[SPARK-15310][SQL] Rename HiveTypeCoercion -> TypeCoercion

## What changes were proposed in this pull request?
We originally designed the type coercion rules to match Hive, but over time we have diverged. It does not make sense to call it HiveTypeCoercion anymore. This patch renames it TypeCoercion.

## How was this patch tested?
Updated unit tests to reflect the rename.

Author: Reynold Xin <rxin@databricks.com>

Closes #13091 from rxin/SPARK-15310.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala

@@ -105,7 +105,7 @@ class Analyzer(
       GlobalAggregates ::
       ResolveAggregateFunctions ::
       TimeWindowing ::
-      HiveTypeCoercion.typeCoercionRules ++
+      TypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
       PullOutNondeterministic),
@@ -1922,8 +1922,8 @@ class Analyzer(
     }
 
     private def illegalNumericPrecedence(from: DataType, to: DataType): Boolean = {
-      val fromPrecedence = HiveTypeCoercion.numericPrecedence.indexOf(from)
-      val toPrecedence = HiveTypeCoercion.numericPrecedence.indexOf(to)
+      val fromPrecedence = TypeCoercion.numericPrecedence.indexOf(from)
+      val toPrecedence = TypeCoercion.numericPrecedence.indexOf(to)
       toPrecedence > 0 && fromPrecedence > toPrecedence
     }
 

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala

@@ -24,7 +24,6 @@ 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.expressions.Literal.{FalseLiteral, TrueLiteral}
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
@@ -34,9 +33,6 @@ 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.
  *
- * Most of these rules are based on Hive semantics, but they do not introduce any dependencies on
- * the hive codebase.
- *
  * 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
@@ -46,7 +42,7 @@ import org.apache.spark.sql.types._
  * 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 HiveTypeCoercion {
+object TypeCoercion {
 
   val typeCoercionRules =
     PropagateTypes ::

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullExpressions.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.analysis.{HiveTypeCoercion, TypeCheckResult}
+import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, TypeCoercion}
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
@@ -96,7 +96,7 @@ case class IfNull(left: Expression, right: Expression) extends RuntimeReplaceabl
 
   override def replaceForTypeCoercion(): Expression = {
     if (left.dataType != right.dataType) {
-      HiveTypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
+      TypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
         copy(left = Cast(left, dtype), right = Cast(right, dtype))
       }.getOrElse(this)
     } else {
@@ -116,7 +116,7 @@ case class NullIf(left: Expression, right: Expression) extends RuntimeReplaceabl
 
   override def replaceForTypeCoercion(): Expression = {
     if (left.dataType != right.dataType) {
-      HiveTypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
+      TypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
         copy(left = Cast(left, dtype), right = Cast(right, dtype))
       }.getOrElse(this)
     } else {
@@ -134,7 +134,7 @@ case class Nvl(left: Expression, right: Expression) extends RuntimeReplaceable {
 
   override def replaceForTypeCoercion(): Expression = {
     if (left.dataType != right.dataType) {
-      HiveTypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
+      TypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
         copy(left = Cast(left, dtype), right = Cast(right, dtype))
       }.getOrElse(this)
     } else {
@@ -154,7 +154,7 @@ case class Nvl2(expr1: Expression, expr2: Expression, expr3: Expression)
 
   override def replaceForTypeCoercion(): Expression = {
     if (expr2.dataType != expr3.dataType) {
-      HiveTypeCoercion.findTightestCommonTypeOfTwo(expr2.dataType, expr3.dataType).map { dtype =>
+      TypeCoercion.findTightestCommonTypeOfTwo(expr2.dataType, expr3.dataType).map { dtype =>
         copy(expr2 = Cast(expr2, dtype), expr3 = Cast(expr3, dtype))
       }.getOrElse(this)
     } else {

