[SQL][MINOR] A few minor tweaks to CSV reader.

This pull request simply fixes a few minor coding style issues in csv, as I was reviewing the change post-hoc.

Author: Reynold Xin <rxin@databricks.com>

Closes #10919 from rxin/csv-minor.

2 files changed, 9 insertions, 14 deletions

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
index 0aa4539e60..ace8cd7ad8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
@@ -30,16 +30,15 @@ import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
 import org.apache.spark.sql.types._
 
 
-private[sql] object CSVInferSchema {
+private[csv] object CSVInferSchema {
 
   /**
     * Similar to the JSON schema inference
     *     1. Infer type of each row
     *     2. Merge row types to find common type
     *     3. Replace any null types with string type
-    * TODO(hossein): Can we reuse JSON schema inference? [SPARK-12670]
     */
-  def apply(
+  def infer(
       tokenRdd: RDD[Array[String]],
       header: Array[String],
       nullValue: String = ""): StructType = {
@@ -65,10 +64,7 @@ private[sql] object CSVInferSchema {
     rowSoFar
   }
 
-  private[csv] def mergeRowTypes(
-      first: Array[DataType],
-      second: Array[DataType]): Array[DataType] = {
-
+  def mergeRowTypes(first: Array[DataType], second: Array[DataType]): Array[DataType] = {
     first.zipAll(second, NullType, NullType).map { case ((a, b)) =>
       val tpe = findTightestCommonType(a, b).getOrElse(StringType)
       tpe match {
@@ -82,8 +78,7 @@ private[sql] object CSVInferSchema {
     * Infer type of string field. Given known type Double, and a string "1", there is no
     * point checking if it is an Int, as the final type must be Double or higher.
     */
-  private[csv] def inferField(
-      typeSoFar: DataType, field: String, nullValue: String = ""): DataType = {
+  def inferField(typeSoFar: DataType, field: String, nullValue: String = ""): DataType = {
     if (field == null || field.isEmpty || field == nullValue) {
       typeSoFar
     } else {
@@ -155,7 +150,8 @@ private[sql] object CSVInferSchema {
   }
 }
 
-object CSVTypeCast {
+
+private[csv] object CSVTypeCast {
 
   /**
    * Casts given string datum to specified type.
@@ -167,7 +163,7 @@ object CSVTypeCast {
    * @param datum string value
    * @param castType SparkSQL type
    */
-  private[csv] def castTo(
+  def castTo(
       datum: String,
       castType: DataType,
       nullable: Boolean = true,
@@ -201,10 +197,9 @@ object CSVTypeCast {
    * Helper method that converts string representation of a character to actual character.
    * It handles some Java escaped strings and throws exception if given string is longer than one
    * character.
-   *
    */
   @throws[IllegalArgumentException]
-  private[csv] def toChar(str: String): Char = {
+  def toChar(str: String): Char = {
     if (str.charAt(0) == '\\') {
       str.charAt(1)
       match {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala
index 5959f7cc50..dc449fea95 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala
@@ -139,7 +139,7 @@ private[csv] class CSVRelation(
 
     val parsedRdd = tokenRdd(header, paths)
     if (params.inferSchemaFlag) {
-      CSVInferSchema(parsedRdd, header, params.nullValue)
+      CSVInferSchema.infer(parsedRdd, header, params.nullValue)
     } else {
       // By default fields are assumed to be StringType
       val schemaFields = header.map { fieldName =>