[SPARK-13667][SQL] Support for specifying custom date format for date and timestamp types at CSV datasource.

## What changes were proposed in this pull request?

This PR adds the support to specify custom date format for `DateType` and `TimestampType`.

For `TimestampType`, this uses the given format to infer schema and also to convert the values
For `DateType`, this uses the given format to convert the values.
If the `dateFormat` is not given, then it works with `DateTimeUtils.stringToTime()` for backwords compatibility.
When it's given, then it uses `SimpleDateFormat` for parsing data.

In addition, `IntegerType`, `DoubleType` and `LongType` have a higher priority than `TimestampType` in type inference. This means even if the given format is `yyyy` or `yyyy.MM`, it will be inferred as `IntegerType` or `DoubleType`. Since it is type inference, I think it is okay to give such precedences.

In addition, I renamed `csv.CSVInferSchema` to `csv.InferSchema` as JSON datasource has `json.InferSchema`. Although they have the same names, I did this because I thought the parent package name can still differentiate each.  Accordingly, the suite name was also changed from `CSVInferSchemaSuite` to `InferSchemaSuite`.

## How was this patch tested?

unit tests are used and `./dev/run_tests` for coding style tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11550 from HyukjinKwon/SPARK-13667.

8 files changed, 173 insertions, 66 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 ea843a1013..a26a8084b6 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
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution.datasources.csv
 
 import java.math.BigDecimal
-import java.text.NumberFormat
+import java.text.{NumberFormat, SimpleDateFormat}
 import java.util.Locale
 
 import scala.util.control.Exception._
@@ -41,11 +41,10 @@ private[csv] object CSVInferSchema {
   def infer(
       tokenRdd: RDD[Array[String]],
       header: Array[String],
-      nullValue: String = ""): StructType = {
-
+      options: CSVOptions): StructType = {
     val startType: Array[DataType] = Array.fill[DataType](header.length)(NullType)
     val rootTypes: Array[DataType] =
-      tokenRdd.aggregate(startType)(inferRowType(nullValue), mergeRowTypes)
+      tokenRdd.aggregate(startType)(inferRowType(options), mergeRowTypes)
 
     val structFields = header.zip(rootTypes).map { case (thisHeader, rootType) =>
       val dType = rootType match {
@@ -58,11 +57,11 @@ private[csv] object CSVInferSchema {
     StructType(structFields)
   }
 
-  private def inferRowType(nullValue: String)
+  private def inferRowType(options: CSVOptions)
       (rowSoFar: Array[DataType], next: Array[String]): Array[DataType] = {
     var i = 0
     while (i < math.min(rowSoFar.length, next.length)) {  // May have columns on right missing.
-      rowSoFar(i) = inferField(rowSoFar(i), next(i), nullValue)
+      rowSoFar(i) = inferField(rowSoFar(i), next(i), options)
       i+=1
     }
     rowSoFar
@@ -78,17 +77,17 @@ private[csv] 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.
    */
-  def inferField(typeSoFar: DataType, field: String, nullValue: String = ""): DataType = {
-    if (field == null || field.isEmpty || field == nullValue) {
+  def inferField(typeSoFar: DataType, field: String, options: CSVOptions): DataType = {
+    if (field == null || field.isEmpty || field == options.nullValue) {
       typeSoFar
     } else {
       typeSoFar match {
-        case NullType => tryParseInteger(field)
-        case IntegerType => tryParseInteger(field)
-        case LongType => tryParseLong(field)
-        case DoubleType => tryParseDouble(field)
-        case TimestampType => tryParseTimestamp(field)
-        case BooleanType => tryParseBoolean(field)
+        case NullType => tryParseInteger(field, options)
+        case IntegerType => tryParseInteger(field, options)
+        case LongType => tryParseLong(field, options)
+        case DoubleType => tryParseDouble(field, options)
+        case TimestampType => tryParseTimestamp(field, options)
+        case BooleanType => tryParseBoolean(field, options)
         case StringType => StringType
         case other: DataType =>
           throw new UnsupportedOperationException(s"Unexpected data type $other")
@@ -96,35 +95,49 @@ private[csv] object CSVInferSchema {
     }
   }
 
-  private def tryParseInteger(field: String): DataType = if ((allCatch opt field.toInt).isDefined) {
-    IntegerType
-  } else {
-    tryParseLong(field)
+  private def tryParseInteger(field: String, options: CSVOptions): DataType = {
+    if ((allCatch opt field.toInt).isDefined) {
+      IntegerType
+    } else {
+      tryParseLong(field, options)
+    }
   }
 
