[SPARK-26384][SQL] Propagate SQL configs for CSV schema inferring

## What changes were proposed in this pull request?

Currently, SQL configs are not propagated to executors while schema inferring in CSV datasource. For example, changing of `spark.sql.legacy.timeParser.enabled` does not impact on inferring timestamp types. In the PR, I propose to fix the issue by wrapping schema inferring action using `SQLExecution.withSQLConfPropagated`.

## How was this patch tested?

Added logging to `TimestampFormatter`:
```patch
-object TimestampFormatter {
+object TimestampFormatter extends Logging {
   def apply(format: String, timeZone: TimeZone, locale: Locale): TimestampFormatter = {
     if (SQLConf.get.legacyTimeParserEnabled) {
+      logError("LegacyFallbackTimestampFormatter is being used")
       new LegacyFallbackTimestampFormatter(format, timeZone, locale)
     } else {
+      logError("Iso8601TimestampFormatter is being used")
       new Iso8601TimestampFormatter(format, timeZone, locale)
     }
   }
```
and run the command in `spark-shell`:
```shell
$ ./bin/spark-shell --conf spark.sql.legacy.timeParser.enabled=true
```
```scala
scala> Seq("2010|10|10").toDF.repartition(1).write.mode("overwrite").text("/tmp/foo")
scala> spark.read.option("inferSchema", "true").option("header", "false").option("timestampFormat", "yyyy|MM|dd").csv("/tmp/foo").printSchema()
18/12/18 10:47:27 ERROR TimestampFormatter: LegacyFallbackTimestampFormatter is being used
root
 |-- _c0: timestamp (nullable = true)
```

Closes apache#23345 from MaxGekk/csv-schema-infer-propagate-configs.

Authored-by: Maxim Gekk <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

Author: MaxGekk

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

@@ -35,6 +35,7 @@ import org.apache.spark.rdd.{BinaryFileRDD, RDD}
 import org.apache.spark.sql.{Dataset, Encoders, SparkSession}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.csv.{CSVHeaderChecker, CSVInferSchema, CSVOptions, UnivocityParser}
+import org.apache.spark.sql.execution.SQLExecution
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.execution.datasources.text.TextFileFormat
 import org.apache.spark.sql.types.StructType
@@ -135,7 +136,9 @@ object TextInputCSVDataSource extends CSVDataSource {
           val parser = new CsvParser(parsedOptions.asParserSettings)
           linesWithoutHeader.map(parser.parseLine)
         }
-        new CSVInferSchema(parsedOptions).infer(tokenRDD, header)
+        SQLExecution.withSQLConfPropagated(csv.sparkSession) {
+          new CSVInferSchema(parsedOptions).infer(tokenRDD, header)
+        }
       case _ =>
         // If the first line could not be read, just return the empty schema.
         StructType(Nil)
@@ -208,7 +211,9 @@ object MultiLineCSVDataSource extends CSVDataSource {
             encoding = parsedOptions.charset)
         }
         val sampled = CSVUtils.sample(tokenRDD, parsedOptions)
-        new CSVInferSchema(parsedOptions).infer(sampled, header)
+        SQLExecution.withSQLConfPropagated(sparkSession) {
+          new CSVInferSchema(parsedOptions).infer(sampled, header)
+        }
       case None =>
         // If the first row could not be read, just return the empty schema.
         StructType(Nil)