cloud-fan commented on code in PR #44119:
URL: https://github.com/apache/spark/pull/44119#discussion_r1428621468
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sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -4129,6 +4129,36 @@ class Dataset[T] private[sql](
new DataFrameWriterV2[T](table, this)
}
+ /**
+ * Create a [[DataFrameWriterV2]] for MergeInto action.
+ *
+ * Scala Examples:
+ * {{{
+ * spark.table("source")
+ * .mergeInto("target")
+ * .on($"source.id" === $"target.id")
+ * .whenMatched($"salary" === 100)
+ * .delete()
+ * .whenNotMatched()
+ * .insertAll()
+ * .whenNotMatchedBySource($"salary" === 100)
+ * .update(Map(
+ * "salary" -> lit(200)
+ * ))
+ * .merge()
+ * }}}
+ *
+ * @since 4.0.0
+ */
+ def mergeInto(table: String): DataFrameWriterV2[T] = {
+ if (isStreaming) {
+ logicalPlan.failAnalysis(
+ errorClass = "CALL_ON_STREAMING_DATASET_UNSUPPORTED",
+ messageParameters = Map("methodName" -> toSQLId("mergeInto")))
+ }
+ new DataFrameWriterV2[T](table, this)
Review Comment:
I think it's a good place here to enter the merge API layer, e.g.
`MergeIntoWriter[T]`, similar to `CreateTableWriter[T]`. It's still the
`DataFrameWriterV2` to extend `MergeIntoWriter[T]`, but the exposed APIs become
a subset.
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