shrirangmhalgi commented on code in PR #55905:
URL: https://github.com/apache/spark/pull/55905#discussion_r3250807811
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sql/core/src/test/scala/org/apache/spark/sql/DataFrameSetOperationsSuite.scala:
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@@ -1642,6 +1642,35 @@ class DataFrameSetOperationsSuite extends
SharedSparkSession with AdaptiveSparkP
}
}
}
+
+ test("SPARK-51262: exceptAll after dropDuplicates with subset should not
throw") {
+ val df1 = spark.createDataFrame(Seq(
+ (1, "a", 100),
+ (1, "a", 200),
+ (2, "b", 300)
+ )).toDF("id", "name", "value")
+
+ val df2 = spark.createDataFrame(Seq(
+ (1, "a", 100)
+ )).toDF("id", "name", "value")
+
+ // dropDuplicates with subset keeps one row per (id, name) group
+ val deduped = df1.dropDuplicates("id", "name")
+
+ // exceptAll should work without INTERNAL_ERROR_ATTRIBUTE_NOT_FOUND
+ val result = deduped.exceptAll(df2)
+ assert(result.columns === Array("id", "name", "value"))
+ assert(result.count() === 1)
+ assert(result.collect().head.getInt(0) === 2)
+
+ // Also verify except (non-all) works
+ val result2 = deduped.except(df2)
+ assert(result2.count() === 1)
+
+ // intersectAll should also work
+ val result3 = deduped.intersectAll(df2)
+ assert(result3.count() <= 1)
Review Comment:
I realized that the test dataframe can cause non deterministic rows to be
picked up. To avoid test flakiness i modified the test data to produce
deterministic results keeping all the dataframe rows unique.
The issue persisted and the proposed fix works on new dataset as well. 😊
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