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[SPARK-21646][SQL] Add new type coercion to compatible with Hive #23626

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[SPARK-21646][SQL] Add new type coercion to compatible with Hive #23626

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33 changes: 32 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -113,12 +113,21 @@ object TypeCoercion {
case _ => None
}

private def findCommonTypeForBinaryComparison(
dt1: DataType, dt2: DataType, conf: SQLConf): Option[DataType] = {
if (conf.isHiveTypeCoercionMode) {
findHiveCommonTypeForBinary(dt1, dt2)
} else {
findNativeCommonTypeForBinary(dt1, dt2, conf)
}
}

/**
* This function determines the target type of a comparison operator when one operand
* is a String and the other is not. It also handles when one op is a Date and the
* other is a Timestamp by making the target type to be String.
*/
private def findCommonTypeForBinaryComparison(
private def findNativeCommonTypeForBinary(
dt1: DataType, dt2: DataType, conf: SQLConf): Option[DataType] = (dt1, dt2) match {
// We should cast all relative timestamp/date/string comparison into string comparisons
// This behaves as a user would expect because timestamp strings sort lexicographically.
Expand Down Expand Up @@ -204,6 +213,28 @@ object TypeCoercion {
}
}

/**
* This function follow hive's binary comparison action:
* https://github.com/apache/hive/blob/rel/release-3.0.0/ql/src/java/
* org/apache/hadoop/hive/ql/exec/FunctionRegistry.java#L802
*/
private def findHiveCommonTypeForBinary(
dt1: DataType, dt2: DataType): Option[DataType] = (dt1, dt2) match {
case (StringType, DateType) => Some(DateType)
case (DateType, StringType) => Some(DateType)
case (StringType, TimestampType) => Some(TimestampType)
case (TimestampType, StringType) => Some(TimestampType)
case (TimestampType, DateType) => Some(TimestampType)
case (DateType, TimestampType) => Some(TimestampType)
case (StringType, NullType) => Some(StringType)
case (NullType, StringType) => Some(StringType)
case (StringType | TimestampType, r: NumericType) => Some(DoubleType)
case (l: NumericType, StringType | TimestampType) => Some(DoubleType)
case (l: StringType, r: AtomicType) if r != StringType => Some(r)
case (l: AtomicType, r: StringType) if l != StringType => Some(l)
case _ => None
}

/**
* Case 2 type widening (see the classdoc comment above for TypeCoercion).
*
Expand Down
11 changes: 11 additions & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
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Expand Up @@ -1334,6 +1334,15 @@ object SQLConf {
.booleanConf
.createWithDefault(false)

val typeCoercionMode =
buildConf("spark.sql.typeCoercion.mode")
.doc("Since Spark 2.4, the 'hive' mode is introduced for Hive compatiblity. " +
"Spark SQL has its native type cocersion mode, which is enabled by default.")
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@mahendra-osc mahendra-osc Jan 26, 2019

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I like that you added documentation. Did you mean "coercion" rather than "cocersion"?

.stringConf
.transform(_.toLowerCase(Locale.ROOT))
.checkValues(Set("default", "hive"))
.createWithDefault("default")

val REPLACE_EXCEPT_WITH_FILTER = buildConf("spark.sql.optimizer.replaceExceptWithFilter")
.internal()
.doc("When true, the apply function of the rule verifies whether the right node of the" +
Expand Down Expand Up @@ -2010,6 +2019,8 @@ class SQLConf extends Serializable with Logging {

def arrowSafeTypeConversion: Boolean = getConf(SQLConf.PANDAS_ARROW_SAFE_TYPE_CONVERSION)

def isHiveTypeCoercionMode: Boolean = getConf(SQLConf.typeCoercionMode).equals("hive")

def replaceExceptWithFilter: Boolean = getConf(REPLACE_EXCEPT_WITH_FILTER)

def decimalOperationsAllowPrecisionLoss: Boolean = getConf(DECIMAL_OPERATIONS_ALLOW_PREC_LOSS)
Expand Down