Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/20800#discussion_r187831971
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -511,6 +511,14 @@ class Dataset[T] private[sql](
*/
def isLocal: Boolean = logicalPlan.isInstanceOf[LocalRelation]
+ /**
+ * Returns true if the `Dataset` is empty.
+ *
+ * @group basic
+ * @since 2.4.0
+ */
+ def isEmpty: Boolean = rdd.isEmpty()
--- End diff --
`RDD#isEmpty` is pretty effective, it just checks if all the partitions are
empty, without loading the data. The problem is how to build an RDD from
`Dataset`, which minimize the cost of building the `Iterator`.
It seems `Dataset#rdd` is not good enough, e.g., if we have a `Filter` in
the query, we may do a full scan(no column pruning) for the underlying files.
Doing a count is not perfect either. Ideally we can stop as soon as we see
one record.
I'd suggest doing a `limit 1` first and then count.
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