[GitHub] [spark] cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL] Support the `INTERVAL` type by Parquet datasource
cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL]
Support the `INTERVAL` type by Parquet datasource
URL: https://github.com/apache/spark/pull/26102#discussion_r347223313
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetWriteSupport.scala
##
@@ -207,7 +208,16 @@ class ParquetWriteSupport extends
WriteSupport[InternalRow] with Logging {
case t: UserDefinedType[_] => makeWriter(t.sqlType)
- // TODO Adds IntervalType support
+ case CalendarIntervalType =>
+(row: SpecializedGetters, ordinal: Int) =>
+ val interval = row.getInterval(ordinal)
+ val buf = ByteBuffer.wrap(reusableBuffer)
+ buf.order(ByteOrder.LITTLE_ENDIAN)
+.putInt((interval.milliseconds()).toInt)
Review comment:
we should use strict toInt so that we can fail earlier if out of range.
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[GitHub] [spark] cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL] Support the `INTERVAL` type by Parquet datasource
cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL]
Support the `INTERVAL` type by Parquet datasource
URL: https://github.com/apache/spark/pull/26102#discussion_r344585132
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
##
@@ -498,10 +498,8 @@ case class DataSource(
outputColumnNames: Seq[String],
physicalPlan: SparkPlan): BaseRelation = {
val outputColumns = DataWritingCommand.logicalPlanOutputWithNames(data,
outputColumnNames)
-if
(outputColumns.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
Review comment:
interval type is kind of an internal type for now. It's a big decision if we
can read/write it from/to data sources.
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[GitHub] [spark] cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL] Support the `INTERVAL` type by Parquet datasource
cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL]
Support the `INTERVAL` type by Parquet datasource
URL: https://github.com/apache/spark/pull/26102#discussion_r344585132
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
##
@@ -498,10 +498,8 @@ case class DataSource(
outputColumnNames: Seq[String],
physicalPlan: SparkPlan): BaseRelation = {
val outputColumns = DataWritingCommand.logicalPlanOutputWithNames(data,
outputColumnNames)
-if
(outputColumns.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
Review comment:
interval type is kind of an interval type for now. It's a big decision if we
can read/write it from/to data sources.
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[GitHub] [spark] cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL] Support the `INTERVAL` type by Parquet datasource
cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL]
Support the `INTERVAL` type by Parquet datasource
URL: https://github.com/apache/spark/pull/26102#discussion_r341931042
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
##
@@ -498,10 +498,8 @@ case class DataSource(
outputColumnNames: Seq[String],
physicalPlan: SparkPlan): BaseRelation = {
val outputColumns = DataWritingCommand.logicalPlanOutputWithNames(data,
outputColumnNames)
-if
(outputColumns.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
Review comment:
Let's do this change after we officially make `CalendarIntervalType` public.
i.e. move it to a public package.
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[GitHub] [spark] cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL] Support the `INTERVAL` type by Parquet datasource
cloud-fan commented on a change in pull request #26102: [SPARK-29448][SQL]
Support the `INTERVAL` type by Parquet datasource
URL: https://github.com/apache/spark/pull/26102#discussion_r334474170
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala
##
@@ -325,6 +325,26 @@ private[parquet] class ParquetRowConverter(
override def set(value: Any): Unit =
updater.set(value.asInstanceOf[InternalRow].copy())
})
+ case CalendarIntervalType
+if parquetType.asPrimitiveType().getPrimitiveTypeName ==
FIXED_LEN_BYTE_ARRAY =>
+new ParquetPrimitiveConverter(updater) {
+ override def addBinary(value: Binary): Unit = {
+assert(
+ value.length() == 12,
+ "Intervals are expected to be stored in 12-byte fixed len byte
array, " +
+s"but got a ${value.length()}-byte array.")
+
+val buf = value.toByteBuffer.order(ByteOrder.LITTLE_ENDIAN)
+val milliseconds = buf.getInt
+var microseconds = milliseconds * DateTimeUtils.MICROS_PER_MILLIS
+val days = buf.getInt
+val daysInUs = Math.multiplyExact(days,
DateTimeUtils.MICROS_PER_DAY)
Review comment:
Parquet stores # of days as a separated field because one logical day
interval can be 23 or 24 or 25 hours in case of daylight saving. If we convert
parquet interval to Spark interval, it's not a truncation but losing
information.
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