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala

@@ -26,11 +26,11 @@ import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
 
-class HiveTypeCoercionSuite extends PlanTest {
+class TypeCoercionSuite extends PlanTest {
 
   test("eligible implicit type cast") {
     def shouldCast(from: DataType, to: AbstractDataType, expected: DataType): Unit = {
-      val got = HiveTypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
+      val got = TypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
       assert(got.map(_.dataType) == Option(expected),
         s"Failed to cast $from to $to")
     }
@@ -101,7 +101,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
   test("ineligible implicit type cast") {
     def shouldNotCast(from: DataType, to: AbstractDataType): Unit = {
-      val got = HiveTypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
+      val got = TypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
       assert(got.isEmpty, s"Should not be able to cast $from to $to, but got $got")
     }
 
@@ -129,11 +129,11 @@ class HiveTypeCoercionSuite extends PlanTest {
 
   test("tightest common bound for types") {
     def widenTest(t1: DataType, t2: DataType, tightestCommon: Option[DataType]) {
-      var found = HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2)
+      var found = TypeCoercion.findTightestCommonTypeOfTwo(t1, t2)
       assert(found == tightestCommon,
         s"Expected $tightestCommon as tightest common type for $t1 and $t2, found $found")
       // Test both directions to make sure the widening is symmetric.
-      found = HiveTypeCoercion.findTightestCommonTypeOfTwo(t2, t1)
+      found = TypeCoercion.findTightestCommonTypeOfTwo(t2, t1)
       assert(found == tightestCommon,
         s"Expected $tightestCommon as tightest common type for $t2 and $t1, found $found")
     }
@@ -206,31 +206,31 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("cast NullType for expressions that implement ExpectsInputTypes") {
-    import HiveTypeCoercionSuite._
+    import TypeCoercionSuite._
 
-    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+    ruleTest(TypeCoercion.ImplicitTypeCasts,
       AnyTypeUnaryExpression(Literal.create(null, NullType)),
       AnyTypeUnaryExpression(Literal.create(null, NullType)))
 
-    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+    ruleTest(TypeCoercion.ImplicitTypeCasts,
       NumericTypeUnaryExpression(Literal.create(null, NullType)),
       NumericTypeUnaryExpression(Literal.create(null, DoubleType)))
   }
 
   test("cast NullType for binary operators") {
-    import HiveTypeCoercionSuite._
+    import TypeCoercionSuite._
 
-    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+    ruleTest(TypeCoercion.ImplicitTypeCasts,
       AnyTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)),
       AnyTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)))
 
-    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+    ruleTest(TypeCoercion.ImplicitTypeCasts,
       NumericTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)),
       NumericTypeBinaryOperator(Literal.create(null, DoubleType), Literal.create(null, DoubleType)))
   }
 
   test("coalesce casts") {
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       Coalesce(Literal(1.0)
         :: Literal(1)
         :: Literal.create(1.0, FloatType)
@@ -239,7 +239,7 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Cast(Literal(1), DoubleType)
         :: Cast(Literal.create(1.0, FloatType), DoubleType)
         :: Nil))
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       Coalesce(Literal(1L)
         :: Literal(1)
         :: Literal(new java.math.BigDecimal("1000000000000000000000"))
@@ -251,7 +251,7 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("CreateArray casts") {
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       CreateArray(Literal(1.0)
         :: Literal(1)
         :: Literal.create(1.0, FloatType)
@@ -261,7 +261,7 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Cast(Literal.create(1.0, FloatType), DoubleType)
         :: Nil))
 
