOAT.
* - BOOLEAN types cannot be converted to any other type.
* - Any integral numeric type can be implicitly converted to decimal type.
* - two different decimal types will be converted into a wider decimal type for both of them.
* - decimal type will be converted into double if there float or double together with it.
*
* Additionally, all types when UNION-ed with strings will be promoted to strings.
* Other string conversions are handled by PromoteStrings.
*
* Widening types might result in loss of precision in the following cases:
* - IntegerType to FloatType
* - LongType to FloatType
* - LongType to DoubleType
* - DecimalType to Double
*
* This rule is only applied to Union/Except/Intersect
*/
object WidenSetOperationTypes extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
case p if p.analyzed => p
case s @ SetOperation(left, right) if s.childrenResolved &&
left.output.length == right.output.length && !s.resolved =>
val newChildren: Seq[LogicalPlan] = buildNewChildrenWithWiderTypes(left :: right :: Nil)
assert(newChildren.length == 2)
s.makeCopy(Array(newChildren.head, newChildren.last))
case s: Union if s.childrenResolved &&
s.children.forall(_.output.length == s.children.head.output.length) && !s.resolved =>
val newChildren: Seq[LogicalPlan] = buildNewChildrenWithWiderTypes(s.children)
s.makeCopy(Array(newChildren))
}
/** Build new children with the widest types for each attribute among all the children */
private def buildNewChildrenWithWiderTypes(children: Seq[LogicalPlan]): Seq[LogicalPlan] = {
require(children.forall(_.output.length == children.head.output.length))
// Get a sequence of data types, each of which is the widest type of this specific attribute
// in all the children
val targetTypes: Seq[DataType] =
getWidestTypes(children, attrIndex = 0, mutable.Queue[DataType]())
if (targetTypes.nonEmpty) {
// Add an extra Project if the targetTypes are different from the original types.
children.map(widenTypes(_, targetTypes))
} else {
// Unable to find a target type to widen, then just return the original set.
children
}
}
/** Get the widest type for each attribute in all the children */
@tailrec private def getWidestTypes(
children: Seq[LogicalPlan],
attrIndex: Int,
castedTypes: mutable.Queue[DataType]): Seq[DataType] = {
// Return the result after the widen data types have been found for all the children
if (attrIndex >= children.head.output.length) return castedTypes.toSeq
// For the attrIndex-th attribute, find the widest type
findWiderCommonType(children.map(_.output(attrIndex).dataType)) match {
// If unable to find an appropriate widen type for this column, return an empty Seq
case None => Seq.empty[DataType]
// Otherwise, record the result in the queue and find the type for the next column
case Some(widenType) =>
castedTypes.enqueue(widenType)
getWidestTypes(children, attrIndex + 1, castedTypes)
}
}
/** Given a plan, add an extra project on top to widen some columns' data types. */
private def widenTypes(plan: LogicalPlan, targetTypes: Seq[DataType]): LogicalPlan = {
val casted = plan.output.zip(targetTypes).map {
case (e, dt) if e.dataType != dt => Alias(Cast(e, dt), e.name)()
case (e, _) => e
}
Project(casted, plan)
}
}
/**
* Promotes strings that appear in arithmetic expressions.
*/
object PromoteStrings extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
case a @ BinaryArithmetic(left @ StringType(), right) =>
a.makeCopy(Array(Cast(left, DoubleType), right))
case a @ BinaryArithmetic(left, right @ StringType()) =>
a.makeCopy(Array(left, Cast(right, DoubleType)))
// For equality between string and timestamp we cast the string to a timestamp
// so that things like rounding of subsecond precision does not affect the comparison.
case p @ Equality(left @ StringType(), right @ TimestampType()) =>
p.makeCopy(Array(Cast(left, TimestampType), right))
case p @ Equality(left @ TimestampType(), right @ StringType()) =>
p.makeCopy(Array(left, Cast(right, TimestampType)))
// We should cast all relative timestamp/date/string comparison into string comparisons
// This behaves as a user would expect because timestamp strings sort lexicographically.
