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[SPARK-32696][SQL][test-hive1.2][test-hadoop2.7]Get columns operation should handle interval column properly #29539

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[SPARK-32696][SQL][test-hive1.2][test-hadoop2.7]Get columns operation should handle interval column properly #29539

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[SPARK-32696][SQL]Get columns operation should handle interval column…
yaooqinn Aug 25, 2020
ae9d76a
fix
yaooqinn Aug 25, 2020
bd3e692
set column size to 16
yaooqinn Aug 26, 2020
d3888d4
add test
yaooqinn Aug 27, 2020
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set column size to 16
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@yaooqinn
yaooqinn committed Aug 26, 2020
commit bd3e6925427ae3d97ba84382e48b06e5a56cd254
3 changes: 2 additions & 1 deletion ...rver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkGetColumnsOperation.scala
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Expand Up @@ -130,7 +130,8 @@ private[hive] class SparkGetColumnsOperation(
* For array, map, string, and binaries, the column size is variable, return null as unknown.
*/
private def getColumnSize(typ: DataType): Option[Int] = typ match {
case dt @ (BooleanType | _: NumericType | DateType | TimestampType) => Some(dt.defaultSize)
case dt @ (BooleanType | _: NumericType | DateType | TimestampType | CalendarIntervalType) =>
Some(dt.defaultSize)
case StructType(fields) =>
val sizeArr = fields.map(f => getColumnSize(f.dataType))
if (sizeArr.contains(None)) {
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2 changes: 1 addition & 1 deletion ...r/src/test/scala/org/apache/spark/sql/hive/thriftserver/SparkMetadataOperationSuite.scala
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Expand Up @@ -349,7 +349,7 @@ class SparkMetadataOperationSuite extends HiveThriftJdbcTest {
assert(rowSet.getString("COLUMN_NAME") === "i")
assert(rowSet.getInt("DATA_TYPE") === java.sql.Types.OTHER)
assert(rowSet.getString("TYPE_NAME").equalsIgnoreCase(CalendarIntervalType.sql))
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@cloud-fan cloud-fan Aug 27, 2020

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if we treat it as string type, can we still report the TYPE_NAME as INTERVAL?

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@yaooqinn yaooqinn Aug 27, 2020 •
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I guess that all the meta operations should do nothing else but specifically describe how all the objects(database/table /columns etc) stored in the Spark system.

image

If we change the column meta here from INTERVAL to STRING, so the column here becomes orderable and comparable?(JDBC users may guess), they may have no idea about what they are dealing with and get a completely different awareness of the meta-information comparing to those users who use spark-sql self-contained applications.

assert(rowSet.getInt("COLUMN_SIZE") === 0)
assert(rowSet.getInt("COLUMN_SIZE") === CalendarIntervalType.defaultSize)
assert(rowSet.getInt("DECIMAL_DIGITS") === 0)
assert(rowSet.getInt("NUM_PREC_RADIX") === 0)
assert(rowSet.getInt("NULLABLE") === 0)
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