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       CreateArray(Literal(1.0)
         :: Literal(1)
         :: Literal("a")
@@ -274,7 +274,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
   test("CreateMap casts") {
     // type coercion for map keys
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       CreateMap(Literal(1)
         :: Literal("a")
         :: Literal.create(2.0, FloatType)
@@ -286,7 +286,7 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Literal("b")
         :: Nil))
     // type coercion for map values
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       CreateMap(Literal(1)
         :: Literal("a")
         :: Literal(2)
@@ -298,7 +298,7 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Cast(Literal(3.0), StringType)
         :: Nil))
     // type coercion for both map keys and values
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       CreateMap(Literal(1)
         :: Literal("a")
         :: Literal(2.0)
@@ -313,7 +313,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
   test("greatest/least cast") {
     for (operator <- Seq[(Seq[Expression] => Expression)](Greatest, Least)) {
-      ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+      ruleTest(TypeCoercion.FunctionArgumentConversion,
         operator(Literal(1.0)
           :: Literal(1)
           :: Literal.create(1.0, FloatType)
@@ -322,7 +322,7 @@ class HiveTypeCoercionSuite extends PlanTest {
           :: Cast(Literal(1), DoubleType)
           :: Cast(Literal.create(1.0, FloatType), DoubleType)
           :: Nil))
-      ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+      ruleTest(TypeCoercion.FunctionArgumentConversion,
         operator(Literal(1L)
           :: Literal(1)
           :: Literal(new java.math.BigDecimal("1000000000000000000000"))
@@ -335,19 +335,19 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("nanvl casts") {
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       NaNvl(Literal.create(1.0, FloatType), Literal.create(1.0, DoubleType)),
       NaNvl(Cast(Literal.create(1.0, FloatType), DoubleType), Literal.create(1.0, DoubleType)))
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       NaNvl(Literal.create(1.0, DoubleType), Literal.create(1.0, FloatType)),
       NaNvl(Literal.create(1.0, DoubleType), Cast(Literal.create(1.0, FloatType), DoubleType)))
-    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+    ruleTest(TypeCoercion.FunctionArgumentConversion,
       NaNvl(Literal.create(1.0, DoubleType), Literal.create(1.0, DoubleType)),
       NaNvl(Literal.create(1.0, DoubleType), Literal.create(1.0, DoubleType)))
   }
 
   test("type coercion for If") {
-    val rule = HiveTypeCoercion.IfCoercion
+    val rule = TypeCoercion.IfCoercion
 
     ruleTest(rule,
       If(Literal(true), Literal(1), Literal(1L)),
@@ -367,20 +367,20 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("type coercion for CaseKeyWhen") {
-    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+    ruleTest(TypeCoercion.ImplicitTypeCasts,
       CaseKeyWhen(Literal(1.toShort), Seq(Literal(1), Literal("a"))),
       CaseKeyWhen(Cast(Literal(1.toShort), IntegerType), Seq(Literal(1), Literal("a")))
     )
-    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+    ruleTest(TypeCoercion.CaseWhenCoercion,
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a"))),
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a")))
     )
-    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+    ruleTest(TypeCoercion.CaseWhenCoercion,
       CaseWhen(Seq((Literal(true), Literal(1.2))), Literal.create(1, DecimalType(7, 2))),
       CaseWhen(Seq((Literal(true), Literal(1.2))),
         Cast(Literal.create(1, DecimalType(7, 2)), DoubleType))
     )
-    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+    ruleTest(TypeCoercion.CaseWhenCoercion,
       CaseWhen(Seq((Literal(true), Literal(100L))), Literal.create(1, DecimalType(7, 2))),
       CaseWhen(Seq((Literal(true), Cast(Literal(100L), DecimalType(22, 2)))),
         Cast(Literal.create(1, DecimalType(7, 2)), DecimalType(22, 2)))
@@ -388,7 +388,7 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("BooleanEquality type cast") {
-    val be = HiveTypeCoercion.BooleanEquality
+    val be = TypeCoercion.BooleanEquality
     // Use something more than a literal to avoid triggering the simplification rules.
     val one = Add(Literal(Decimal(1)), Literal(Decimal(0)))
 
@@ -414,7 +414,7 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("BooleanEquality simplification") {
-    val be = HiveTypeCoercion.BooleanEquality
+    val be = TypeCoercion.BooleanEquality
 
     ruleTest(be,
       EqualTo(Literal(true), Literal(1)),
@@ -473,7 +473,7 @@ class HiveTypeCoercionSuite extends PlanTest {
       AttributeReference("f", FloatType)(),
       AttributeReference("l", LongType)())
 
