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[SPARK-19254][SQL] Support Seq, Map, and Struct in functions.lit #16610

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[SPARK-19254][SQL] Support Seq, Map, and Struct in functions.lit #16610

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ca2e07f
Add a new create with TypeTag in Literal
maropu Nov 14, 2016
8d26e8b
Rename lit2 to typedlit
maropu Feb 9, 2017
4f65fe2
Apply review comments
maropu Feb 20, 2017
028b19b
Add tests in ColumnExpressionSuite
maropu Feb 20, 2017
af2d70a
Update desc in typedLit
maropu Mar 2, 2017
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12 changes: 11 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
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Expand Up @@ -32,11 +32,13 @@ import java.util.Objects
import javax.xml.bind.DatatypeConverter

import scala.math.{BigDecimal, BigInt}
import scala.reflect.runtime.universe.TypeTag
import scala.util.Try

import org.json4s.JsonAST._

import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, ScalaReflection}
import org.apache.spark.sql.catalyst.expressions.codegen._
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.types._
Expand Down Expand Up @@ -153,6 +155,14 @@ object Literal {
Literal(CatalystTypeConverters.convertToCatalyst(v), dataType)
}

def create[T : TypeTag](v: T): Literal = Try {
val ScalaReflection.Schema(dataType, _) = ScalaReflection.schemaFor[T]
val convert = CatalystTypeConverters.createToCatalystConverter(dataType)
Literal(convert(v), dataType)
}.getOrElse {
Literal(v)
}

/**
* Create a literal with default value for given DataType
*/
Expand Down
90 changes: 79 additions & 11 deletions ...yst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
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Expand Up @@ -19,9 +19,11 @@ package org.apache.spark.sql.catalyst.expressions

import java.nio.charset.StandardCharsets

import scala.reflect.runtime.universe.{typeTag, TypeTag}

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection}
import org.apache.spark.sql.catalyst.encoders.ExamplePointUDT
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.types._
Expand Down Expand Up @@ -75,6 +77,9 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
test("boolean literals") {
checkEvaluation(Literal(true), true)
checkEvaluation(Literal(false), false)

checkEvaluation(Literal.create(true), true)
checkEvaluation(Literal.create(false), false)
}

test("int literals") {
Expand All @@ -83,36 +88,60 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
checkEvaluation(Literal(d.toLong), d.toLong)
checkEvaluation(Literal(d.toShort), d.toShort)
checkEvaluation(Literal(d.toByte), d.toByte)

checkEvaluation(Literal.create(d), d)
checkEvaluation(Literal.create(d.toLong), d.toLong)
checkEvaluation(Literal.create(d.toShort), d.toShort)
checkEvaluation(Literal.create(d.toByte), d.toByte)
}
checkEvaluation(Literal(Long.MinValue), Long.MinValue)
checkEvaluation(Literal(Long.MaxValue), Long.MaxValue)

checkEvaluation(Literal.create(Long.MinValue), Long.MinValue)
checkEvaluation(Literal.create(Long.MaxValue), Long.MaxValue)
}

test("double literals") {
List(0.0, -0.0, Double.NegativeInfinity, Double.PositiveInfinity).foreach { d =>
checkEvaluation(Literal(d), d)
checkEvaluation(Literal(d.toFloat), d.toFloat)

checkEvaluation(Literal.create(d), d)
checkEvaluation(Literal.create(d.toFloat), d.toFloat)
}
checkEvaluation(Literal(Double.MinValue), Double.MinValue)
checkEvaluation(Literal(Double.MaxValue), Double.MaxValue)
checkEvaluation(Literal(Float.MinValue), Float.MinValue)
checkEvaluation(Literal(Float.MaxValue), Float.MaxValue)

checkEvaluation(Literal.create(Double.MinValue), Double.MinValue)
checkEvaluation(Literal.create(Double.MaxValue), Double.MaxValue)
checkEvaluation(Literal.create(Float.MinValue), Float.MinValue)
checkEvaluation(Literal.create(Float.MaxValue), Float.MaxValue)

}

test("string literals") {
checkEvaluation(Literal(""), "")
checkEvaluation(Literal("test"), "test")
checkEvaluation(Literal("\u0000"), "\u0000")

checkEvaluation(Literal.create(""), "")
checkEvaluation(Literal.create("test"), "test")
checkEvaluation(Literal.create("\u0000"), "\u0000")
}

test("sum two literals") {
checkEvaluation(Add(Literal(1), Literal(1)), 2)
checkEvaluation(Add(Literal.create(1), Literal.create(1)), 2)
}

test("binary literals") {
checkEvaluation(Literal.create(new Array[Byte](0), BinaryType), new Array[Byte](0))
checkEvaluation(Literal.create(new Array[Byte](2), BinaryType), new Array[Byte](2))

checkEvaluation(Literal.create(new Array[Byte](0)), new Array[Byte](0))
checkEvaluation(Literal.create(new Array[Byte](2)), new Array[Byte](2))
}

test("decimal") {
Expand All @@ -124,24 +153,63 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
Decimal((d * 1000L).toLong, 10, 3))
checkEvaluation(Literal(BigDecimal(d.toString)), Decimal(d))
checkEvaluation(Literal(new java.math.BigDecimal(d.toString)), Decimal(d))

checkEvaluation(Literal.create(Decimal(d)), Decimal(d))
checkEvaluation(Literal.create(Decimal(d.toInt)), Decimal(d.toInt))
checkEvaluation(Literal.create(Decimal(d.toLong)), Decimal(d.toLong))
checkEvaluation(Literal.create(Decimal((d * 1000L).toLong, 10, 3)),
Decimal((d * 1000L).toLong, 10, 3))
checkEvaluation(Literal.create(BigDecimal(d.toString)), Decimal(d))
checkEvaluation(Literal.create(new java.math.BigDecimal(d.toString)), Decimal(d))

