---
layout: global
title: Reference
displayTitle: Reference
---

* Table of contents
{:toc}

## Data Types

Spark SQL and DataFrames support the following data types:

* Numeric types
  - `ByteType`: Represents 1-byte signed integer numbers.
  The range of numbers is from `-128` to `127`.
  - `ShortType`: Represents 2-byte signed integer numbers.
  The range of numbers is from `-32768` to `32767`.
  - `IntegerType`: Represents 4-byte signed integer numbers.
  The range of numbers is from `-2147483648` to `2147483647`.
  - `LongType`: Represents 8-byte signed integer numbers.
  The range of numbers is from `-9223372036854775808` to `9223372036854775807`.
  - `FloatType`: Represents 4-byte single-precision floating point numbers.
  - `DoubleType`: Represents 8-byte double-precision floating point numbers.
  - `DecimalType`: Represents arbitrary-precision signed decimal numbers. Backed internally by `java.math.BigDecimal`. A `BigDecimal` consists of an arbitrary precision integer unscaled value and a 32-bit integer scale.
* String type
  - `StringType`: Represents character string values.
* Binary type
  - `BinaryType`: Represents byte sequence values.
* Boolean type
  - `BooleanType`: Represents boolean values.
* Datetime type
  - `TimestampType`: Represents values comprising values of fields year, month, day,
  hour, minute, and second.
  - `DateType`: Represents values comprising values of fields year, month, day.
* Complex types
  - `ArrayType(elementType, containsNull)`: Represents values comprising a sequence of
  elements with the type of `elementType`. `containsNull` is used to indicate if
  elements in a `ArrayType` value can have `null` values.
  - `MapType(keyType, valueType, valueContainsNull)`:
  Represents values comprising a set of key-value pairs. The data type of keys are
  described by `keyType` and the data type of values are described by `valueType`.
  For a `MapType` value, keys are not allowed to have `null` values. `valueContainsNull`
  is used to indicate if values of a `MapType` value can have `null` values.
  - `StructType(fields)`: Represents values with the structure described by
  a sequence of `StructField`s (`fields`).
    * `StructField(name, dataType, nullable)`: Represents a field in a `StructType`.
    The name of a field is indicated by `name`. The data type of a field is indicated
    by `dataType`. `nullable` is used to indicate if values of this fields can have
    `null` values.

<div class="codetabs">
<div data-lang="scala"  markdown="1">

All data types of Spark SQL are located in the package `org.apache.spark.sql.types`.
You can access them by doing

{% include_example data_types scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

<table class="table">
<tr>
  <th style="width:20%">Data type</th>
  <th style="width:40%">Value type in Scala</th>
  <th>API to access or create a data type</th></tr>
<tr>
  <td> <b>ByteType</b> </td>
  <td> Byte </td>
  <td>
  ByteType
  </td>
</tr>
<tr>
  <td> <b>ShortType</b> </td>
  <td> Short </td>
  <td>
  ShortType
  </td>
</tr>
<tr>
  <td> <b>IntegerType</b> </td>
  <td> Int </td>
  <td>
  IntegerType
  </td>
</tr>
<tr>
  <td> <b>LongType</b> </td>
  <td> Long </td>
  <td>
  LongType
  </td>
</tr>
<tr>
  <td> <b>FloatType</b> </td>
  <td> Float </td>
  <td>
  FloatType
  </td>
</tr>
<tr>
  <td> <b>DoubleType</b> </td>
  <td> Double </td>
  <td>
  DoubleType
  </td>
</tr>
<tr>
  <td> <b>DecimalType</b> </td>
  <td> java.math.BigDecimal </td>
  <td>
  DecimalType
  </td>
</tr>
<tr>
  <td> <b>StringType</b> </td>
  <td> String </td>
  <td>
  StringType
  </td>
</tr>
<tr>
  <td> <b>BinaryType</b> </td>
  <td> Array[Byte] </td>
  <td>
  BinaryType
  </td>
</tr>
<tr>
  <td> <b>BooleanType</b> </td>
  <td> Boolean </td>
  <td>
  BooleanType
  </td>
</tr>
<tr>
  <td> <b>TimestampType</b> </td>
  <td> java.sql.Timestamp </td>
  <td>
  TimestampType
  </td>
</tr>
<tr>
  <td> <b>DateType</b> </td>
  <td> java.sql.Date </td>
  <td>
  DateType
  </td>
</tr>
<tr>
  <td> <b>ArrayType</b> </td>
  <td> scala.collection.Seq </td>
  <td>
  ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
  <b>Note:</b> The default value of <i>containsNull</i> is <i>true</i>.
  </td>
</tr>
<tr>
  <td> <b>MapType</b> </td>
  <td> scala.collection.Map </td>
  <td>
  MapType(<i>keyType</i>, <i>valueType</i>, [<i>valueContainsNull</i>])<br />
  <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>true</i>.
  </td>
</tr>
<tr>
  <td> <b>StructType</b> </td>
  <td> org.apache.spark.sql.Row </td>
  <td>
  StructType(<i>fields</i>)<br />
  <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
  name are not allowed.
  </td>
</tr>
<tr>
  <td> <b>StructField</b> </td>
  <td> The value type in Scala of the data type of this field
  (For example, Int for a StructField with the data type IntegerType) </td>
  <td>
  StructField(<i>name</i>, <i>dataType</i>, [<i>nullable</i>])<br />
  <b>Note:</b> The default value of <i>nullable</i> is <i>true</i>.
  </td>
</tr>
</table>