-    val wt = HiveTypeCoercion.WidenSetOperationTypes
+    val wt = TypeCoercion.WidenSetOperationTypes
     val expectedTypes = Seq(StringType, DecimalType.SYSTEM_DEFAULT, FloatType, DoubleType)
 
     val r1 = wt(Except(firstTable, secondTable)).asInstanceOf[Except]
@@ -512,7 +512,7 @@ class HiveTypeCoercionSuite extends PlanTest {
       AttributeReference("p", ByteType)(),
       AttributeReference("q", DoubleType)())
 
-    val wt = HiveTypeCoercion.WidenSetOperationTypes
+    val wt = TypeCoercion.WidenSetOperationTypes
     val expectedTypes = Seq(StringType, DecimalType.SYSTEM_DEFAULT, FloatType, DoubleType)
 
     val unionRelation = wt(
@@ -536,7 +536,7 @@ class HiveTypeCoercionSuite extends PlanTest {
       }
     }
 
-    val dp = HiveTypeCoercion.WidenSetOperationTypes
+    val dp = TypeCoercion.WidenSetOperationTypes
 
     val left1 = LocalRelation(
       AttributeReference("l", DecimalType(10, 8))())
@@ -584,7 +584,7 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("rule for date/timestamp operations") {
-    val dateTimeOperations = HiveTypeCoercion.DateTimeOperations
+    val dateTimeOperations = TypeCoercion.DateTimeOperations
     val date = Literal(new java.sql.Date(0L))
     val timestamp = Literal(new Timestamp(0L))
     val interval = Literal(new CalendarInterval(0, 0))
@@ -615,7 +615,7 @@ class HiveTypeCoercionSuite extends PlanTest {
    */
   test("make sure rules do not fire early") {
     // InConversion
-    val inConversion = HiveTypeCoercion.InConversion
+    val inConversion = TypeCoercion.InConversion
     ruleTest(inConversion,
       In(UnresolvedAttribute("a"), Seq(Literal(1))),
       In(UnresolvedAttribute("a"), Seq(Literal(1)))
@@ -633,7 +633,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 }
 
 
-object HiveTypeCoercionSuite {
+object TypeCoercionSuite {
 
   case class AnyTypeUnaryExpression(child: Expression)
     extends UnaryExpression with ExpectsInputTypes with Unevaluable {

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala

@@ -25,7 +25,7 @@ import scala.util.control.Exception._
 import scala.util.Try
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
+import org.apache.spark.sql.catalyst.analysis.TypeCoercion
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -171,11 +171,11 @@ private[csv] object CSVInferSchema {
     StringType
   }
 
-  private val numericPrecedence: IndexedSeq[DataType] = HiveTypeCoercion.numericPrecedence
+  private val numericPrecedence: IndexedSeq[DataType] = TypeCoercion.numericPrecedence
 
   /**
    * Copied from internal Spark api
-   * [[org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion]]
+   * [[org.apache.spark.sql.catalyst.analysis.TypeCoercion]]
    */
   val findTightestCommonType: (DataType, DataType) => Option[DataType] = {
     case (t1, t2) if t1 == t2 => Some(t1)

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala

@@ -22,7 +22,7 @@ import java.util.Comparator
 import com.fasterxml.jackson.core._
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
+import org.apache.spark.sql.catalyst.analysis.TypeCoercion
 import org.apache.spark.sql.execution.datasources.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -252,7 +252,7 @@ private[sql] object InferSchema {
    * Returns the most general data type for two given data types.
    */
   def compatibleType(t1: DataType, t2: DataType): DataType = {
-    HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2).getOrElse {
+    TypeCoercion.findTightestCommonTypeOfTwo(t1, t2).getOrElse {
       // t1 or t2 is a StructType, ArrayType, or an unexpected type.
       (t1, t2) match {
         // Double support larger range than fixed decimal, DecimalType.Maximum should be enough