// i.e. TimeStamp(2013-01-01 00:00 ...) < "2014" = true
case p @ BinaryComparison(left @ StringType(), right @ DateType()) =>
p.makeCopy(Array(left, Cast(right, StringType)))
case p @ BinaryComparison(left @ DateType(), right @ StringType()) =>
p.makeCopy(Array(Cast(left, StringType), right))
case p @ BinaryComparison(left @ StringType(), right @ TimestampType()) =>
p.makeCopy(Array(left, Cast(right, StringType)))
case p @ BinaryComparison(left @ TimestampType(), right @ StringType()) =>
p.makeCopy(Array(Cast(left, StringType), right))
// Comparisons between dates and timestamps.
case p @ BinaryComparison(left @ TimestampType(), right @ DateType()) =>
p.makeCopy(Array(Cast(left, StringType), Cast(right, StringType)))
case p @ BinaryComparison(left @ DateType(), right @ TimestampType()) =>
p.makeCopy(Array(Cast(left, StringType), Cast(right, StringType)))
// Checking NullType
case p @ BinaryComparison(left @ StringType(), right @ NullType()) =>
p.makeCopy(Array(left, Literal.create(null, StringType)))
case p @ BinaryComparison(left @ NullType(), right @ StringType()) =>
p.makeCopy(Array(Literal.create(null, StringType), right))
case p @ BinaryComparison(left @ StringType(), right) if right.dataType != StringType =>
p.makeCopy(Array(Cast(left, DoubleType), right))
case p @ BinaryComparison(left, right @ StringType()) if left.dataType != StringType =>
p.makeCopy(Array(left, Cast(right, DoubleType)))
case i @ In(a @ DateType(), b) if b.forall(_.dataType == StringType) =>
i.makeCopy(Array(Cast(a, StringType), b))
case i @ In(a @ TimestampType(), b) if b.forall(_.dataType == StringType) =>
i.makeCopy(Array(a, b.map(Cast(_, TimestampType))))
case i @ In(a @ DateType(), b) if b.forall(_.dataType == TimestampType) =>
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
case i @ In(a @ TimestampType(), b) if b.forall(_.dataType == DateType) =>
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
case Sum(e @ StringType()) => Sum(Cast(e, DoubleType))
case Average(e @ StringType()) => Average(Cast(e, DoubleType))
case StddevPop(e @ StringType()) => StddevPop(Cast(e, DoubleType))
case StddevSamp(e @ StringType()) => StddevSamp(Cast(e, DoubleType))
case VariancePop(e @ StringType()) => VariancePop(Cast(e, DoubleType))
case VarianceSamp(e @ StringType()) => VarianceSamp(Cast(e, DoubleType))
case Skewness(e @ StringType()) => Skewness(Cast(e, DoubleType))
case Kurtosis(e @ StringType()) => Kurtosis(Cast(e, DoubleType))
}
}
/**
* Convert the value and in list expressions to the common operator type
* by looking at all the argument types and finding the closest one that
* all the arguments can be cast to. When no common operator type is found
* the original expression will be returned and an Analysis Exception will
* be raised at type checking phase.
*/
object InConversion extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
case i @ In(a, b) if b.exists(_.dataType != a.dataType) =>
findWiderCommonType(i.children.map(_.dataType)) match {
case Some(finalDataType) => i.withNewChildren(i.children.map(Cast(_, finalDataType)))
case None => i
}
}
}
/**
* Changes numeric values to booleans so that expressions like true = 1 can be evaluated.
*/
object BooleanEquality extends Rule[LogicalPlan] {
private val trueValues = Seq(1.toByte, 1.toShort, 1, 1L, Decimal.ONE)
private val falseValues = Seq(0.toByte, 0.toShort, 0, 0L, Decimal.ZERO)
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
// Hive treats (true = 1) as true and (false = 0) as true,
// all other cases are considered as false.
