cloud-fan commented on code in PR #37320:
URL: https://github.com/apache/spark/pull/37320#discussion_r938796458


##########
sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCV2Suite.scala:
##########
@@ -864,6 +851,254 @@ class JDBCV2Suite extends QueryTest with 
SharedSparkSession with ExplainSuiteHel
     checkAnswer(df2, Seq(Row(2, "david", 10000.00)))
   }
 
+  test("scan with aggregate push-down and top N push-down") {
+    val df1 = spark.read
+      .table("h2.test.employee")
+      .groupBy("DEPT").sum("SALARY")
+      .orderBy("DEPT")
+      .limit(1)
+    checkSortRemoved(df1)
+    checkLimitRemoved(df1)
+    checkPushedInfo(df1,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: [DEPT]",
+      "PushedFilters: []",
+      "PushedTopN: ORDER BY [DEPT ASC NULLS FIRST] LIMIT 1")
+    checkAnswer(df1, Seq(Row(1, 19000.00)))
+
+    val df2 = spark.read
+      .table("h2.test.employee")
+      .select($"DEPT".as("my_dept"), $"SALARY")
+      .groupBy("my_dept").sum("SALARY")
+      .orderBy("my_dept")
+      .limit(1)
+    checkSortRemoved(df2)
+    checkLimitRemoved(df2)
+    checkPushedInfo(df2,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: [DEPT]",
+      "PushedFilters: []",
+      "PushedTopN: ORDER BY [DEPT ASC NULLS FIRST] LIMIT 1")
+    checkAnswer(df2, Seq(Row(1, 19000.00)))
+
+    val df3 = spark.read
+      .table("h2.test.employee")
+      .select($"SALARY",
+        when(($"SALARY" > 8000).and($"SALARY" < 10000), 
$"salary").otherwise(0).as("key"))
+      .groupBy("key").sum("SALARY")
+      .orderBy("key")
+      .limit(1)
+    checkSortRemoved(df3)
+    checkLimitRemoved(df3)
+    checkPushedInfo(df3,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: " +
+        "[CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY 
ELSE 0.00 END]",
+      "PushedFilters: []",
+      "PushedTopN: ORDER BY [" +
+        "CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY ELSE 
0.00 END " +
+        "ASC NULLS FIRST] LIMIT 1")
+    checkAnswer(df3, Seq(Row(0, 44000.00)))
+
+    val df4 = spark.read
+      .table("h2.test.employee")
+      .groupBy("DEPT", "IS_MANAGER").sum("SALARY")
+      .orderBy("DEPT", "IS_MANAGER")
+      .limit(1)
+    checkSortRemoved(df4)
+    checkLimitRemoved(df4)
+    checkPushedInfo(df4,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: [DEPT, IS_MANAGER]",
+      "PushedFilters: []",
+      "PushedTopN: ORDER BY [DEPT ASC NULLS FIRST, IS_MANAGER ASC NULLS FIRST] 
LIMIT 1")
+    checkAnswer(df4, Seq(Row(1, false, 9000.00)))
+
+    val df5 = spark.read
+      .table("h2.test.employee")
+      .select($"DEPT".as("my_dept"), $"IS_MANAGER".as("my_manager"), $"SALARY")
+      .groupBy("my_dept", "my_manager").sum("SALARY")
+      .orderBy("my_dept", "my_manager")
+      .limit(1)
+    checkSortRemoved(df5)
+    checkLimitRemoved(df5)
+    checkPushedInfo(df5,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: [DEPT, IS_MANAGER]",
+      "PushedFilters: []",
+      "PushedTopN: ORDER BY [DEPT ASC NULLS FIRST, IS_MANAGER ASC NULLS FIRST] 
LIMIT 1")
+    checkAnswer(df5, Seq(Row(1, false, 9000.00)))
+
+    val df6 = spark.read
+      .table("h2.test.employee")
+      .select($"SALARY", $"IS_MANAGER",
+        when(($"SALARY" > 8000).and($"SALARY" < 10000), 
$"salary").otherwise(0).as("key"))
+      .groupBy("key", "IS_MANAGER").sum("SALARY")
+      .orderBy("key", "IS_MANAGER")
+      .limit(1)
+    checkSortRemoved(df6)
+    checkLimitRemoved(df6)
+    checkPushedInfo(df6,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: " +
+        "[CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY 
ELSE 0.00 END, " +
+        "IS_MANAGER]",
+      "PushedFilters: []",
+      "PushedTopN: ORDER BY [" +
+        "CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY ELSE 
0.00 END " +
+        "ASC NULLS FIRST, IS_MANAGER ASC NULLS FIRST] LIMIT 1")
+    checkAnswer(df6, Seq(Row(0.00, false, 12000.00)))
+
+    val df7 = spark.read
+      .table("h2.test.employee")
+      .select($"DEPT", $"SALARY")
+      .groupBy("dept").agg(sum("SALARY"))
+      .orderBy(sum("SALARY"))
+      .limit(1)
+    checkSortRemoved(df7, false)
+    checkLimitRemoved(df7, false)
+    checkPushedInfo(df7,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: [DEPT]",
+      "PushedFilters: []")
+    checkAnswer(df7, Seq(Row(6, 12000.00)))
+
+    val df8 = spark.read
+      .table("h2.test.employee")
+      .select($"DEPT", $"SALARY")
+      .groupBy("dept").agg(sum("SALARY").as("total"))
+      .orderBy("total")
+      .limit(1)
+    checkSortRemoved(df8, false)
+    checkLimitRemoved(df8, false)
+    checkPushedInfo(df8,
+      "PushedAggregates: [SUM(SALARY)]",
+      "PushedGroupByExpressions: [DEPT]",
+      "PushedFilters: []")
+    checkAnswer(df8, Seq(Row(6, 12000.00)))
+  }
+
+  test("scan with aggregate push-down and paging push-down") {

Review Comment:
   can we reduce the code duplication?
   ```
   Seq(true, false).foreach { hasOffset =>
     var df = ...
     if (hasOffset) df = df.offset(1)
     df = df.limit(1)
     ...
   }
   ```



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