</div>

<div data-lang="java" markdown="1">

All data types of Spark SQL are located in the package of
`org.apache.spark.sql.types`. To access or create a data type,
please use factory methods provided in
`org.apache.spark.sql.types.DataTypes`.

<table class="table">
<tr>
  <th style="width:20%">Data type</th>
  <th style="width:40%">Value type in Java</th>
  <th>API to access or create a data type</th></tr>
<tr>
  <td> <b>ByteType</b> </td>
  <td> byte or Byte </td>
  <td>
  DataTypes.ByteType
  </td>
</tr>
<tr>
  <td> <b>ShortType</b> </td>
  <td> short or Short </td>
  <td>
  DataTypes.ShortType
  </td>
</tr>
<tr>
  <td> <b>IntegerType</b> </td>
  <td> int or Integer </td>
  <td>
  DataTypes.IntegerType
  </td>
</tr>
<tr>
  <td> <b>LongType</b> </td>
  <td> long or Long </td>
  <td>
  DataTypes.LongType
  </td>
</tr>
<tr>
  <td> <b>FloatType</b> </td>
  <td> float or Float </td>
  <td>
  DataTypes.FloatType
  </td>
</tr>
<tr>
  <td> <b>DoubleType</b> </td>
  <td> double or Double </td>
  <td>
  DataTypes.DoubleType
  </td>
</tr>
<tr>
  <td> <b>DecimalType</b> </td>
  <td> java.math.BigDecimal </td>
  <td>
  DataTypes.createDecimalType()<br />
  DataTypes.createDecimalType(<i>precision</i>, <i>scale</i>).
  </td>
</tr>
<tr>
  <td> <b>StringType</b> </td>
  <td> String </td>
  <td>
  DataTypes.StringType
  </td>
</tr>
<tr>
  <td> <b>BinaryType</b> </td>
  <td> byte[] </td>
  <td>
  DataTypes.BinaryType
  </td>
</tr>
<tr>
  <td> <b>BooleanType</b> </td>
  <td> boolean or Boolean </td>
  <td>
  DataTypes.BooleanType
  </td>
</tr>
<tr>
  <td> <b>TimestampType</b> </td>
  <td> java.sql.Timestamp </td>
  <td>
  DataTypes.TimestampType
  </td>
</tr>
<tr>
  <td> <b>DateType</b> </td>
  <td> java.sql.Date </td>
  <td>
  DataTypes.DateType
  </td>
</tr>
<tr>
  <td> <b>ArrayType</b> </td>
  <td> java.util.List </td>
  <td>
  DataTypes.createArrayType(<i>elementType</i>)<br />
  <b>Note:</b> The value of <i>containsNull</i> will be <i>true</i><br />
  DataTypes.createArrayType(<i>elementType</i>, <i>containsNull</i>).
  </td>
</tr>
<tr>
  <td> <b>MapType</b> </td>
  <td> java.util.Map </td>
  <td>
  DataTypes.createMapType(<i>keyType</i>, <i>valueType</i>)<br />
  <b>Note:</b> The value of <i>valueContainsNull</i> will be <i>true</i>.<br />
  DataTypes.createMapType(<i>keyType</i>, <i>valueType</i>, <i>valueContainsNull</i>)<br />
  </td>
</tr>
<tr>
  <td> <b>StructType</b> </td>
  <td> org.apache.spark.sql.Row </td>
  <td>
  DataTypes.createStructType(<i>fields</i>)<br />
  <b>Note:</b> <i>fields</i> is a List or an array of StructFields.
  Also, two fields with the same name are not allowed.
  </td>
</tr>
<tr>
  <td> <b>StructField</b> </td>
  <td> The value type in Java of the data type of this field
  (For example, int for a StructField with the data type IntegerType) </td>
  <td>
  DataTypes.createStructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
  </td>
</tr>
</table>

