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Posted to reviews@spark.apache.org by "amaliujia (via GitHub)" <gi...@apache.org> on 2023/04/07 02:48:31 UTC
[GitHub] [spark] amaliujia opened a new pull request, #40693: [SPARK-43058] Move Numeric to PhysicalDataType
amaliujia opened a new pull request, #40693:
URL: https://github.com/apache/spark/pull/40693
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### What changes were proposed in this pull request?
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This PR proposes that we start to move Numeric to PhysicalDataType. This is to simplify the DataType class to make it become a simple interface without coupling too many internal representations.
### Why are the changes needed?
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1. If you propose a new API, clarify the use case for a new API.
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To make DataType become a simpler interface, non-public code can be moved outside of the DataType class.
### Does this PR introduce _any_ user-facing change?
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NO
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162318834
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalExpressions.scala:
##########
@@ -273,7 +274,8 @@ case class DecimalDivideWithOverflowCheck(
}
} else {
val value2 = right.eval(input)
- dataType.fractional.asInstanceOf[Fractional[Any]].div(value1, value2).asInstanceOf[Decimal]
+ PhysicalDecimalType(dataType.precision, dataType.scale)
+ .fractional.asInstanceOf[Fractional[Any]].div(value1, value2).asInstanceOf[Decimal]
Review Comment:
```suggestion
.fractional.div(value1, value2).asInstanceOf[Decimal]
```
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162212156
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
This won't work because there is a `PhysicalDataType(dt).asInstanceOf[PhysicalNumericType]` thus the code will break whenever the type is not numeric type. We will see exception like `xxx cannot be cast to PhysicalNumericType`.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1161954353
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -915,9 +916,9 @@ case class Cast(
case TimestampType =>
buildCast[Long](_, t => timestampToLong(t))
case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
+ b => PhysicalNumericType.exactNumeric(x).toLong(b)
Review Comment:
Done
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40693: [SPARK-43058] Move Numeric to PhysicalDataType
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1160400552
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/PhysicalDataType.scala:
##########
@@ -23,7 +23,7 @@ import scala.reflect.runtime.universe.typeTag
import org.apache.spark.sql.catalyst.expressions.{Ascending, BoundReference, InterpretedOrdering, SortOrder}
import org.apache.spark.sql.catalyst.util.{ArrayData, SQLOrderingUtil}
import org.apache.spark.sql.errors.QueryExecutionErrors
-import org.apache.spark.sql.types.{ArrayType, BinaryType, BooleanType, ByteType, DataType, DateType, DayTimeIntervalType, Decimal, DecimalType, DoubleType, FloatType, IntegerType, LongType, MapType, NullType, ShortType, StringType, StructField, StructType, TimestampNTZType, TimestampType, YearMonthIntervalType}
+import org.apache.spark.sql.types.{ArrayType, BinaryType, BooleanType, ByteExactNumeric, ByteType, DataType, DateType, DayTimeIntervalType, Decimal, DecimalExactNumeric, DecimalType, DoubleExactNumeric, DoubleType, FloatExactNumeric, FloatType, IntegerExactNumeric, IntegerType, LongExactNumeric, LongType, MapType, NullType, NumericType, ShortExactNumeric, ShortType, StringType, StructField, StructType, TimestampNTZType, TimestampType, YearMonthIntervalType}
Review Comment:
We also have to move `*ExactNumeric` classes at some point.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1161097318
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -915,9 +916,9 @@ case class Cast(
case TimestampType =>
buildCast[Long](_, t => timestampToLong(t))
case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
+ b => PhysicalNumericType.exactNumeric(x).toLong(b)
Review Comment:
One small issue with all of these changes is that we are doing the same resolution over and over on the hot path, while we only need to do it once. Can you move the resolution to the `PhysicalNumericType.numeric` out of the function, and do this for all calls in here?
For example:
``` scala
val exactNumeric = PhysicalNumericType.exactNumeric(x)
b => exactNumeric.toLong(b)
```
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1161521386
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -915,9 +916,9 @@ case class Cast(
case TimestampType =>
buildCast[Long](_, t => timestampToLong(t))
case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
+ b => PhysicalNumericType.exactNumeric(x).toLong(b)
Review Comment:
let's not overcomplicate it. The code here matches a data type and returns a lambda. We just need to get the `numeric` ahead of the lambda function body.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162225985
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
Or we keep `PhysicalNumericType`, but its `numeric` takes `DataType` and throws an exception if the input is not `NumericType`. Then we can still simplify the code here.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162342255
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -820,11 +821,13 @@ case class Divide(
}
private lazy val div: (Any, Any) => Any = dataType match {
- case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
- checkDecimalOverflow(value.asInstanceOf[Decimal], precision, scale)
- }
- case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
+ case d @ DecimalType.Fixed(precision, scale) =>
+ val fractional = PhysicalDecimalType(precision, scale).fractional
+ (l, r) => {
+ val value = fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
ah got it, no need to change then.
