[SPARK-50157][SQL] Using SQLConf provided by SparkSession first

### What changes were proposed in this pull request?
This PR proposes to use `SQLConf` provided by `SparkSession` first.

### Why are the changes needed?
`SQLConf` provided by `SparkSession` have better perf than `SQLConf.get`.

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

### How was this patch tested?
GA tests.
The benchmark test.
```
object SQLConfBenchmark extends SqlBasedBenchmark {
  override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
    runBenchmark("Get SQLConf") {
      val iters = 1000
      val benchmark = new Benchmark("Benchmark SQLConf", iters, output = output)
      benchmark.addCase("SQLConf.get") { _ =>
        for (_ <- 1 to iters) {
          val conf = SQLConf.get
        }
      }

      benchmark.addCase("sessionState.conf") { _ =>
        for (_ <- 1 to iters) {
          val conf = spark.sessionState.conf
        }
      }

      benchmark.run()
    }
  }
}
```
The benchmark output.
```
Benchmark SQLConf:                        Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
SQLConf.get                                           0              0           1         22.9          43.6       1.0X
sessionState.conf                                     0              0           0       1377.4           0.7      60.1X
```

### Was this patch authored or co-authored using generative AI tooling?
'No'.

Closes apache#48693 from beliefer/SPARK-50157.

Authored-by: beliefer <[email protected]>
Signed-off-by: beliefer <[email protected]>

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

@@ -25,10 +25,13 @@ import org.apache.spark.sql.execution.adaptive.LogicalQueryStageStrategy
 import org.apache.spark.sql.execution.command.v2.V2CommandStrategy
 import org.apache.spark.sql.execution.datasources.{DataSourceStrategy, FileSourceStrategy}
 import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Strategy
+import org.apache.spark.sql.internal.SQLConf
 
 class SparkPlanner(val session: SparkSession, val experimentalMethods: ExperimentalMethods)
   extends SparkStrategies with SQLConfHelper {
 
+  override def conf: SQLConf = session.sessionState.conf
+
   def numPartitions: Int = conf.numShufflePartitions
 
   override def strategies: Seq[Strategy] =

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala

@@ -33,6 +33,8 @@ import org.apache.spark.util.Utils
  */
 case class CoalesceShufflePartitions(session: SparkSession) extends AQEShuffleReadRule {
 
+  override def conf: SQLConf = session.sessionState.conf
+
   override val supportedShuffleOrigins: Seq[ShuffleOrigin] =
     Seq(ENSURE_REQUIREMENTS, REPARTITION_BY_COL, REBALANCE_PARTITIONS_BY_NONE,
       REBALANCE_PARTITIONS_BY_COL)

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/InsertAdaptiveSparkPlan.scala

@@ -44,6 +44,8 @@ import org.apache.spark.sql.internal.SQLConf
 case class InsertAdaptiveSparkPlan(
     adaptiveExecutionContext: AdaptiveExecutionContext) extends Rule[SparkPlan] {
 
+  override def conf: SQLConf = adaptiveExecutionContext.session.sessionState.conf
+
   override def apply(plan: SparkPlan): SparkPlan = applyInternal(plan, false)
 
   private def applyInternal(plan: SparkPlan, isSubquery: Boolean): SparkPlan = plan match {

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/PlanAdaptiveDynamicPruningFilters.scala

@@ -25,12 +25,16 @@ import org.apache.spark.sql.catalyst.trees.TreePattern._
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.exchange.BroadcastExchangeExec
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, HashedRelationBroadcastMode, HashJoin}
+import org.apache.spark.sql.internal.SQLConf
 