</div>

<div data-lang="python"  markdown="1">

All data types of Spark SQL are located in the package of `pyspark.sql.types`.
You can access them by doing
{% highlight python %}
from pyspark.sql.types import *
{% endhighlight %}

<table class="table">
<tr>
  <th style="width:20%">Data type</th>
  <th style="width:40%">Value type in Python</th>
  <th>API to access or create a data type</th></tr>
<tr>
  <td> <b>ByteType</b> </td>
  <td>
  int or long <br />
  <b>Note:</b> Numbers will be converted to 1-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -128 to 127.
  </td>
  <td>
  ByteType()
  </td>
</tr>
<tr>
  <td> <b>ShortType</b> </td>
  <td>
  int or long <br />
  <b>Note:</b> Numbers will be converted to 2-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -32768 to 32767.
  </td>
  <td>
  ShortType()
  </td>
</tr>
<tr>
  <td> <b>IntegerType</b> </td>
  <td> int or long </td>
  <td>
  IntegerType()
  </td>
</tr>
<tr>
  <td> <b>LongType</b> </td>
  <td>
  long <br />
  <b>Note:</b> Numbers will be converted to 8-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of
  -9223372036854775808 to 9223372036854775807.
  Otherwise, please convert data to decimal.Decimal and use DecimalType.
  </td>
  <td>
  LongType()
  </td>
</tr>
<tr>
  <td> <b>FloatType</b> </td>
  <td>
  float <br />
  <b>Note:</b> Numbers will be converted to 4-byte single-precision floating
  point numbers at runtime.
  </td>
  <td>
  FloatType()
  </td>
</tr>
<tr>
  <td> <b>DoubleType</b> </td>
  <td> float </td>
  <td>
  DoubleType()
  </td>
</tr>
<tr>
  <td> <b>DecimalType</b> </td>
  <td> decimal.Decimal </td>
  <td>
  DecimalType()
  </td>
</tr>
<tr>
  <td> <b>StringType</b> </td>
  <td> string </td>
  <td>
  StringType()
  </td>
</tr>
<tr>
  <td> <b>BinaryType</b> </td>
  <td> bytearray </td>
  <td>
  BinaryType()
  </td>
</tr>
<tr>
  <td> <b>BooleanType</b> </td>
  <td> bool </td>
  <td>
  BooleanType()
  </td>
</tr>
<tr>
  <td> <b>TimestampType</b> </td>
  <td> datetime.datetime </td>
  <td>
  TimestampType()
  </td>
</tr>
<tr>
  <td> <b>DateType</b> </td>
  <td> datetime.date </td>
  <td>
  DateType()
  </td>
</tr>
<tr>
  <td> <b>ArrayType</b> </td>
  <td> list, tuple, or array </td>
  <td>
  ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
  <b>Note:</b> The default value of <i>containsNull</i> is <i>True</i>.
  </td>
</tr>
<tr>
  <td> <b>MapType</b> </td>
  <td> dict </td>
  <td>
  MapType(<i>keyType</i>, <i>valueType</i>, [<i>valueContainsNull</i>])<br />
  <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>True</i>.
  </td>
</tr>
<tr>
  <td> <b>StructType</b> </td>
  <td> list or tuple </td>
  <td>
  StructType(<i>fields</i>)<br />
  <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
  name are not allowed.
  </td>
</tr>
<tr>
  <td> <b>StructField</b> </td>
  <td> The value type in Python of the data type of this field
  (For example, Int for a StructField with the data type IntegerType) </td>
  <td>
  StructField(<i>name</i>, <i>dataType</i>, [<i>nullable</i>])<br />
  <b>Note:</b> The default value of <i>nullable</i> is <i>True</i>.
  </td>
</tr>
</table>

