[SPARK-31220][SQL] repartition obeys initialPartitionNum when adaptiveExecutionEnabled

### What changes were proposed in this pull request?
This PR makes `repartition`/`DISTRIBUTE BY` obeys [initialPartitionNum](https://github.com/apache/spark/blob/af4248b2d661d04fec89b37857a47713246d9465/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L446-L455) when adaptive execution enabled.

### Why are the changes needed?
To make `DISTRIBUTE BY`/`GROUP BY` partitioned by same partition number.
How to reproduce:
```scala
spark.sql("CREATE TABLE spark_31220(id int)")
spark.sql("set spark.sql.adaptive.enabled=true")
spark.sql("set spark.sql.adaptive.coalescePartitions.initialPartitionNum=1000")
```

Before this PR:
```
scala> spark.sql("SELECT id from spark_31220 GROUP BY id").explain
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=false)
+- HashAggregate(keys=[id#5], functions=[])
   +- Exchange hashpartitioning(id#5, 1000), true, [id=#171]
      +- HashAggregate(keys=[id#5], functions=[])
         +- FileScan parquet default.spark_31220[id#5]

scala> spark.sql("SELECT id from spark_31220 DISTRIBUTE BY id").explain
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=false)
+- Exchange hashpartitioning(id#5, 200), false, [id=#179]
   +- FileScan parquet default.spark_31220[id#5]
```
After this PR:
```
scala> spark.sql("SELECT id from spark_31220 GROUP BY id").explain
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=false)
+- HashAggregate(keys=[id#5], functions=[])
   +- Exchange hashpartitioning(id#5, 1000), true, [id=#171]
      +- HashAggregate(keys=[id#5], functions=[])
         +- FileScan parquet default.spark_31220[id#5]

scala> spark.sql("SELECT id from spark_31220 DISTRIBUTE BY id").explain
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=false)
+- Exchange hashpartitioning(id#5, 1000), false, [id=#179]
   +- FileScan parquet default.spark_31220[id#5]
```

### Does this PR introduce any user-facing change?
No.

### How was this patch tested?
Unit test.

Closes #27986 from wangyum/SPARK-31220.

Authored-by: Yuming Wang <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
(cherry picked from commit 1d1eacd)
Signed-off-by: Wenchen Fan <[email protected]>

Author: wangyum

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -2765,7 +2765,15 @@ class SQLConf extends Serializable with Logging {
 
   def cacheVectorizedReaderEnabled: Boolean = getConf(CACHE_VECTORIZED_READER_ENABLED)
 
-  def numShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS)
+  def defaultNumShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS)
+
+  def numShufflePartitions: Int = {
+    if (adaptiveExecutionEnabled && coalesceShufflePartitionsEnabled) {
+      getConf(COALESCE_PARTITIONS_INITIAL_PARTITION_NUM).getOrElse(defaultNumShufflePartitions)
+    } else {
+      defaultNumShufflePartitions
+    }
+  }
 
   def adaptiveExecutionEnabled: Boolean = getConf(ADAPTIVE_EXECUTION_ENABLED)
 
@@ -2778,9 +2786,6 @@ class SQLConf extends Serializable with Logging {
 
   def coalesceShufflePartitionsEnabled: Boolean = getConf(COALESCE_PARTITIONS_ENABLED)
 
-  def initialShufflePartitionNum: Int =
-    getConf(COALESCE_PARTITIONS_INITIAL_PARTITION_NUM).getOrElse(numShufflePartitions)
-
   def minBatchesToRetain: Int = getConf(MIN_BATCHES_TO_RETAIN)
 
   def maxBatchesToRetainInMemory: Int = getConf(MAX_BATCHES_TO_RETAIN_IN_MEMORY)

sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala

@@ -35,12 +35,6 @@ import org.apache.spark.sql.internal.SQLConf
  * the input partition ordering requirements are met.
  */
 case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] {
-  private def defaultNumPreShufflePartitions: Int =
-    if (conf.adaptiveExecutionEnabled && conf.coalesceShufflePartitionsEnabled) {
-      conf.initialShufflePartitionNum
-    } else {
-      conf.numShufflePartitions
-    }
 
   private def ensureDistributionAndOrdering(operator: SparkPlan): SparkPlan = {
     val requiredChildDistributions: Seq[Distribution] = operator.requiredChildDistribution
@@ -57,7 +51,7 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] {
         BroadcastExchangeExec(mode, child)
       case (child, distribution) =>
         val numPartitions = distribution.requiredNumPartitions
-          .getOrElse(defaultNumPreShufflePartitions)
+          .getOrElse(conf.numShufflePartitions)
         ShuffleExchangeExec(distribution.createPartitioning(numPartitions), child)
     }
 
@@ -95,7 +89,7 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] {
         // expected number of shuffle partitions. However, if it's smaller than
         // `conf.numShufflePartitions`, we pick `conf.numShufflePartitions` as the
         // expected number of shuffle partitions.
-        math.max(nonShuffleChildrenNumPartitions.max, conf.numShufflePartitions)
+        math.max(nonShuffleChildrenNumPartitions.max, conf.defaultNumShufflePartitions)
       } else {
         childrenNumPartitions.max
       }

sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala

@@ -873,4 +873,20 @@ class AdaptiveQueryExecSuite
       }
     }
   }
+
+  test("SPARK-31220 repartition obeys initialPartitionNum when adaptiveExecutionEnabled") {
+    Seq(true, false).foreach { enableAQE =>
+      withSQLConf(
+        SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> enableAQE.toString,
+        SQLConf.SHUFFLE_PARTITIONS.key -> "6",
+        SQLConf.COALESCE_PARTITIONS_INITIAL_PARTITION_NUM.key -> "7") {
+        val partitionsNum = spark.range(10).repartition($"id").rdd.collectPartitions().length
+        if (enableAQE) {
+          assert(partitionsNum === 7)
+        } else {
+          assert(partitionsNum === 6)
+        }
+      }
+    }
+  }
 }