layout: global title: Data Types displayTitle: Data Types license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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.VarcharType(length)
: A variant of StringType
which has a length limitation. Data writing will fail if the input string exceeds the length limitation. Note: this type can only be used in table schema, not functions/operators.CharType(length)
: A variant of VarcharType(length)
which is fixed length. Reading column of type CharType(n)
always returns string values of length n
. Char type column comparison will pad the short one to the longer length.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, with the session local time-zone. The timestamp value represents an absolute point in time.DateType
: Represents values comprising values of fields year, month and day, without a time-zone.Interval types
YearMonthIntervalType(startField, endField)
: Represents a year-month interval which is made up of a contiguous subset of the following fields:
[0..11]
,[0..178956970]
.Individual interval fields are non-negative, but an interval itself can have a sign, and be negative.
startField
is the leftmost field, and endField
is the rightmost field of the type. Valid values of startField
and endField
are 0(MONTH) and 1(YEAR). Supported year-month interval types are:
Year-Month Interval Type | SQL type | An instance of the type |
---|---|---|
YearMonthIntervalType(YEAR, YEAR) or YearMonthIntervalType(YEAR) | INTERVAL YEAR | INTERVAL '2021' YEAR |
YearMonthIntervalType(YEAR, MONTH) | INTERVAL YEAR TO MONTH | INTERVAL '2021-07' YEAR TO MONTH |
YearMonthIntervalType(MONTH, MONTH) or YearMonthIntervalType(MONTH) | INTERVAL MONTH | INTERVAL '10' MONTH |
DayTimeIntervalType(startField, endField)
: Represents a day-time interval which is made up of a contiguous subset of the following fields:
[0..59.999999]
,[0..59]
,[0..23]
,[0..106751991]
.Individual interval fields are non-negative, but an interval itself can have a sign, and be negative.
startField
is the leftmost field, and endField
is the rightmost field of the type. Valid values of startField
and endField
are 0 (DAY), 1 (HOUR), 2 (MINUTE), 3 (SECOND). Supported day-time interval types are:
Day-Time Interval Type | SQL type | An instance of the type |
---|---|---|
DayTimeIntervalType(DAY, DAY) or DayTimeIntervalType(DAY) | INTERVAL DAY | INTERVAL '100' DAY |
DayTimeIntervalType(DAY, HOUR) | INTERVAL DAY TO HOUR | INTERVAL '100 10' DAY TO HOUR |
DayTimeIntervalType(DAY, MINUTE) | INTERVAL DAY TO MINUTE | INTERVAL '100 10:30' DAY TO MINUTE |
DayTimeIntervalType(DAY, SECOND) | INTERVAL DAY TO SECOND | INTERVAL '100 10:30:40.999999' DAY TO SECOND |
DayTimeIntervalType(HOUR, HOUR) or DayTimeIntervalType(HOUR) | INTERVAL HOUR | INTERVAL '123' HOUR |
DayTimeIntervalType(HOUR, MINUTE) | INTERVAL HOUR TO MINUTE | INTERVAL '123:10' HOUR TO MINUTE |
DayTimeIntervalType(HOUR, SECOND) | INTERVAL HOUR TO SECOND | INTERVAL '123:10:59' HOUR TO SECOND |
DayTimeIntervalType(MINUTE, MINUTE) or DayTimeIntervalType(MINUTE) | INTERVAL MINUTE | INTERVAL '1000' MINUTE |
DayTimeIntervalType(MINUTE, SECOND) | INTERVAL MINUTE TO SECOND | INTERVAL '1000:01.001' MINUTE TO SECOND |
DayTimeIntervalType(SECOND, SECOND) or DayTimeIntervalType(SECOND) | INTERVAL SECOND | INTERVAL '1000.000001' SECOND |
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 is described by keyType
and the data type of values is 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 these fields can have null
values.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 %}
Data type | Value type in Scala | API to access or create a data type |
---|---|---|
ByteType | Byte | ByteType |
ShortType | Short | ShortType |
IntegerType | Int | IntegerType |
LongType | Long | LongType |
FloatType | Float | FloatType |
DoubleType | Double | DoubleType |
DecimalType | java.math.BigDecimal | DecimalType |
StringType | String | StringType |
BinaryType | Array[Byte] | BinaryType |
BooleanType | Boolean | BooleanType |
TimestampType | java.sql.Timestamp | TimestampType |
DateType | java.sql.Date | DateType |
YearMonthIntervalType | java.time.Period | YearMonthIntervalType |
DayTimeIntervalType | java.time.Duration | DayTimeIntervalType |
ArrayType | scala.collection.Seq | ArrayType(elementType, [containsNull]) Note: The default value of containsNull is true. |
MapType | scala.collection.Map | MapType(keyType, valueType, [valueContainsNull]) Note: The default value of valueContainsNull is true. |
StructType | org.apache.spark.sql.Row | StructType(fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed. |
StructField | The value type in Scala of the data type of this field(For example, Int for a StructField with the data type IntegerType) | StructField(name, dataType, [nullable]) Note: The default value of nullable is true. |
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
.