// We may simplify the expression if one side is literal numeric values
// TODO: Maybe these rules should go into the optimizer.
case EqualTo(bool @ BooleanType(), Literal(value, _: NumericType))
if trueValues.contains(value) => bool
case EqualTo(bool @ BooleanType(), Literal(value, _: NumericType))
if falseValues.contains(value) => Not(bool)
case EqualTo(Literal(value, _: NumericType), bool @ BooleanType())
if trueValues.contains(value) => bool
case EqualTo(Literal(value, _: NumericType), bool @ BooleanType())
if falseValues.contains(value) => Not(bool)
case EqualNullSafe(bool @ BooleanType(), Literal(value, _: NumericType))
if trueValues.contains(value) => And(IsNotNull(bool), bool)
case EqualNullSafe(bool @ BooleanType(), Literal(value, _: NumericType))
if falseValues.contains(value) => And(IsNotNull(bool), Not(bool))
case EqualNullSafe(Literal(value, _: NumericType), bool @ BooleanType())
if trueValues.contains(value) => And(IsNotNull(bool), bool)
case EqualNullSafe(Literal(value, _: NumericType), bool @ BooleanType())
if falseValues.contains(value) => And(IsNotNull(bool), Not(bool))
case EqualTo(left @ BooleanType(), right @ NumericType()) =>
EqualTo(Cast(left, right.dataType), right)
case EqualTo(left @ NumericType(), right @ BooleanType()) =>
EqualTo(left, Cast(right, left.dataType))
case EqualNullSafe(left @ BooleanType(), right @ NumericType()) =>
EqualNullSafe(Cast(left, right.dataType), right)
case EqualNullSafe(left @ NumericType(), right @ BooleanType()) =>
EqualNullSafe(left, Cast(right, left.dataType))
}
}
/**
* This ensure that the types for various functions are as expected.
*/
object FunctionArgumentConversion extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
case a @ CreateArray(children) if !haveSameType(children) =>
val types = children.map(_.dataType)
findWiderCommonType(types) match {
case Some(finalDataType) => CreateArray(children.map(Cast(_, finalDataType)))
case None => a
}
case m @ CreateMap(children) if m.keys.length == m.values.length &&
(!haveSameType(m.keys) || !haveSameType(m.values)) =>
val newKeys = if (haveSameType(m.keys)) {
m.keys
} else {
val types = m.keys.map(_.dataType)
findWiderCommonType(types) match {
case Some(finalDataType) => m.keys.map(Cast(_, finalDataType))
case None => m.keys
}
}
val newValues = if (haveSameType(m.values)) {
m.values
} else {
val types = m.values.map(_.dataType)
findWiderCommonType(types) match {
case Some(finalDataType) => m.values.map(Cast(_, finalDataType))
case None => m.values
}
}
CreateMap(newKeys.zip(newValues).flatMap { case (k, v) => Seq(k, v) })
// Promote SUM, SUM DISTINCT and AVERAGE to largest types to prevent overflows.
case s @ Sum(e @ DecimalType()) => s // Decimal is already the biggest.
case Sum(e @ IntegralType()) if e.dataType != LongType => Sum(Cast(e, LongType))
case Sum(e @ FractionalType()) if e.dataType != DoubleType => Sum(Cast(e, DoubleType))
case s @ Average(e @ DecimalType()) => s // Decimal is already the biggest.
case Average(e @ IntegralType()) if e.dataType != LongType =>
Average(Cast(e, LongType))
case Average(e @ FractionalType()) if e.dataType != DoubleType =>
Average(Cast(e, DoubleType))
// Hive lets you do aggregation of timestamps... for some reason
case Sum(e @ TimestampType()) => Sum(Cast(e, DoubleType))
case Average(e @ TimestampType()) => Average(Cast(e, DoubleType))
// Coalesce should return the first non-null value, which could be any column
// from the list. So we need to make sure the return type is deterministic and
// compatible with every child column.