-  private def tryParseLong(field: String): DataType = if ((allCatch opt field.toLong).isDefined) {
-    LongType
-  } else {
-    tryParseDouble(field)
+  private def tryParseLong(field: String, options: CSVOptions): DataType = {
+    if ((allCatch opt field.toLong).isDefined) {
+      LongType
+    } else {
+      tryParseDouble(field, options)
+    }
   }
 
-  private def tryParseDouble(field: String): DataType = {
+  private def tryParseDouble(field: String, options: CSVOptions): DataType = {
     if ((allCatch opt field.toDouble).isDefined) {
       DoubleType
     } else {
-      tryParseTimestamp(field)
+      tryParseTimestamp(field, options)
     }
   }
 
-  def tryParseTimestamp(field: String): DataType = {
-    if ((allCatch opt DateTimeUtils.stringToTime(field)).isDefined) {
-      TimestampType
+  private def tryParseTimestamp(field: String, options: CSVOptions): DataType = {
+    if (options.dateFormat != null) {
+      // This case infers a custom `dataFormat` is set.
+      if ((allCatch opt options.dateFormat.parse(field)).isDefined) {
+        TimestampType
+      } else {
+        tryParseBoolean(field, options)
+      }
     } else {
-      tryParseBoolean(field)
+      // We keep this for backwords competibility.
+      if ((allCatch opt DateTimeUtils.stringToTime(field)).isDefined) {
+        TimestampType
+      } else {
+        tryParseBoolean(field, options)
+      }
     }
   }
 
-  def tryParseBoolean(field: String): DataType = {
+  private def tryParseBoolean(field: String, options: CSVOptions): DataType = {
     if ((allCatch opt field.toBoolean).isDefined) {
       BooleanType
     } else {
@@ -177,7 +190,8 @@ private[csv] object CSVTypeCast {
       datum: String,
       castType: DataType,
       nullable: Boolean = true,
-      nullValue: String = ""): Any = {
+      nullValue: String = "",
+      dateFormat: SimpleDateFormat = null): Any = {
 
     if (datum == nullValue && nullable && (!castType.isInstanceOf[StringType])) {
       null
@@ -195,12 +209,16 @@ private[csv] object CSVTypeCast {
         case dt: DecimalType =>
           val value = new BigDecimal(datum.replaceAll(",", ""))
           Decimal(value, dt.precision, dt.scale)
-        // TODO(hossein): would be good to support other common timestamp formats
+        case _: TimestampType if dateFormat != null =>
+          // This one will lose microseconds parts.
+          // See https://issues.apache.org/jira/browse/SPARK-10681.
+          dateFormat.parse(datum).getTime * 1000L
         case _: TimestampType =>
           // This one will lose microseconds parts.
           // See https://issues.apache.org/jira/browse/SPARK-10681.
           DateTimeUtils.stringToTime(datum).getTime  * 1000L
-        // TODO(hossein): would be good to support other common date formats
+        case _: DateType if dateFormat != null =>
+          DateTimeUtils.millisToDays(dateFormat.parse(datum).getTime)
         case _: DateType =>
           DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(datum).getTime)
         case _: StringType => UTF8String.fromString(datum)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala
index 80a0ad7856..b87d19f7cf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.execution.datasources.csv
 
 import java.nio.charset.StandardCharsets
+import java.text.SimpleDateFormat
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.sql.execution.datasources.{CompressionCodecs, ParseModes}
@@ -94,6 +95,12 @@ private[sql] class CSVOptions(@transient private val parameters: Map[String, Str
     name.map(CompressionCodecs.getCodecClassName)
   }
 
