AnishMahto commented on code in PR #56124:
URL: https://github.com/apache/spark/pull/56124#discussion_r3307744746


##########
sql/connect/server/src/test/scala/org/apache/spark/sql/connect/pipelines/PythonPipelineSuite.scala:
##########
@@ -935,6 +937,286 @@ class PythonPipelineSuite
     assert(ex.getMessage.contains("table_with_wrong_struct_schema"))
   }
 
+  private def buildAutoCdcFlow(pipelineSource: String): AutoCdcFlow = {
+    val graph = buildGraph(pipelineSource)
+    graph.flows
+      .collectFirst { case f: AutoCdcFlow => f }
+      .getOrElse(
+        throw new AssertionError(
+          s"Expected an AutoCdcFlow in the graph, got: ${graph.flows}"))
+  }
+
+  test("AutoCDC API: minimal flow registers an AutoCdcFlow with default name 
and SCD1 default") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |)
+        |""".stripMargin)
+
+    assert(flow.identifier == graphIdentifier("target"))
+    assert(flow.destinationIdentifier == graphIdentifier("target"))
+    assert(flow.changeArgs.keys == Seq(UnqualifiedColumnName("value")))
+    assert(flow.changeArgs.sequencing.expr.sql == "timestamp")
+    assert(flow.changeArgs.deleteCondition.isEmpty)
+    assert(flow.changeArgs.columnSelection.isEmpty)
+    assert(flow.changeArgs.storedAsScdType == ScdType.Type1)
+  }
+
+  test("AutoCDC API: composite keys are forwarded to ChangeArgs in order") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value", "timestamp"],
+        |    sequence_by = "timestamp",
+        |)
+        |""".stripMargin)
+
+    assert(flow.changeArgs.keys ==
+      Seq(UnqualifiedColumnName("value"), UnqualifiedColumnName("timestamp")))
+  }
+
+  test("AutoCDC API: apply_as_deletes is forwarded as a delete condition 
column") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |    apply_as_deletes = "value % 2 = 0",
+        |)
+        |""".stripMargin)
+
+    val deleteCondition = flow.changeArgs.deleteCondition.getOrElse(
+      throw new AssertionError("expected apply_as_deletes to populate 
deleteCondition"))
+    assert(deleteCondition.expr.sql.contains("value"))
+    assert(deleteCondition.expr.sql.contains("0"))
+  }
+
+  test("AutoCDC API: column_list is forwarded as IncludeColumns") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |    column_list = ["value", "timestamp"],
+        |)
+        |""".stripMargin)
+
+    assert(flow.changeArgs.columnSelection.contains(
+      ColumnSelection.IncludeColumns(
+        Seq(UnqualifiedColumnName("value"), 
UnqualifiedColumnName("timestamp")))))
+  }
+
+  test("AutoCDC API: except_column_list is forwarded as ExcludeColumns") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |    except_column_list = ["timestamp"],
+        |)
+        |""".stripMargin)
+
+    assert(flow.changeArgs.columnSelection.contains(
+      ColumnSelection.ExcludeColumns(Seq(UnqualifiedColumnName("timestamp")))))
+  }
+
+  test("AutoCDC API: explicit `name` is honored as the flow identifier") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |    name = "my_flow",
+        |)
+        |""".stripMargin)
+
+    assert(flow.identifier == graphIdentifier("my_flow"))
+    assert(flow.destinationIdentifier == graphIdentifier("target"))
+  }
+
+  test("AutoCDC API: omitted stored_as_scd_type defaults to ScdType.Type1") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |)
+        |""".stripMargin)
+
+    assert(flow.changeArgs.storedAsScdType == ScdType.Type1)
+  }
+
+  test("AutoCDC API: explicit stored_as_scd_type=1 is forwarded as 
ScdType.Type1") {
+    val flow = buildAutoCdcFlow(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |    stored_as_scd_type = 1,
+        |)
+        |""".stripMargin)
+
+    assert(flow.changeArgs.storedAsScdType == ScdType.Type1)
+  }
+
+  test("AutoCDC API: multi-part `keys` column is rejected at flow 
registration") {
+    val ex = intercept[RuntimeException] {
+      buildAutoCdcFlow(
+        """
+          |@dp.table
+          |def src():
+          |  return spark.readStream.format("rate").load()
+          |
+          |dp.create_streaming_table("target")
+          |
+          |dp.create_auto_cdc_flow(
+          |    target = "target",
+          |    source = "src",
+          |    keys = ["a.b"],
+          |    sequence_by = "timestamp",
+          |)
+          |""".stripMargin)
+    }
+    assert(ex.getMessage.contains("AUTOCDC_MULTIPART_COLUMN_IDENTIFIER"))
+  }
+
+  test("AutoCDC API: multi-part `column_list` entry is rejected at flow 
registration") {
+    val ex = intercept[RuntimeException] {
+      buildAutoCdcFlow(
+        """
+          |@dp.table
+          |def src():
+          |  return spark.readStream.format("rate").load()
+          |
+          |dp.create_streaming_table("target")
+          |
+          |dp.create_auto_cdc_flow(
+          |    target = "target",
+          |    source = "src",
+          |    keys = ["value"],
+          |    sequence_by = "timestamp",
+          |    column_list = ["nested.field"],
+          |)
+          |""".stripMargin)
+    }
+    assert(ex.getMessage.contains("AUTOCDC_MULTIPART_COLUMN_IDENTIFIER"))
+  }
+
+  test("AutoCDC API: Column-object form of keys/sequence_by/apply_as_deletes 
is honored") {
+    val flow = buildAutoCdcFlow(
+      """
+        |from pyspark.sql.functions import col, expr
+        |
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = [col("value")],
+        |    sequence_by = col("timestamp"),
+        |    apply_as_deletes = expr("value % 2 = 0"),
+        |)
+        |""".stripMargin)
+
+    assert(flow.changeArgs.keys == Seq(UnqualifiedColumnName("value")))
+    assert(flow.changeArgs.sequencing.expr.sql == "timestamp")
+    val deleteCondition = flow.changeArgs.deleteCondition.getOrElse(
+      throw new AssertionError("expected apply_as_deletes to populate 
deleteCondition"))
+    assert(deleteCondition.expr.sql.contains("value"))
+    assert(deleteCondition.expr.sql.contains("0"))
+  }
+
+  test("AutoCDC API: graph resolves with the source streaming table as the 
flow's input") {
+    val graph = buildGraph(
+      """
+        |@dp.table
+        |def src():
+        |  return spark.readStream.format("rate").load()
+        |
+        |dp.create_streaming_table("target")
+        |
+        |dp.create_auto_cdc_flow(
+        |    target = "target",
+        |    source = "src",
+        |    keys = ["value"],
+        |    sequence_by = "timestamp",
+        |)
+        |""".stripMargin).resolve()
+
+    val resolvedFlow = graph.resolvedFlow(graphIdentifier("target"))
+    assert(resolvedFlow.inputs == Set(graphIdentifier("src")))
+  }

Review Comment:
   Added:
   1. `AutoCDC API: multi-part `source` resolves to the corresponding qualified 
dataset`
   2. `AutoCDC API: specifying both column_list and except_column_list is 
rejected`
   3. `AutoCDC API: registered flow survives graph resolution and validation 
end-to-end`



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