[SPARK-44342][SQL] Replace SQLContext with SparkSession for GenTPCDSData

### What changes were proposed in this pull request?
The `SQLContext` is an old API for Spark SQL.
But `GenTPCDSData` still use it directly.

### Why are the changes needed?
Avoid use the legacy API for `GenTPCDSData`.

### Does this PR introduce _any_ user-facing change?
'No'.
Just update the benchmark utils.

### How was this patch tested?
Manual test by running:
`build/sbt "sql/Test/runMain org.apache.spark.sql.GenTPCDSData --dsdgenDir ... --location ... --scaleFactor 1"`

Closes #41900 from beliefer/SPARK-44342.

Authored-by: Jiaan Geng <[email protected]>
Signed-off-by: Kent Yao <[email protected]>

Author: beliefer

sql/core/src/test/scala/org/apache/spark/sql/GenTPCDSData.scala

@@ -27,6 +27,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.internal.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.functions.{col, rpad}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types.{CharType, StringType, StructField, StructType, VarcharType}
 
 // The classes in this file are basically moved from https://github.com/databricks/spark-sql-perf
@@ -120,7 +121,7 @@ class Dsdgen(dsdgenDir: String) extends Serializable {
   }
 }
 
-class TPCDSTables(sqlContext: SQLContext, dsdgenDir: String, scaleFactor: Int)
+class TPCDSTables(spark: SparkSession, dsdgenDir: String, scaleFactor: Int)
   extends TPCDSSchema with Logging with Serializable {
 
   private val dataGenerator = new Dsdgen(dsdgenDir)
@@ -138,7 +139,7 @@ class TPCDSTables(sqlContext: SQLContext, dsdgenDir: String, scaleFactor: Int)
 
     private def df(numPartition: Int) = {
       val generatedData = dataGenerator.generate(
-        sqlContext.sparkContext, name, numPartition, scaleFactor)
+        spark.sparkContext, name, numPartition, scaleFactor)
       val rows = generatedData.mapPartitions { iter =>
         iter.map { l =>
           val values = l.split("\\|", -1).dropRight(1).map { v =>
@@ -154,7 +155,7 @@ class TPCDSTables(sqlContext: SQLContext, dsdgenDir: String, scaleFactor: Int)
       }
 
       val stringData =
-        sqlContext.createDataFrame(
+        spark.createDataFrame(
           rows,
           StructType(schema.fields.map(f => StructField(f.name, StringType))))
 
@@ -210,7 +211,7 @@ class TPCDSTables(sqlContext: SQLContext, dsdgenDir: String, scaleFactor: Int)
                |DISTRIBUTE BY
                |  $partitionColumnString
             """.stripMargin
-          val grouped = sqlContext.sql(query)
+          val grouped = spark.sql(query)
           logInfo(s"Pre-clustering with partitioning columns with query $query.")
           grouped.write
         } else {
@@ -223,9 +224,7 @@ class TPCDSTables(sqlContext: SQLContext, dsdgenDir: String, scaleFactor: Int)
           // datagen speed files will be truncated to maxRecordsPerFile value, so the final
           // result will be the same.
           val numRows = data.count
-          val maxRecordPerFile = Try {
-            sqlContext.getConf("spark.sql.files.maxRecordsPerFile").toInt
-          }.getOrElse(0)
+          val maxRecordPerFile = spark.conf.get(SQLConf.MAX_RECORDS_PER_FILE)
 
           if (maxRecordPerFile > 0 && numRows > maxRecordPerFile) {
             val numFiles = (numRows.toDouble/maxRecordPerFile).ceil.toInt
@@ -244,7 +243,7 @@ class TPCDSTables(sqlContext: SQLContext, dsdgenDir: String, scaleFactor: Int)
       }
       logInfo(s"Generating table $name in database to $location with save mode $mode.")
       writer.save(location)
-      sqlContext.dropTempTable(tempTableName)
+      spark.catalog.dropTempView(tempTableName)
     }
   }
 
@@ -429,7 +428,7 @@ object GenTPCDSData {
       .getOrCreate()
 
     val tables = new TPCDSTables(
-      spark.sqlContext,
+      spark,
       dsdgenDir = config.dsdgenDir,
       scaleFactor = config.scaleFactor)