</div>

<div data-lang="r"  markdown="1">

<table class="table">
<tr>
  <th style="width:20%">Data type</th>
  <th style="width:40%">Value type in R</th>
  <th>API to access or create a data type</th></tr>
<tr>
  <td> <b>ByteType</b> </td>
  <td>
  integer <br />
  <b>Note:</b> Numbers will be converted to 1-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -128 to 127.
  </td>
  <td>
  "byte"
  </td>
</tr>
<tr>
  <td> <b>ShortType</b> </td>
  <td>
  integer <br />
  <b>Note:</b> Numbers will be converted to 2-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -32768 to 32767.
  </td>
  <td>
  "short"
  </td>
</tr>
<tr>
  <td> <b>IntegerType</b> </td>
  <td> integer </td>
  <td>
  "integer"
  </td>
</tr>
<tr>
  <td> <b>LongType</b> </td>
  <td>
  integer <br />
  <b>Note:</b> Numbers will be converted to 8-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of
  -9223372036854775808 to 9223372036854775807.
  Otherwise, please convert data to decimal.Decimal and use DecimalType.
  </td>
  <td>
  "long"
  </td>
</tr>
<tr>
  <td> <b>FloatType</b> </td>
  <td>
  numeric <br />
  <b>Note:</b> Numbers will be converted to 4-byte single-precision floating
  point numbers at runtime.
  </td>
  <td>
  "float"
  </td>
</tr>
<tr>
  <td> <b>DoubleType</b> </td>
  <td> numeric </td>
  <td>
  "double"
  </td>
</tr>
<tr>
  <td> <b>DecimalType</b> </td>
  <td> Not supported </td>
  <td>
   Not supported
  </td>
</tr>
<tr>
  <td> <b>StringType</b> </td>
  <td> character </td>
  <td>
  "string"
  </td>
</tr>
<tr>
  <td> <b>BinaryType</b> </td>
  <td> raw </td>
  <td>
  "binary"
  </td>
</tr>
<tr>
  <td> <b>BooleanType</b> </td>
  <td> logical </td>
  <td>
  "bool"
  </td>
</tr>
<tr>
  <td> <b>TimestampType</b> </td>
  <td> POSIXct </td>
  <td>
  "timestamp"
  </td>
</tr>
<tr>
  <td> <b>DateType</b> </td>
  <td> Date </td>
  <td>
  "date"
  </td>
</tr>
<tr>
  <td> <b>ArrayType</b> </td>
  <td> vector or list </td>
  <td>
  list(type="array", elementType=<i>elementType</i>, containsNull=[<i>containsNull</i>])<br />
  <b>Note:</b> The default value of <i>containsNull</i> is <i>TRUE</i>.
  </td>
</tr>
<tr>
  <td> <b>MapType</b> </td>
  <td> environment </td>
  <td>
  list(type="map", keyType=<i>keyType</i>, valueType=<i>valueType</i>, valueContainsNull=[<i>valueContainsNull</i>])<br />
  <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>TRUE</i>.
  </td>
</tr>
<tr>
  <td> <b>StructType</b> </td>
  <td> named list</td>
  <td>
  list(type="struct", fields=<i>fields</i>)<br />
  <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
  name are not allowed.
  </td>
</tr>
<tr>
  <td> <b>StructField</b> </td>
  <td> The value type in R of the data type of this field
  (For example, integer for a StructField with the data type IntegerType) </td>
  <td>
  list(name=<i>name</i>, type=<i>dataType</i>, nullable=[<i>nullable</i>])<br />
  <b>Note:</b> The default value of <i>nullable</i> is <i>TRUE</i>.
  </td>
</tr>
</table>

</div>

</div>

## NaN Semantics

There is specially handling for not-a-number (NaN) when dealing with `float` or `double` types that
does not exactly match standard floating point semantics.
Specifically:

 - NaN = NaN returns true.
 - In aggregations, all NaN values are grouped together.
 - NaN is treated as a normal value in join keys.
 - NaN values go last when in ascending order, larger than any other numeric value.
 
## Arithmetic operations
 
Operations performed on numeric types (with the exception of `decimal`) are not checked for overflow.
This means that in case an operation causes an overflow, the result is the same that the same operation
returns in a Java/Scala program (eg. if the sum of 2 integers is higher than the maximum value representable,
the result is a negative number).