 /**
  * A rule to insert dynamic pruning predicates in order to reuse the results of broadcast.
  */
 case class PlanAdaptiveDynamicPruningFilters(
     rootPlan: AdaptiveSparkPlanExec) extends Rule[SparkPlan] with AdaptiveSparkPlanHelper {
+
+  override def conf: SQLConf = rootPlan.context.session.sessionState.conf
+
   def apply(plan: SparkPlan): SparkPlan = {
     if (!conf.dynamicPartitionPruningEnabled) {
       return plan

sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala

@@ -196,7 +196,8 @@ case class DescribeDatabaseCommand(
         if (properties.isEmpty) {
           ""
         } else {
-          conf.redactOptions(properties).toSeq.sortBy(_._1).mkString("(", ", ", ")")
+          sparkSession.sessionState.conf.redactOptions(properties).toSeq
+            .sortBy(_._1).mkString("(", ", ", ")")
         }
       result :+ Row("Properties", propertiesStr)
     } else {
@@ -548,7 +549,7 @@ case class AlterTableAddPartitionCommand(
     // Hive metastore may not have enough memory to handle millions of partitions in single RPC.
     // Also the request to metastore times out when adding lot of partitions in one shot.
     // we should split them into smaller batches
-    val batchSize = conf.getConf(SQLConf.ADD_PARTITION_BATCH_SIZE)
+    val batchSize = sparkSession.sessionState.conf.getConf(SQLConf.ADD_PARTITION_BATCH_SIZE)
     parts.iterator.grouped(batchSize).foreach { batch =>
       catalog.createPartitions(table.identifier, batch, ignoreIfExists = ifNotExists)
     }

sql/core/src/main/scala/org/apache/spark/sql/execution/command/tables.scala

@@ -240,7 +240,7 @@ case class AlterTableAddColumnsCommand(
 
     SchemaUtils.checkColumnNameDuplication(
       (colsWithProcessedDefaults ++ catalogTable.schema).map(_.name),
-      conf.caseSensitiveAnalysis)
+      sparkSession.sessionState.conf.caseSensitiveAnalysis)
     if (!conf.allowCollationsInMapKeys) {
       colsToAdd.foreach(col => SchemaUtils.checkNoCollationsInMapKeys(col.dataType))
     }
@@ -501,7 +501,7 @@ case class TruncateTableCommand(
         partLocations
       }
     val hadoopConf = spark.sessionState.newHadoopConf()
-    val ignorePermissionAcl = conf.truncateTableIgnorePermissionAcl
+    val ignorePermissionAcl = spark.sessionState.conf.truncateTableIgnorePermissionAcl
     locations.foreach { location =>
       if (location.isDefined) {
         val path = new Path(location.get)
@@ -819,7 +819,8 @@ case class DescribeColumnCommand(
 
     val catalogTable = catalog.getTempViewOrPermanentTableMetadata(table)
     val colStatsMap = catalogTable.stats.map(_.colStats).getOrElse(Map.empty)
-    val colStats = if (conf.caseSensitiveAnalysis) colStatsMap else CaseInsensitiveMap(colStatsMap)
+    val colStats = if (sparkSession.sessionState.conf.caseSensitiveAnalysis) colStatsMap
+      else CaseInsensitiveMap(colStatsMap)
     val cs = colStats.get(field.name)
 
     val comment = if (field.metadata.contains("comment")) {
@@ -975,7 +976,7 @@ case class ShowTablePropertiesCommand(
       Seq.empty[Row]
     } else {
       val catalogTable = catalog.getTableMetadata(table)
-      val properties = conf.redactOptions(catalogTable.properties)
+      val properties = sparkSession.sessionState.conf.redactOptions(catalogTable.properties)
       propertyKey match {
         case Some(p) =>
           val propValue = properties

sql/core/src/main/scala/org/apache/spark/sql/execution/command/views.scala

@@ -729,7 +729,8 @@ object ViewHelper extends SQLConfHelper with Logging {
     val uncache = getRawTempView(name.table).map { r =>
       needsToUncache(r, aliasedPlan)
     }.getOrElse(false)
-    val storeAnalyzedPlanForView = conf.storeAnalyzedPlanForView || originalText.isEmpty
+    val storeAnalyzedPlanForView = session.sessionState.conf.storeAnalyzedPlanForView ||
+      originalText.isEmpty
     if (replace && uncache) {
       logDebug(s"Try to uncache ${name.quotedString} before replacing.")
       if (!storeAnalyzedPlanForView) {