Data type | Value type in Java | API to access or create a data type |
---|---|---|
ByteType | byte or Byte | DataTypes.ByteType |
ShortType | short or Short | DataTypes.ShortType |
IntegerType | int or Integer | DataTypes.IntegerType |
LongType | long or Long | DataTypes.LongType |
FloatType | float or Float | DataTypes.FloatType |
DoubleType | double or Double | DataTypes.DoubleType |
DecimalType | java.math.BigDecimal | DataTypes.createDecimalType() DataTypes.createDecimalType(precision, scale). |
StringType | String | DataTypes.StringType |
BinaryType | byte[] | DataTypes.BinaryType |
BooleanType | boolean or Boolean | DataTypes.BooleanType |
TimestampType | java.sql.Timestamp | DataTypes.TimestampType |
DateType | java.sql.Date | DataTypes.DateType |
YearMonthIntervalType | java.time.Period | YearMonthIntervalType |
DayTimeIntervalType | java.time.Duration | DayTimeIntervalType |
ArrayType | java.util.List | DataTypes.createArrayType(elementType) Note: The value of containsNull will be true. DataTypes.createArrayType(elementType, containsNull). |
MapType | java.util.Map | DataTypes.createMapType(keyType, valueType) Note: The value of valueContainsNull will be true. DataTypes.createMapType(keyType, valueType, valueContainsNull) |
StructType | org.apache.spark.sql.Row | DataTypes.createStructType(fields) Note: fields is a List or an array of StructFields.Also, two fields with the same name are not allowed. |
StructField | The value type in Java of the data type of this field (For example, int for a StructField with the data type IntegerType) | DataTypes.createStructField(name, dataType, nullable) |
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 %}
Data type | Value type in Python | API to access or create a data type |
---|---|---|
ByteType | int or long Note: 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. | ByteType() |
ShortType | int or long Note: 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. | ShortType() |
IntegerType | int or long | IntegerType() |
LongType | long Note: 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. | LongType() |
FloatType | float Note: Numbers will be converted to 4-byte single-precision floating point numbers at runtime. | FloatType() |
DoubleType | float | DoubleType() |
DecimalType | decimal.Decimal | DecimalType() |
StringType | string | StringType() |
BinaryType | bytearray | BinaryType() |
BooleanType | bool | BooleanType() |
TimestampType | datetime.datetime | TimestampType() |
DateType | datetime.date | DateType() |
ArrayType | list, tuple, or array | ArrayType(elementType, [containsNull]) **Note:**The default value of containsNull is True. |
MapType | dict | MapType(keyType, valueType, [valueContainsNull]) **Note:**The default value of valueContainsNull is True. |
StructType | list or tuple | StructType(fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed. |
StructField | The value type in Python of the data type of this field (For example, Int for a StructField with the data type IntegerType) | StructField(name, dataType, [nullable]) Note: The default value of nullable is True. |
Data type | Value type in R | API to access or create a data type |
---|---|---|
ByteType | integer Note: 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. | “byte” |
ShortType | integer Note: 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. | “short” |
IntegerType | integer | “integer” |
LongType | integer Note: 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. | “long” |
FloatType | numeric Note: Numbers will be converted to 4-byte single-precision floating point numbers at runtime. | “float” |
DoubleType | numeric | “double” |
DecimalType | Not supported | Not supported |
StringType | character | “string” |
BinaryType | raw | “binary” |
BooleanType | logical | “bool” |
TimestampType | POSIXct | “timestamp” |
DateType | Date | “date” |
ArrayType | vector or list | list(type=“array”, elementType=elementType, containsNull=[containsNull]) Note: The default value of containsNull is TRUE. |
MapType | environment | list(type=“map”, keyType=keyType, valueType=valueType, valueContainsNull=[valueContainsNull]) Note: The default value of valueContainsNull is TRUE. |
StructType | named list | list(type=“struct”, fields=fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed. |
StructField | The value type in R of the data type of this field (For example, integer for a StructField with the data type IntegerType) | list(name=name, type=dataType, nullable=[nullable]) Note: The default value of nullable is TRUE. |
The following table shows the type names as well as aliases used in Spark SQL parser for each data type.
