Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/8026#discussion_r42506907
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
---
@@ -236,15 +241,22 @@ private[sql] object PartitioningUtils {
}
/**
- * Converts a string to a [[Literal]] with automatic type inference.
Currently only supports
- * [[IntegerType]], [[LongType]], [[DoubleType]],
[[DecimalType.SYSTEM_DEFAULT]], and
- * [[StringType]].
+ * Converts a string to a [[Literal]] with automatic type inference if
no field type specified.
+ * Auto inference only supports [[IntegerType]], [[LongType]],
[[DoubleType]],
+ * [[DecimalType.SYSTEM_DEFAULT]], and [[StringType]].
*/
private[sql] def inferPartitionColumnValue(
+ expectedDT: Option[DataType],
--- End diff --
We need to pass the expect the data type down and then get the associated
literal-based partition column value; and @liancheng's suggestion kind of like
get the literal (maybe string based) first, and then do casting outside,
however, this probably lose some data precision during the re-casting.
For example:
The path looks like, /part1=1.000, and with the auto inference, we will get
a Double, and it will be cast to string as `1.0` if what user expect is
StringType;
However, this is totally different if we get it as StringType directly,
which supposed to be `1.000`.
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