}
}

private def toCatalyst[T: TypeTag](value: T): Any = {
val ScalaReflection.Schema(dataType, _) = ScalaReflection.schemaFor[T]
CatalystTypeConverters.createToCatalystConverter(dataType)(value)
}

test("array") {
def checkArrayLiteral(a: Array[_], elementType: DataType): Unit = {
val toCatalyst = (a: Array[_], elementType: DataType) => {
CatalystTypeConverters.createToCatalystConverter(ArrayType(elementType))(a)
}
checkEvaluation(Literal(a), toCatalyst(a, elementType))
def checkArrayLiteral[T: TypeTag](a: Array[T]): Unit = {
checkEvaluation(Literal(a), toCatalyst(a))
checkEvaluation(Literal.create(a), toCatalyst(a))
}
checkArrayLiteral(Array(1, 2, 3))
checkArrayLiteral(Array("a", "b", "c"))
checkArrayLiteral(Array(1.0, 4.0))
checkArrayLiteral(Array(CalendarInterval.MICROS_PER_DAY, CalendarInterval.MICROS_PER_HOUR))
}

test("seq") {
def checkSeqLiteral[T: TypeTag](a: Seq[T], elementType: DataType): Unit = {
checkEvaluation(Literal.create(a), toCatalyst(a))
}
checkArrayLiteral(Array(1, 2, 3), IntegerType)
checkArrayLiteral(Array("a", "b", "c"), StringType)
checkArrayLiteral(Array(1.0, 4.0), DoubleType)
checkArrayLiteral(Array(CalendarInterval.MICROS_PER_DAY, CalendarInterval.MICROS_PER_HOUR),
checkSeqLiteral(Seq(1, 2, 3), IntegerType)
checkSeqLiteral(Seq("a", "b", "c"), StringType)
checkSeqLiteral(Seq(1.0, 4.0), DoubleType)
checkSeqLiteral(Seq(CalendarInterval.MICROS_PER_DAY, CalendarInterval.MICROS_PER_HOUR),
CalendarIntervalType)
}

test("unsupported types (map and struct) in literals") {
test("map") {
def checkMapLiteral[T: TypeTag](m: T): Unit = {
checkEvaluation(Literal.create(m), toCatalyst(m))
}
checkMapLiteral(Map("a" -> 1, "b" -> 2, "c" -> 3))
checkMapLiteral(Map("1" -> 1.0, "2" -> 2.0, "3" -> 3.0))
}

test("struct") {
def checkStructLiteral[T: TypeTag](s: T): Unit = {
checkEvaluation(Literal.create(s), toCatalyst(s))
}
checkStructLiteral((1, 3.0, "abcde"))
checkStructLiteral(("de", 1, 2.0f))
checkStructLiteral((1, ("fgh", 3.0)))
}

test("unsupported types (map and struct) in Literal.apply") {
def checkUnsupportedTypeInLiteral(v: Any): Unit = {
val errMsgMap = intercept[RuntimeException] {
Literal(v)
Expand Down
25 changes: 17 additions & 8 deletions sql/core/src/main/scala/org/apache/spark/sql/functions.scala
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Expand Up @@ -91,15 +91,24 @@ object functions {
* @group normal_funcs
* @since 1.3.0
*/
def lit(literal: Any): Column = {
literal match {
case c: Column => return c
case s: Symbol => return new ColumnName(literal.asInstanceOf[Symbol].name)
case _ => // continue
}
def lit(literal: Any): Column = typedLit(literal)

val literalExpr = Literal(literal)
Column(literalExpr)
/**
* Creates a [[Column]] of literal value.
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We should explain when you want to use this method instead of lit.

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okay, I'll update

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updated

*
* The passed in object is returned directly if it is already a [[Column]].
* If the object is a Scala Symbol, it is converted into a [[Column]] also.
* Otherwise, a new [[Column]] is created to represent the literal value.
* The difference between this function and [[lit]] is that this function
* can handle parameterized scala types e.g.: List, Seq and Map.
*
* @group normal_funcs
* @since 2.2.0
*/
def typedLit[T : TypeTag](literal: T): Column = literal match {
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cc @cloud-fan wdyt about the naming?

case c: Column => c
case s: Symbol => new ColumnName(s.name)
case _ => Column(Literal.create(literal))
}

//////////////////////////////////////////////////////////////////////////////////////////////
Expand Down
14 changes: 14 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
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Expand Up @@ -712,4 +712,18 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
testData2.select($"a".bitwiseXOR($"b").bitwiseXOR(39)),
testData2.collect().toSeq.map(r => Row(r.getInt(0) ^ r.getInt(1) ^ 39)))
}

test("typedLit") {
val df = Seq(Tuple1(0)).toDF("a")
// Only check the types `lit` cannot handle
checkAnswer(
df.select(typedLit(Seq(1, 2, 3))),
Row(Seq(1, 2, 3)) :: Nil)
checkAnswer(
df.select(typedLit(Map("a" -> 1, "b" -> 2))),
Row(Map("a" -> 1, "b" -> 2)) :: Nil)
checkAnswer(
df.select(typedLit(("a", 2, 1.0))),
Row(Row("a", 2, 1.0)) :: Nil)
}
}