Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/20214#discussion_r161254906
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -237,13 +237,17 @@ class Dataset[T] private[sql](
private[sql] def showString(
_numRows: Int, truncate: Int = 20, vertical: Boolean = false):
String = {
val numRows = _numRows.max(0).min(Int.MaxValue - 1)
- val takeResult = toDF().take(numRows + 1)
+ val newDf = toDF()
+ val castExprs = newDf.schema.map { f => f.dataType match {
+ // Since binary types in top-level schema fields have a specific
format to print,
+ // so we do not cast them to strings here.
+ case BinaryType => s"`${f.name}`"
--- End diff --
can we use dataframe API? which looks more reliable here
```
newDf.logicalPlan.output.map { col =>
if (col.dataType == BinaryType) {
Column(col)
} else {
Column(col).cast(StringType)
}
}
```
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