[SPARK-15979][SQL] Renames CatalystWriteSupport to ParquetWriteSupport

## What changes were proposed in this pull request?

PR #13696 renamed various Parquet support classes but left `CatalystWriteSupport` behind. This PR is renames it as a follow-up.

## How was this patch tested?

N/A.

Author: Cheng Lian <lian@databricks.com>

Closes #14070 from liancheng/spark-15979-follow-up.

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

@@ -99,13 +99,13 @@ private[sql] class ParquetFileFormat
     // bundled with `ParquetOutputFormat[Row]`.
     job.setOutputFormatClass(classOf[ParquetOutputFormat[Row]])
 
-    ParquetOutputFormat.setWriteSupportClass(job, classOf[CatalystWriteSupport])
+    ParquetOutputFormat.setWriteSupportClass(job, classOf[ParquetWriteSupport])
 
     // We want to clear this temporary metadata from saving into Parquet file.
     // This metadata is only useful for detecting optional columns when pushdowning filters.
     val dataSchemaToWrite = StructType.removeMetadata(StructType.metadataKeyForOptionalField,
       dataSchema).asInstanceOf[StructType]
-    CatalystWriteSupport.setSchema(dataSchemaToWrite, conf)
+    ParquetWriteSupport.setSchema(dataSchemaToWrite, conf)
 
     // Sets flags for `CatalystSchemaConverter` (which converts Catalyst schema to Parquet schema)
     // and `CatalystWriteSupport` (writing actual rows to Parquet files).
@@ -295,14 +295,14 @@ private[sql] class ParquetFileFormat
       ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
       ParquetSchemaConverter.checkFieldNames(requiredSchema).json)
     hadoopConf.set(
-      CatalystWriteSupport.SPARK_ROW_SCHEMA,
+      ParquetWriteSupport.SPARK_ROW_SCHEMA,
       ParquetSchemaConverter.checkFieldNames(requiredSchema).json)
 
     // We want to clear this temporary metadata from saving into Parquet file.
     // This metadata is only useful for detecting optional columns when pushdowning filters.
     val dataSchemaToWrite = StructType.removeMetadata(StructType.metadataKeyForOptionalField,
       requiredSchema).asInstanceOf[StructType]
-    CatalystWriteSupport.setSchema(dataSchemaToWrite, hadoopConf)
+    ParquetWriteSupport.setSchema(dataSchemaToWrite, hadoopConf)
 
     // Sets flags for `CatalystSchemaConverter`
     hadoopConf.setBoolean(
@@ -435,14 +435,14 @@ private[sql] class ParquetOutputWriterFactory(
     // bundled with `ParquetOutputFormat[Row]`.
     job.setOutputFormatClass(classOf[ParquetOutputFormat[Row]])
 
-    ParquetOutputFormat.setWriteSupportClass(job, classOf[CatalystWriteSupport])
+    ParquetOutputFormat.setWriteSupportClass(job, classOf[ParquetWriteSupport])
 
     // We want to clear this temporary metadata from saving into Parquet file.
     // This metadata is only useful for detecting optional columns when pushdowning filters.
     val dataSchemaToWrite = StructType.removeMetadata(
       StructType.metadataKeyForOptionalField,
       dataSchema).asInstanceOf[StructType]
-    CatalystWriteSupport.setSchema(dataSchemaToWrite, conf)
+    ParquetWriteSupport.setSchema(dataSchemaToWrite, conf)
 
     // Sets flags for `CatalystSchemaConverter` (which converts Catalyst schema to Parquet schema)
     // and `CatalystWriteSupport` (writing actual rows to Parquet files).
@@ -611,7 +611,7 @@ private[sql] object ParquetFileFormat extends Logging {
     })
 
     conf.set(
-      CatalystWriteSupport.SPARK_ROW_SCHEMA,
+      ParquetWriteSupport.SPARK_ROW_SCHEMA,
       ParquetSchemaConverter.checkFieldNames(dataSchema).json)
 
     // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystWriteSupport.scala → sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetWriteSupport.scala

@@ -48,7 +48,7 @@ import org.apache.spark.sql.types._
  * of this option is propagated to this class by the `init()` method and its Hadoop configuration
  * argument.
  */
-private[parquet] class CatalystWriteSupport extends WriteSupport[InternalRow] with Logging {
+private[parquet] class ParquetWriteSupport extends WriteSupport[InternalRow] with Logging {
   // A `ValueWriter` is responsible for writing a field of an `InternalRow` to the record consumer.
   // Here we are using `SpecializedGetters` rather than `InternalRow` so that we can directly access
   // data in `ArrayData` without the help of `SpecificMutableRow`.
@@ -73,7 +73,7 @@ private[parquet] class CatalystWriteSupport extends WriteSupport[InternalRow] wi
   private val decimalBuffer = new Array[Byte](minBytesForPrecision(DecimalType.MAX_PRECISION))
 
   override def init(configuration: Configuration): WriteContext = {
-    val schemaString = configuration.get(CatalystWriteSupport.SPARK_ROW_SCHEMA)
+    val schemaString = configuration.get(ParquetWriteSupport.SPARK_ROW_SCHEMA)
     this.schema = StructType.fromString(schemaString)
     this.writeLegacyParquetFormat = {
       // `SQLConf.PARQUET_WRITE_LEGACY_FORMAT` should always be explicitly set in ParquetRelation
@@ -424,7 +424,7 @@ private[parquet] class CatalystWriteSupport extends WriteSupport[InternalRow] wi
   }
 }
 
-private[parquet] object CatalystWriteSupport {
+private[parquet] object ParquetWriteSupport {
   val SPARK_ROW_SCHEMA: String = "org.apache.spark.sql.parquet.row.attributes"
 
   def setSchema(schema: StructType, configuration: Configuration): Unit = {