+  // Share date format object as it is expensive to parse date pattern.
+  val dateFormat: SimpleDateFormat = {
+    val dateFormat = parameters.get("dateFormat")
+    dateFormat.map(new SimpleDateFormat(_)).orNull
+  }
+
   val maxColumns = getInt("maxColumns", 20480)
 
   val maxCharsPerColumn = getInt("maxCharsPerColumn", 1000000)
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 ed40cd0c81..9a723630de 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
@@ -99,7 +99,8 @@ object CSVRelation extends Logging {
               indexSafeTokens(index),
               field.dataType,
               field.nullable,
-              params.nullValue)
+              params.nullValue,
+              params.dateFormat)
             if (subIndex < requiredSize) {
               row(subIndex) = value
             }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/DefaultSource.scala
index 8ca105d923..75143e609a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/DefaultSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/DefaultSource.scala
@@ -68,7 +68,7 @@ class DefaultSource extends FileFormat with DataSourceRegister {
 
     val parsedRdd = tokenRdd(sparkSession, csvOptions, header, paths)
     val schema = if (csvOptions.inferSchemaFlag) {
-      CSVInferSchema.infer(parsedRdd, header, csvOptions.nullValue)
+      CSVInferSchema.infer(parsedRdd, header, csvOptions)
     } else {
       // By default fields are assumed to be StringType
       val schemaFields = header.map { fieldName =>
diff --git a/sql/core/src/test/resources/dates.csv b/sql/core/src/test/resources/dates.csv
new file mode 100644
index 0000000000..9ee99c31b3
--- /dev/null
+++ b/sql/core/src/test/resources/dates.csv
@@ -0,0 +1,4 @@
+date
+26/08/2015 18:00
+27/10/2014 18:30
+28/01/2016 20:00
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala
index 23d422635b..daf85be56f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala
@@ -17,45 +17,58 @@
 
 package org.apache.spark.sql.execution.datasources.csv
 
+import java.text.SimpleDateFormat
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.types._
 
 class CSVInferSchemaSuite extends SparkFunSuite {
 
   test("String fields types are inferred correctly from null types") {
-    assert(CSVInferSchema.inferField(NullType, "") == NullType)
-    assert(CSVInferSchema.inferField(NullType, null) == NullType)
-    assert(CSVInferSchema.inferField(NullType, "100000000000") == LongType)
-    assert(CSVInferSchema.inferField(NullType, "60") == IntegerType)
-    assert(CSVInferSchema.inferField(NullType, "3.5") == DoubleType)
-    assert(CSVInferSchema.inferField(NullType, "test") == StringType)
-    assert(CSVInferSchema.inferField(NullType, "2015-08-20 15:57:00") == TimestampType)
-    assert(CSVInferSchema.inferField(NullType, "True") == BooleanType)
-    assert(CSVInferSchema.inferField(NullType, "FAlSE") == BooleanType)
+    val options = new CSVOptions(Map.empty[String, String])
+    assert(CSVInferSchema.inferField(NullType, "", options) == NullType)
+    assert(CSVInferSchema.inferField(NullType, null, options) == NullType)
+    assert(CSVInferSchema.inferField(NullType, "100000000000", options) == LongType)
+    assert(CSVInferSchema.inferField(NullType, "60", options) == IntegerType)
+    assert(CSVInferSchema.inferField(NullType, "3.5", options) == DoubleType)
+    assert(CSVInferSchema.inferField(NullType, "test", options) == StringType)
+    assert(CSVInferSchema.inferField(NullType, "2015-08-20 15:57:00", options) == TimestampType)
+    assert(CSVInferSchema.inferField(NullType, "True", options) == BooleanType)
+    assert(CSVInferSchema.inferField(NullType, "FAlSE", options) == BooleanType)
   }
 
   test("String fields types are inferred correctly from other types") {
-    assert(CSVInferSchema.inferField(LongType, "1.0") == DoubleType)
-    assert(CSVInferSchema.inferField(LongType, "test") == StringType)
-    assert(CSVInferSchema.inferField(IntegerType, "1.0") == DoubleType)
-    assert(CSVInferSchema.inferField(DoubleType, null) == DoubleType)
-    assert(CSVInferSchema.inferField(DoubleType, "test") == StringType)
-    assert(CSVInferSchema.inferField(LongType, "2015-08-20 14:57:00") == TimestampType)
-    assert(CSVInferSchema.inferField(DoubleType, "2015-08-20 15:57:00") == TimestampType)
-    assert(CSVInferSchema.inferField(LongType, "True") == BooleanType)
-    assert(CSVInferSchema.inferField(IntegerType, "FALSE") == BooleanType)
-    assert(CSVInferSchema.inferField(TimestampType, "FALSE") == BooleanType)
+    val options = new CSVOptions(Map.empty[String, String])
+    assert(CSVInferSchema.inferField(LongType, "1.0", options) == DoubleType)
+    assert(CSVInferSchema.inferField(LongType, "test", options) == StringType)
+    assert(CSVInferSchema.inferField(IntegerType, "1.0", options) == DoubleType)
+    assert(CSVInferSchema.inferField(DoubleType, null, options) == DoubleType)
+    assert(CSVInferSchema.inferField(DoubleType, "test", options) == StringType)
+    assert(CSVInferSchema.inferField(LongType, "2015-08-20 14:57:00", options) == TimestampType)
+    assert(CSVInferSchema.inferField(DoubleType, "2015-08-20 15:57:00", options) == TimestampType)
+    assert(CSVInferSchema.inferField(LongType, "True", options) == BooleanType)
+    assert(CSVInferSchema.inferField(IntegerType, "FALSE", options) == BooleanType)
+    assert(CSVInferSchema.inferField(TimestampType, "FALSE", options) == BooleanType)
+  }
+
+  test("Timestamp field types are inferred correctly via custom data format") {
+    var options = new CSVOptions(Map("dateFormat" -> "yyyy-mm"))
+    assert(CSVInferSchema.inferField(TimestampType, "2015-08", options) == TimestampType)
+    options = new CSVOptions(Map("dateFormat" -> "yyyy"))
+    assert(CSVInferSchema.inferField(TimestampType, "2015", options) == TimestampType)
   }
 
