[SPARK-36559][SQL][PYTHON] Create plans dedicated to distributed-sequence index for optimization

### What changes were proposed in this pull request?

This PR proposes to move distributed-sequence index implementation to SQL plan to leverage optimizations such as column pruning.

```python
import pyspark.pandas as ps
ps.set_option('compute.default_index_type', 'distributed-sequence')
ps.range(10).id.value_counts().to_frame().spark.explain()
```

**Before:**

```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#51L DESC NULLS LAST], true, 0
   +- Exchange rangepartitioning(count#51L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#70]
      +- HashAggregate(keys=[id#37L], functions=[count(1)], output=[__index_level_0__#48L, count#51L])
         +- Exchange hashpartitioning(id#37L, 200), ENSURE_REQUIREMENTS, [id=#67]
            +- HashAggregate(keys=[id#37L], functions=[partial_count(1)], output=[id#37L, count#63L])
               +- Project [id#37L]
                  +- Filter atleastnnonnulls(1, id#37L)
                     +- Scan ExistingRDD[__index_level_0__#36L,id#37L]
                        # ^^^ Base DataFrame created by the output RDD from zipWithIndex (and checkpointed)
```

**After:**

```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#275L DESC NULLS LAST], true, 0
   +- Exchange rangepartitioning(count#275L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#174]
      +- HashAggregate(keys=[id#258L], functions=[count(1)])
         +- HashAggregate(keys=[id#258L], functions=[partial_count(1)])
            +- Filter atleastnnonnulls(1, id#258L)
               +- Range (0, 10, step=1, splits=16)
                  # ^^^ Removed the Spark job execution for `zipWithIndex`
```

### Why are the changes needed?

To leverage optimization of SQL engine and avoid unnecessary shuffle to create default index.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unittests were added. Also, this PR will test all unittests in pandas API on Spark after switching the default index implementation to `distributed-sequence`.

Closes #33807 from HyukjinKwon/SPARK-36559.

Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 93cec49212)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

python/pyspark/pandas/tests/test_dataframe.py

@@ -5160,26 +5160,25 @@ class DataFrameTest(PandasOnSparkTestCase, SQLTestUtils):
             sys.stdout = prev
 
     def test_explain_hint(self):
-        with ps.option_context("compute.default_index_type", "sequence"):
+        psdf1 = ps.DataFrame(
-            psdf1 = ps.DataFrame(
+            {"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 5]},
-                {"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 5]},
+            columns=["lkey", "value"],
-                columns=["lkey", "value"],
+        )
-            )
+        psdf2 = ps.DataFrame(
-            psdf2 = ps.DataFrame(
+            {"rkey": ["foo", "bar", "baz", "foo"], "value": [5, 6, 7, 8]},
-                {"rkey": ["foo", "bar", "baz", "foo"], "value": [5, 6, 7, 8]},
+            columns=["rkey", "value"],
-                columns=["rkey", "value"],
+        )
-            )
+        merged = psdf1.merge(psdf2.spark.hint("broadcast"), left_on="lkey", right_on="rkey")
-            merged = psdf1.merge(psdf2.spark.hint("broadcast"), left_on="lkey", right_on="rkey")
+        prev = sys.stdout
-            prev = sys.stdout
+        try:
-            try:
+            out = StringIO()
-                out = StringIO()
+            sys.stdout = out
-                sys.stdout = out
+            merged.spark.explain()
-                merged.spark.explain()
+            actual = out.getvalue().strip()
-                actual = out.getvalue().strip()
 
-                self.assertTrue("Broadcast" in actual, actual)
+            self.assertTrue("Broadcast" in actual, actual)
-            finally:
+        finally:
-                sys.stdout = prev
+            sys.stdout = prev
 
     def test_mad(self):
         pdf = pd.DataFrame(

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DeduplicateRelations.scala

@@ -225,6 +225,10 @@ object DeduplicateRelations extends Rule[LogicalPlan] {
         if oldVersion.outputSet.intersect(conflictingAttributes).nonEmpty =>
         Seq((oldVersion, oldVersion.copy(output = output.map(_.newInstance()))))
 
+      case oldVersion @ AttachDistributedSequence(sequenceAttr, _)
+        if oldVersion.producedAttributes.intersect(conflictingAttributes).nonEmpty =>
+        Seq((oldVersion, oldVersion.copy(sequenceAttr = sequenceAttr.newInstance())))
+
       case oldVersion: Generate
           if oldVersion.producedAttributes.intersect(conflictingAttributes).nonEmpty =>
         val newOutput = oldVersion.generatorOutput.map(_.newInstance())

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala

@@ -794,6 +794,11 @@ object ColumnPruning extends Rule[LogicalPlan] {
       }
       a.copy(child = Expand(newProjects, newOutput, grandChild))
 
+    // Prune and drop AttachDistributedSequence if the produced attribute is not referred.
+    case p @ Project(_, a @ AttachDistributedSequence(_, grandChild))
+        if !p.references.contains(a.sequenceAttr) =>
+      p.copy(child = prunedChild(grandChild, p.references))
+
     // Prunes the unused columns from child of `DeserializeToObject`
     case d @ DeserializeToObject(_, _, child) if !child.outputSet.subsetOf(d.references) =>
       d.copy(child = prunedChild(child, d.references))