Data type | SQL name |
---|---|
BooleanType | BOOLEAN |
ByteType | BYTE, TINYINT |
ShortType | SHORT, SMALLINT |
IntegerType | INT, INTEGER |
LongType | LONG, BIGINT |
FloatType | FLOAT, REAL |
DoubleType | DOUBLE |
DateType | DATE |
TimestampType | TIMESTAMP |
StringType | STRING |
BinaryType | BINARY |
DecimalType | DECIMAL, DEC, NUMERIC |
YearMonthIntervalType | INTERVAL YEAR, INTERVAL YEAR TO MONTH, INTERVAL MONTH |
DayTimeIntervalType | INTERVAL DAY, INTERVAL DAY TO HOUR, INTERVAL DAY TO MINUTE, INTERVAL DAY TO SECOND, INTERVAL HOUR, INTERVAL HOUR TO MINUTE, INTERVAL HOUR TO SECOND, INTERVAL MINUTE, INTERVAL MINUTE TO SECOND, INTERVAL SECOND |
ArrayType | ARRAY<element_type> |
StructType | STRUCT<field1_name: field1_type, field2_name: field2_type, ...> Note: ‘:’ is optional. |
MapType | MAP<key_type, value_type> |
Spark SQL supports several special floating point values in a case-insensitive manner:
FloatType
: equivalent to Scala Float.PositiveInfinity.DoubleType
: equivalent to Scala Double.PositiveInfinity.FloatType
: equivalent to Scala Float.NegativeInfinity.DoubleType
: equivalent to Scala Double.NegativeInfinity.FloatType
: equivalent to Scala Float.NaN.DoubleType
: equivalent to Scala Double.NaN.There is special handling for positive and negative infinity. They have the following semantics:
There is special handling for not-a-number (NaN) when dealing with float
or double
types that do not exactly match standard floating point semantics. Specifically:
SELECT double('infinity') AS col; +--------+ | col| +--------+ |Infinity| +--------+ SELECT float('-inf') AS col; +---------+ | col| +---------+ |-Infinity| +---------+ SELECT float('NaN') AS col; +---+ |col| +---+ |NaN| +---+ SELECT double('infinity') * 0 AS col; +---+ |col| +---+ |NaN| +---+ SELECT double('-infinity') * (-1234567) AS col; +--------+ | col| +--------+ |Infinity| +--------+ SELECT double('infinity') < double('NaN') AS col; +----+ | col| +----+ |true| +----+ SELECT double('NaN') = double('NaN') AS col; +----+ | col| +----+ |true| +----+ SELECT double('inf') = double('infinity') AS col; +----+ | col| +----+ |true| +----+ CREATE TABLE test (c1 int, c2 double); INSERT INTO test VALUES (1, double('infinity')); INSERT INTO test VALUES (2, double('infinity')); INSERT INTO test VALUES (3, double('inf')); INSERT INTO test VALUES (4, double('-inf')); INSERT INTO test VALUES (5, double('NaN')); INSERT INTO test VALUES (6, double('NaN')); INSERT INTO test VALUES (7, double('-infinity')); SELECT COUNT(*), c2 FROM test GROUP BY c2; +---------+---------+ | count(1)| c2| +---------+---------+ | 2| NaN| | 2|-Infinity| | 3| Infinity| +---------+---------+