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[GitHub] [spark] cloud-fan closed pull request #40693: [SPARK-43058][SQL] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan closed pull request #40693: [SPARK-43058][SQL] Move Numeric and Fractional to PhysicalDataType
URL: https://github.com/apache/spark/pull/40693
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162309743
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -821,10 +822,11 @@ case class Divide(
private lazy val div: (Any, Any) => Any = dataType match {
case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
+ val value =
+ PhysicalDecimalType(precision, scale).fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
actually we can just return `PhysicalDecimalType(precision, scale).fractional.asInstanceOf[Fractional[Any]].div`
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162212156
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
I see what you are suggesting.
Then probably we move every of those fields (ordering, fractional, numeric, exactnumeric, integral, etc.) to the top-level fields.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162212156
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
I see what you are suggesting.
Then probably we move every of those fields (ordering, fractional, numeric, exactnumeric, integral, etc.) to the top-level fields.
This probably make PhysicalDataType more bigger as many PhysicalDataTypes need to throw.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162330498
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -820,11 +821,13 @@ case class Divide(
}
private lazy val div: (Any, Any) => Any = dataType match {
- case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
- checkDecimalOverflow(value.asInstanceOf[Decimal], precision, scale)
- }
- case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
+ case d @ DecimalType.Fixed(precision, scale) =>
+ val fractional = PhysicalDecimalType(precision, scale).fractional
+ (l, r) => {
+ val value = fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
We actually needs this because the franctional here is from pyhsical data type directly which is fractional[InternalType]
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162330656
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalExpressions.scala:
##########
@@ -273,7 +274,8 @@ case class DecimalDivideWithOverflowCheck(
}
} else {
val value2 = right.eval(input)
- dataType.fractional.asInstanceOf[Fractional[Any]].div(value1, value2).asInstanceOf[Decimal]
+ PhysicalDecimalType(dataType.precision, dataType.scale)
+ .fractional.asInstanceOf[Fractional[Any]].div(value1, value2).asInstanceOf[Decimal]
Review Comment:
same
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162318609
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -820,11 +821,13 @@ case class Divide(
}
private lazy val div: (Any, Any) => Any = dataType match {
- case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
- checkDecimalOverflow(value.asInstanceOf[Decimal], precision, scale)
- }
- case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
+ case d @ DecimalType.Fixed(precision, scale) =>
+ val fractional = PhysicalDecimalType(precision, scale).fractional
+ (l, r) => {
+ val value = fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
```suggestion
val value = fractional.div(l, r)
```
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162309432
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -821,10 +822,11 @@ case class Divide(
private lazy val div: (Any, Any) => Any = dataType match {
case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
+ val value =
+ PhysicalDecimalType(precision, scale).fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
can we move it outside of the lambda?
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1161169706
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -915,9 +916,9 @@ case class Cast(
case TimestampType =>
buildCast[Long](_, t => timestampToLong(t))
case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
+ b => PhysicalNumericType.exactNumeric(x).toLong(b)
Review Comment:
Essentially I think to achieve what you want to are doing something like this
```
case FloatType: PhysicalFloatType. exactNumeric()
case DoubleType: ....
...
```
This is already the least code implementation.
If we pull it out of the method it will even becomes this
```
Map map;
case FloatType: map.put(FloatType, PhysicalFloatType. exactNumeric())
within methods
case FloatType: map,get(FloatType)
```
@hvanhovell what do you think?
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[GitHub] [spark] amaliujia commented on pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on PR #40693:
URL: https://github.com/apache/spark/pull/40693#issuecomment-1499981247
@hvanhovell @cloud-fan
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1161097318
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -915,9 +916,9 @@ case class Cast(
case TimestampType =>
buildCast[Long](_, t => timestampToLong(t))
case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
+ b => PhysicalNumericType.exactNumeric(x).toLong(b)
Review Comment:
One small issue with all of these changes is that we are doing the same resolution over and over on the hot path, while we only need to do it once. Can you move the resolution to the `PhysicalNumericType.numeric` out of the function?
For example:
``` scala
val exactNumeric = PhysicalNumericType.exactNumeric(x)
b => exactNumeric.toLong(b)
```
The same applies for almost all other uses of numeric and exactNumeric.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162208044
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
this is really ugly... I'm wondering if we should just remove `PhysicalNumericType`, and make `numeric` a field in the top level `PhysicalDataType`, If a type doesn't support numeric, it should throw an exception.