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala

@@ -37,6 +37,7 @@ import org.apache.spark.sql.execution.command.DDLUtils
 import org.apache.spark.sql.execution.command.ViewHelper.generateViewProperties
 import org.apache.spark.sql.execution.datasources.{CreateTable => CreateTableV1}
 import org.apache.spark.sql.execution.datasources.v2.FileDataSourceV2
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.sources.InsertableRelation
 import org.apache.spark.sql.types.{StructField, StructType}
 import org.apache.spark.sql.util.PartitioningUtils.normalizePartitionSpec
@@ -47,6 +48,9 @@ import org.apache.spark.util.ArrayImplicits._
  * Replaces [[UnresolvedRelation]]s if the plan is for direct query on files.
  */
 class ResolveSQLOnFile(sparkSession: SparkSession) extends Rule[LogicalPlan] {
+
+  override def conf: SQLConf = sparkSession.sessionState.conf
+
   object UnresolvedRelationResolution {
     def unapply(plan: LogicalPlan): Option[LogicalPlan] = {
       plan match {

sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/PlanDynamicPruningFilters.scala

@@ -28,6 +28,7 @@ import org.apache.spark.sql.catalyst.trees.TreePattern.DYNAMIC_PRUNING_SUBQUERY
 import org.apache.spark.sql.execution.{InSubqueryExec, QueryExecution, SparkPlan, SubqueryBroadcastExec}
 import org.apache.spark.sql.execution.exchange.BroadcastExchangeExec
 import org.apache.spark.sql.execution.joins._
+import org.apache.spark.sql.internal.SQLConf
 
 /**
  * This planner rule aims at rewriting dynamic pruning predicates in order to reuse the
@@ -36,6 +37,8 @@ import org.apache.spark.sql.execution.joins._
 */
 case class PlanDynamicPruningFilters(sparkSession: SparkSession) extends Rule[SparkPlan] {
 
+  override def conf: SQLConf = sparkSession.sessionState.conf
+
   /**
    * Identify the shape in which keys of a given plan are broadcasted.
    */

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala

@@ -35,7 +35,7 @@ import org.apache.spark.sql.execution.datasources.{CreateTable, DataSourceStrate
 import org.apache.spark.sql.hive.execution._
 import org.apache.spark.sql.hive.execution.HiveScriptTransformationExec
 import org.apache.spark.sql.hive.execution.InsertIntoHiveTable.BY_CTAS
-import org.apache.spark.sql.internal.HiveSerDe
+import org.apache.spark.sql.internal.{HiveSerDe, SQLConf}
 
 
 /**
@@ -117,6 +117,9 @@ class ResolveHiveSerdeTable(session: SparkSession) extends Rule[LogicalPlan] {
 }
 
 class DetermineTableStats(session: SparkSession) extends Rule[LogicalPlan] {
+
+  override def conf: SQLConf = session.sessionState.conf
+
   private def hiveTableWithStats(relation: HiveTableRelation): HiveTableRelation = {
     val table = relation.tableMeta
     val partitionCols = relation.partitionCols

sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/PruneHiveTablePartitions.scala

@@ -28,6 +28,7 @@ import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan, Project
 import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.FilterEstimation
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.execution.datasources.DataSourceStrategy
+import org.apache.spark.sql.internal.SQLConf
 
 /**
  * Prune hive table partitions using partition filters on [[HiveTableRelation]]. The pruned
@@ -43,6 +44,8 @@ import org.apache.spark.sql.execution.datasources.DataSourceStrategy
 private[sql] class PruneHiveTablePartitions(session: SparkSession)
   extends Rule[LogicalPlan] with CastSupport with PredicateHelper {
 
+  override def conf: SQLConf = session.sessionState.conf
+
   /**
    * Extract the partition filters from the filters on the table.
    */