   test("Timestamp field types are inferred correctly from other types") {
-    assert(CSVInferSchema.inferField(IntegerType, "2015-08-20 14") == StringType)
-    assert(CSVInferSchema.inferField(DoubleType, "2015-08-20 14:10") == StringType)
-    assert(CSVInferSchema.inferField(LongType, "2015-08 14:49:00") == StringType)
+    val options = new CSVOptions(Map.empty[String, String])
+    assert(CSVInferSchema.inferField(IntegerType, "2015-08-20 14", options) == StringType)
+    assert(CSVInferSchema.inferField(DoubleType, "2015-08-20 14:10", options) == StringType)
+    assert(CSVInferSchema.inferField(LongType, "2015-08 14:49:00", options) == StringType)
   }
 
   test("Boolean fields types are inferred correctly from other types") {
-    assert(CSVInferSchema.inferField(LongType, "Fale") == StringType)
-    assert(CSVInferSchema.inferField(DoubleType, "TRUEe") == StringType)
+    val options = new CSVOptions(Map.empty[String, String])
+    assert(CSVInferSchema.inferField(LongType, "Fale", options) == StringType)
+    assert(CSVInferSchema.inferField(DoubleType, "TRUEe", options) == StringType)
   }
 
   test("Type arrays are merged to highest common type") {
@@ -71,13 +84,16 @@ class CSVInferSchemaSuite extends SparkFunSuite {
   }
 
   test("Null fields are handled properly when a nullValue is specified") {
-    assert(CSVInferSchema.inferField(NullType, "null", "null") == NullType)
-    assert(CSVInferSchema.inferField(StringType, "null", "null") == StringType)
-    assert(CSVInferSchema.inferField(LongType, "null", "null") == LongType)
-    assert(CSVInferSchema.inferField(IntegerType, "\\N", "\\N") == IntegerType)
-    assert(CSVInferSchema.inferField(DoubleType, "\\N", "\\N") == DoubleType)
-    assert(CSVInferSchema.inferField(TimestampType, "\\N", "\\N") == TimestampType)
-    assert(CSVInferSchema.inferField(BooleanType, "\\N", "\\N") == BooleanType)
+    var options = new CSVOptions(Map("nullValue" -> "null"))
+    assert(CSVInferSchema.inferField(NullType, "null", options) == NullType)
+    assert(CSVInferSchema.inferField(StringType, "null", options) == StringType)
+    assert(CSVInferSchema.inferField(LongType, "null", options) == LongType)
+
+    options = new CSVOptions(Map("nullValue" -> "\\N"))
+    assert(CSVInferSchema.inferField(IntegerType, "\\N", options) == IntegerType)
+    assert(CSVInferSchema.inferField(DoubleType, "\\N", options) == DoubleType)
+    assert(CSVInferSchema.inferField(TimestampType, "\\N", options) == TimestampType)
+    assert(CSVInferSchema.inferField(BooleanType, "\\N", options) == BooleanType)
   }
 
   test("Merging Nulltypes should yield Nulltype.") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
index ceda920ddc..8847c7632f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
@@ -19,7 +19,8 @@ package org.apache.spark.sql.execution.datasources.csv
 