case c @ Coalesce(es) if !haveSameType(es) =>
val types = es.map(_.dataType)
findWiderCommonType(types) match {
case Some(finalDataType) => Coalesce(es.map(Cast(_, finalDataType)))
case None => c
}
// When finding wider type for `Greatest` and `Least`, we should handle decimal types even if
// we need to truncate, but we should not promote one side to string if the other side is
// string.g
case g @ Greatest(children) if !haveSameType(children) =>
val types = children.map(_.dataType)
findWiderTypeWithoutStringPromotion(types) match {
case Some(finalDataType) => Greatest(children.map(Cast(_, finalDataType)))
case None => g
}
case l @ Least(children) if !haveSameType(children) =>
val types = children.map(_.dataType)
findWiderTypeWithoutStringPromotion(types) match {
case Some(finalDataType) => Least(children.map(Cast(_, finalDataType)))
case None => l
}
case NaNvl(l, r) if l.dataType == DoubleType && r.dataType == FloatType =>
NaNvl(l, Cast(r, DoubleType))
case NaNvl(l, r) if l.dataType == FloatType && r.dataType == DoubleType =>
NaNvl(Cast(l, DoubleType), r)
case NaNvl(l, r) if r.dataType == NullType => NaNvl(l, Cast(r, l.dataType))
}
}
/**
* Hive only performs integral division with the DIV operator. The arguments to / are always
* converted to fractional types.
*/
object Division extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who has not been resolved yet,
// as this is an extra rule which should be applied at last.
case e if !e.childrenResolved => e
// Decimal and Double remain the same
case d: Divide if d.dataType == DoubleType => d
case d: Divide if d.dataType.isInstanceOf[DecimalType] => d
case Divide(left, right) if isNumericOrNull(left) && isNumericOrNull(right) =>
Divide(Cast(left, DoubleType), Cast(right, DoubleType))
}
private def isNumericOrNull(ex: Expression): Boolean = {
// We need to handle null types in case a query contains null literals.
ex.dataType.isInstanceOf[NumericType] || ex.dataType == NullType
}
}
/**
* Coerces the type of different branches of a CASE WHEN statement to a common type.
*/
object CaseWhenCoercion extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
case c: CaseWhen if c.childrenResolved && !c.valueTypesEqual =>
val maybeCommonType = findWiderCommonType(c.valueTypes)
maybeCommonType.map { commonType =>
var changed = false
val newBranches = c.branches.map { case (condition, value) =>
if (value.dataType.sameType(commonType)) {
(condition, value)
} else {
changed = true
(condition, Cast(value, commonType))
}
}
val newElseValue = c.elseValue.map { value =>
if (value.dataType.sameType(commonType)) {
value
} else {
changed = true
Cast(value, commonType)
}
}
if (changed) CaseWhen(newBranches, newElseValue) else c
}.getOrElse(c)
}
}
/**
* Coerces the type of different branches of If statement to a common type.
*/
object IfCoercion extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
case e if !e.childrenResolved => e
// Find tightest common type for If, if the true value and false value have different types.
case i @ If(pred, left, right) if left.dataType != right.dataType =>
findWiderTypeForTwo(left.dataType, right.dataType).map { widestType =>
val newLeft = if (left.dataType == widestType) left else Cast(left, widestType)
val newRight = if (right.dataType == widestType) right else Cast(right, widestType)
If(pred, newLeft, newRight)
}.getOrElse(i) // If there is no applicable conversion, leave expression unchanged.
case If(Literal(null, NullType), left, right) =>
If(Literal.create(null, BooleanType), left, right)
case If(pred, left, right) if pred.dataType == NullType =>
If(Cast(pred, BooleanType), left, right)
}
}
/**
* Turns Add/Subtract of DateType/TimestampType/StringType and CalendarIntervalType
* to TimeAdd/TimeSub
*/
object DateTimeOperations extends Rule[LogicalPlan] {
private val acceptedTypes = Seq(DateType, TimestampType, StringType)
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
case Add(l @ CalendarIntervalType(), r) if acceptedTypes.contains(r.dataType) =>
Cast(TimeAdd(r, l), r.dataType)
case Add(l, r @ CalendarIntervalType()) if acceptedTypes.contains(l.dataType) =>
Cast(TimeAdd(l, r), l.dataType)
case Subtract(l, r @ CalendarIntervalType()) if acceptedTypes.contains(l.dataType) =>
Cast(TimeSub(l, r), l.dataType)
}
}
/**
* Casts types according to the expected input types for [[Expression]]s.