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/pythonLogicalOperators.scala

@@ -115,3 +115,20 @@ case class ArrowEvalPython(
   override protected def withNewChildInternal(newChild: LogicalPlan): ArrowEvalPython =
     copy(child = newChild)
 }
+
+/**
+ * A logical plan that adds a new long column with the name `name` that
+ * increases one by one. This is for 'distributed-sequence' default index
+ * in pandas API on Spark.
+ */
+case class AttachDistributedSequence(
+    sequenceAttr: Attribute,
+    child: LogicalPlan) extends UnaryNode {
+
+  override val producedAttributes: AttributeSet = AttributeSet(sequenceAttr)
+
+  override val output: Seq[Attribute] = sequenceAttr +: child.output
+
+  override protected def withNewChildInternal(newChild: LogicalPlan): AttachDistributedSequence =
+    copy(child = newChild)
+}

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala

@@ -452,5 +452,11 @@ class ColumnPruningSuite extends PlanTest {
     val expected = input.where(rand(0L) > 0.5).where('key < 10).select('key).analyze
     comparePlans(optimized, expected)
   }
-  // todo: add more tests for column pruning
+
+  test("SPARK-36559 Prune and drop distributed-sequence if the produced column is not referred") {
+    val input = LocalRelation('a.int, 'b.int, 'c.int)
+    val plan1 = AttachDistributedSequence('d.int, input).select('a)
+    val correctAnswer1 = Project(Seq('a), input).analyze
+    comparePlans(Optimize.execute(plan1.analyze), correctAnswer1)
+  }
 }

sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala

@@ -3514,24 +3514,11 @@ class Dataset[T] private[sql](
    * This is for 'distributed-sequence' default index in pandas API on Spark.
    */
   private[sql] def withSequenceColumn(name: String) = {
-    val rdd: RDD[InternalRow] =
+    Dataset.ofRows(
-      // Checkpoint the DataFrame to fix the partition ID.
+      sparkSession,
-      localCheckpoint(false)
+      AttachDistributedSequence(
-      .queryExecution.toRdd.zipWithIndex().mapPartitions { iter =>
+        AttributeReference(name, LongType, nullable = false)(),
-      val joinedRow = new JoinedRow
+        logicalPlan))
-      val unsafeRowWriter =
-        new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1)
-
-      iter.map { case (row, id) =>
-        // Writes to an UnsafeRow directly
-        unsafeRowWriter.reset()
-        unsafeRowWriter.write(0, id)
-        joinedRow(unsafeRowWriter.getRow, row)
-      }
-    }
-
-    sparkSession.internalCreateDataFrame(
-      rdd, StructType(StructField(name, LongType, nullable = false) +: schema), isStreaming)
   }
 
   /**

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -712,6 +712,8 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           func, output, planLater(left), planLater(right)) :: Nil
       case logical.MapInPandas(func, output, child) =>
         execution.python.MapInPandasExec(func, output, planLater(child)) :: Nil
+      case logical.AttachDistributedSequence(attr, child) =>
+        execution.python.AttachDistributedSequenceExec(attr, planLater(child)) :: Nil
       case logical.MapElements(f, _, _, objAttr, child) =>
         execution.MapElementsExec(f, objAttr, planLater(child)) :: Nil
       case logical.AppendColumns(f, _, _, in, out, child) =>

sql/core/src/main/scala/org/apache/spark/sql/execution/python/AttachDistributedSequenceExec.scala

@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.python
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+
+/**
+ * A physical plan that adds a new long column with `sequenceAttr` that
+ * increases one by one. This is for 'distributed-sequence' default index
+ * in pandas API on Spark.
+ */
+case class AttachDistributedSequenceExec(
+    sequenceAttr: Attribute,
+    child: SparkPlan)
+  extends UnaryExecNode {
+
+  override def producedAttributes: AttributeSet = AttributeSet(sequenceAttr)
+
+  override val output: Seq[Attribute] = sequenceAttr +: child.output
+
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+
+  override protected def doExecute(): RDD[InternalRow] = {
+    child.execute().map(_.copy())
+        .localCheckpoint() // to avoid execute multiple jobs. zipWithIndex launches a Spark job.
+        .zipWithIndex().mapPartitions { iter =>
+      val unsafeProj = UnsafeProjection.create(output, output)
+      val joinedRow = new JoinedRow
+      val unsafeRowWriter =
+        new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1)
+
+      iter.map { case (row, id) =>
+        // Writes to an UnsafeRow directly
+        unsafeRowWriter.reset()
+        unsafeRowWriter.write(0, id)
+        joinedRow(unsafeRowWriter.getRow, row)
+      }.map(unsafeProj)
+    }
+  }
+
+  override protected def withNewChildInternal(newChild: SparkPlan): AttachDistributedSequenceExec =
+    copy(child = newChild)
+}