 import java.io.File
 import java.nio.charset.UnsupportedCharsetException
-import java.sql.Timestamp
+import java.sql.{Date, Timestamp}
+import java.text.SimpleDateFormat
 
 import scala.collection.JavaConverters._
 
@@ -45,6 +46,7 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
   private val disableCommentsFile = "disable_comments.csv"
   private val boolFile = "bool.csv"
   private val simpleSparseFile = "simple_sparse.csv"
+  private val datesFile = "dates.csv"
   private val unescapedQuotesFile = "unescaped-quotes.csv"
 
   private def testFile(fileName: String): String = {
@@ -367,6 +369,54 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
     assert(results.toSeq.map(_.toSeq) === expected)
   }
 
+  test("inferring timestamp types via custom date format") {
+    val options = Map(
+      "header" -> "true",
+      "inferSchema" -> "true",
+      "dateFormat" -> "dd/MM/yyyy hh:mm")
+    val results = sqlContext.read
+      .format("csv")
+      .options(options)
+      .load(testFile(datesFile))
+      .select("date")
+      .collect()
+
+    val dateFormat = new SimpleDateFormat("dd/MM/yyyy hh:mm")
+    val expected =
+      Seq(Seq(new Timestamp(dateFormat.parse("26/08/2015 18:00").getTime)),
+        Seq(new Timestamp(dateFormat.parse("27/10/2014 18:30").getTime)),
+        Seq(new Timestamp(dateFormat.parse("28/01/2016 20:00").getTime)))
+    assert(results.toSeq.map(_.toSeq) === expected)
+  }
+
+  test("load date types via custom date format") {
+    val customSchema = new StructType(Array(StructField("date", DateType, true)))
+    val options = Map(
+      "header" -> "true",
+      "inferSchema" -> "false",
+      "dateFormat" -> "dd/MM/yyyy hh:mm")
+    val results = sqlContext.read
+      .format("csv")
+      .options(options)
+      .schema(customSchema)
+      .load(testFile(datesFile))
+      .select("date")
+      .collect()
+
+    val dateFormat = new SimpleDateFormat("dd/MM/yyyy hh:mm")
+    val expected = Seq(
+      new Date(dateFormat.parse("26/08/2015 18:00").getTime),
+      new Date(dateFormat.parse("27/10/2014 18:30").getTime),
+      new Date(dateFormat.parse("28/01/2016 20:00").getTime))
+    val dates = results.toSeq.map(_.toSeq.head)
+    expected.zip(dates).foreach {
+      case (expectedDate, date) =>
+        // As it truncates the hours, minutes and etc., we only check
+        // if the dates (days, months and years) are the same via `toString()`.
+        assert(expectedDate.toString === date.toString)
+    }
+  }
+
   test("setting comment to null disables comment support") {
     val results = sqlContext.read
       .format("csv")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVTypeCastSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVTypeCastSuite.scala
index 5702a1b4ea..8b59bc148f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVTypeCastSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVTypeCastSuite.scala
@@ -18,6 +18,8 @@
 package org.apache.spark.sql.execution.datasources.csv
 
 import java.math.BigDecimal
+import java.sql.{Date, Timestamp}
+import java.text.SimpleDateFormat
 import java.util.Locale
 
 import org.apache.spark.SparkFunSuite
@@ -87,6 +89,15 @@ class CSVTypeCastSuite extends SparkFunSuite {
     assert(CSVTypeCast.castTo("1.00", FloatType) == 1.0)
     assert(CSVTypeCast.castTo("1.00", DoubleType) == 1.0)
     assert(CSVTypeCast.castTo("true", BooleanType) == true)
+
+    val dateFormat = new SimpleDateFormat("dd/MM/yyyy hh:mm")
+    val customTimestamp = "31/01/2015 00:00"
+    val expectedTime = dateFormat.parse("31/01/2015 00:00").getTime
+    assert(CSVTypeCast.castTo(customTimestamp, TimestampType, dateFormat = dateFormat)
+      == expectedTime * 1000L)
+    assert(CSVTypeCast.castTo(customTimestamp, DateType, dateFormat = dateFormat) ==
+      DateTimeUtils.millisToDays(expectedTime))
+
     val timestamp = "2015-01-01 00:00:00"
     assert(CSVTypeCast.castTo(timestamp, TimestampType) ==
       DateTimeUtils.stringToTime(timestamp).getTime  * 1000L)