*/
object ImplicitTypeCasts extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { commonType =>
if (b.inputType.acceptsType(commonType)) {
// If the expression accepts the tightest common type, cast to that.
val newLeft = if (left.dataType == commonType) left else Cast(left, commonType)
val newRight = if (right.dataType == commonType) right else Cast(right, commonType)
b.withNewChildren(Seq(newLeft, newRight))
} else {
// Otherwise, don't do anything with the expression.
b
}
}.getOrElse(b) // If there is no applicable conversion, leave expression unchanged.
case e: ImplicitCastInputTypes if e.inputTypes.nonEmpty =>
val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
// If we cannot do the implicit cast, just use the original input.
implicitCast(in, expected).getOrElse(in)
}
e.withNewChildren(children)
case e: ExpectsInputTypes if e.inputTypes.nonEmpty =>
// Convert NullType into some specific target type for ExpectsInputTypes that don't do
// general implicit casting.
val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
if (in.dataType == NullType && !expected.acceptsType(NullType)) {
Literal.create(null, expected.defaultConcreteType)
} else {
in
}
}
e.withNewChildren(children)
}
/**
* Given an expected data type, try to cast the expression and return the cast expression.
*
* If the expression already fits the input type, we simply return the expression itself.
* If the expression has an incompatible type that cannot be implicitly cast, return None.
*/
def implicitCast(e: Expression, expectedType: AbstractDataType): Option[Expression] = {
val inType = e.dataType
// Note that ret is nullable to avoid typing a lot of Some(...) in this local scope.
// We wrap immediately an Option after this.
@Nullable val ret: Expression = (inType, expectedType) match {
// If the expected type is already a parent of the input type, no need to cast.
case _ if expectedType.acceptsType(inType) => e
// Cast null type (usually from null literals) into target types
case (NullType, target) => Cast(e, target.defaultConcreteType)
// If the function accepts any numeric type and the input is a string, we follow the hive
// convention and cast that input into a double
case (StringType, NumericType) => Cast(e, NumericType.defaultConcreteType)
// Implicit cast among numeric types. When we reach here, input type is not acceptable.
// If input is a numeric type but not decimal, and we expect a decimal type,
// cast the input to decimal.
case (d: NumericType, DecimalType) => Cast(e, DecimalType.forType(d))
// For any other numeric types, implicitly cast to each other, e.g. long -> int, int -> long
case (_: NumericType, target: NumericType) => Cast(e, target)
// Implicit cast between date time types
case (DateType, TimestampType) => Cast(e, TimestampType)
case (TimestampType, DateType) => Cast(e, DateType)
// Implicit cast from/to string
case (StringType, DecimalType) => Cast(e, DecimalType.SYSTEM_DEFAULT)
case (StringType, target: NumericType) => Cast(e, target)
case (StringType, DateType) => Cast(e, DateType)
case (StringType, TimestampType) => Cast(e, TimestampType)
case (StringType, BinaryType) => Cast(e, BinaryType)
// Cast any atomic type to string.
case (any: AtomicType, StringType) if any != StringType => Cast(e, StringType)
// When we reach here, input type is not acceptable for any types in this type collection,
// try to find the first one we can implicitly cast.
case (_, TypeCollection(types)) => types.flatMap(implicitCast(e, _)).headOption.orNull
// Else, just return the same input expression
case _ => null
}
Option(ret)
}
}
}