Then here we can just write
```
lazy val numeric = PhysicalDataType.numeric(from)
from match {
...
}
```
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[GitHub] [spark] cloud-fan commented on pull request #40693: [SPARK-43058][SQL] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on PR #40693:
URL: https://github.com/apache/spark/pull/40693#issuecomment-1503248755
thanks, merging to master!
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1161167873
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -915,9 +916,9 @@ case class Cast(
case TimestampType =>
buildCast[Long](_, t => timestampToLong(t))
case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
+ b => PhysicalNumericType.exactNumeric(x).toLong(b)
Review Comment:
Well after reading the code, I think we need to expland `NumericType` to all its sub-class and find the numeric directly to avoid `PhysicalNumericType.exactNumeric(x)`.
This is because we do not know the running type in advance.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162254891
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
Code updated for simplification.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162225985
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
Or we keep `PhysicalNumericType`, but its `numeric` method takes `DataType` and throws an exception if the input is not `NumericType`. Then we can still simplify the code here.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162310377
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -821,10 +822,11 @@ case class Divide(
private lazy val div: (Any, Any) => Any = dataType match {
case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
+ val value =
+ PhysicalDecimalType(precision, scale).fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
and we can remove `.asInstanceOf[Fractional[Any]]`
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[GitHub] [spark] cloud-fan commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162208044
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala:
##########
@@ -902,152 +903,191 @@ case class Cast(
}
// LongConverter
- private[this] def castToLong(from: DataType): Any => Any = from match {
- case StringType if ansiEnabled =>
- buildCast[UTF8String](_, v => UTF8StringUtils.toLongExact(v, getContextOrNull()))
- case StringType =>
- val result = new LongWrapper()
- buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null)
- case BooleanType =>
- buildCast[Boolean](_, b => if (b) 1L else 0L)
- case DateType =>
- buildCast[Int](_, d => null)
- case TimestampType =>
- buildCast[Long](_, t => timestampToLong(t))
- case x: NumericType if ansiEnabled =>
- b => x.exactNumeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: NumericType =>
- b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
- case x: DayTimeIntervalType =>
- buildCast[Long](_, i => dayTimeIntervalToLong(i, x.startField, x.endField))
- case x: YearMonthIntervalType =>
- buildCast[Int](_, i => yearMonthIntervalToInt(i, x.startField, x.endField).toLong)
+ private[this] def castToLong(from: DataType): Any => Any = {
+ var exactNumeric: Numeric[Any] = null
+ var numeric: Numeric[Any] = null
Review Comment:
this is really ugly... I'm wondering if we should just remove `PhysicalNumericType`, and make `numeric` a field in the top level `PhysicalDataType`, If a type doesn't support numeric, it should throw an exception.
Then here we can just write
```
lazy val numeric = PhysicalType.numeric(from)
from match {
...
}
```
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162331907
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -820,11 +821,13 @@ case class Divide(
}
private lazy val div: (Any, Any) => Any = dataType match {
- case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
- checkDecimalOverflow(value.asInstanceOf[Decimal], precision, scale)
- }
- case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
+ case d @ DecimalType.Fixed(precision, scale) =>
+ val fractional = PhysicalDecimalType(precision, scale).fractional
+ (l, r) => {
+ val value = fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
If you think this is better, we can still does `PhysicalFrancitonalType.franctional(dt: Datatype)` which does a resolution but hide the `asInstanceOf[Fractional[Any]]`?
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[GitHub] [spark] amaliujia commented on a diff in pull request #40693: [SPARK-43058] Move Numeric and Fractional to PhysicalDataType
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40693:
URL: https://github.com/apache/spark/pull/40693#discussion_r1162330879
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -820,11 +821,13 @@ case class Divide(
}
private lazy val div: (Any, Any) => Any = dataType match {
- case d @ DecimalType.Fixed(precision, scale) => (l, r) => {
- val value = d.fractional.asInstanceOf[Fractional[Any]].div(l, r)
- checkDecimalOverflow(value.asInstanceOf[Decimal], precision, scale)
- }
- case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
+ case d @ DecimalType.Fixed(precision, scale) =>
+ val fractional = PhysicalDecimalType(precision, scale).fractional
+ (l, r) => {
+ val value = fractional.asInstanceOf[Fractional[Any]].div(l, r)
Review Comment:
This is because we know it is decimal type thus just use `PhysicalDecimalType`
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