Standard SQL Data Types

• Data type properties
• Numeric types
  • Integer type
  • NUMERIC type
  • Floating point type
    • Floating point semantics
      • Mathematical function examples
      • Comparison operator examples
• Boolean type
• String type
• Bytes type
• Date type
  • Canonical format
• Datetime type
  • Canonical format
• Time type
  • Canonical format
• Timestamp type
  • Canonical format
  • Time zones
    • Offset from Coordinated Universal Time (UTC)
      • Offset Format
      • Examples
    • Time zone name
      • Format
      • Examples
    • Leap seconds
• Array type
  • Declaring an ARRAY type
    • Format
    • Examples
• Struct type
  • Declaring a STRUCT type
    • Format
    • Examples
  • Constructing a STRUCT
    • Tuple syntax
      • Format
      • Examples
    • Typeless struct syntax
      • Format
      • Examples
    • Typed struct syntax
      • Format
      • Examples
  • Limited comparisons for STRUCT

BigQuery supports simple data types such as integers, as well as more complex types such as ARRAY and STRUCT. This page provides an overview of each data type, including allowed values. For information on data type literals and constructors, see Lexical Structure and Syntax.

Data type properties

When storing and querying data, it is helpful to keep the following data type properties in mind:

Property	Description	Applies To
Nullable	NULL is a valid value.	All data types, with the following exceptions: ARRAYs cannot be NULL. NULL ARRAY elements cannot persist to a table.
Orderable	Can be used in an ORDER BY clause.	All data types except for: ARRAY STRUCT
Groupable	Can generally appear in an expression following GROUP BY, DISTINCT, or PARTITION BY. However, PARTITION BY expressions cannot include the floating point types FLOAT and DOUBLE.	All data types except for: ARRAY STRUCT
Comparable	Values of the same type can be compared to each other.	All data types, with the following exceptions: ARRAY comparisons are not supported. Equality comparisons for STRUCTs are supported field by field, in field order. Field names are ignored. Less than and greater than comparisons are not supported. All types that support comparisons can be used in a JOIN condition. See JOIN Types for an explanation of join conditions.

Numeric types

Numeric types include integer types, floating point types and the NUMERIC data type.

Integer type

Integers are numeric values that do not have fractional components.

Name	Storage Size	Range
INT64	8 bytes	-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807

NUMERIC type

The NUMERIC data type is an exact numeric value with 38 digits of precision and 9 decimal digits of scale. Precision is the number of digits that the number contains. Scale is how many of these digits appear after the decimal point.

This type can represent decimal fractions exactly, and is suitable for financial calculations.

Name	Storage Size	Description	Range
NUMERIC	16 bytes	Decimal values with 38 decimal digits of precision and 9 decimal digits of scale.	-99999999999999999999999999999.999999999 to 99999999999999999999999999999.999999999

Floating point type

Floating point values are approximate numeric values with fractional components.

Name	Storage Size	Description
FLOAT64	8 bytes	Double precision (approximate) decimal values.

Floating point semantics

When working with floating point numbers, there are special non-numeric values that need to be considered: NaN and +/-inf

Arithmetic operators provide standard IEEE-754 behavior for all finite input values that produce finite output and for all operations for which at least one input is non-finite.

Function calls and operators return an overflow error if the input is finite but the output would be non-finite. If the input contains non-finite values, the output can be non-finite. In general functions do not introduce NaNs or +/-inf. However, specific functions like IEEE_DIVIDE can return non-finite values on finite input. All such cases are noted explicitly in Mathematical functions.

Mathematical function examples

Left Term	Operator	Right Term	Returns
Any value	+	NaN	NaN
1.0	+	+inf	+inf
1.0	+	-inf	-inf
-inf	+	+inf	NaN
Maximum FLOAT64 value	+	Maximum FLOAT64 value	Overflow error
Minimum FLOAT64 value	/	2.0	0.0
1.0	/	0.0	"Divide by zero" error

Comparison operators provide standard IEEE-754 behavior for floating point input.

Comparison operator examples

Left Term	Operator	Right Term	Returns
NaN	=	Any value	FALSE
NaN	<	Any value	FALSE
Any value	<	NaN	FALSE
-0.0	=	0.0	TRUE
-0.0	<	0.0	FALSE

Floating point values are sorted in this order, from least to greatest:

1. NULL
2. NaN — All NaN values are considered equal when sorting.
3. -inf
4. Negative numbers
5. 0 or -0 — All zero values are considered equal when sorting.
6. Positive numbers
7. +inf

Special floating point values are grouped this way, including both grouping done by a GROUP BY clause and grouping done by the DISTINCT keyword:

• NULL
• NaN — All NaN values are considered equal when grouping.
• -inf
• 0 or -0 — All zero values are considered equal when grouping.
• +inf

Boolean type

Name	Description
BOOL	Boolean values are represented by the keywords TRUE and FALSE (case insensitive).

String type

Name	Description
STRING	Variable-length character (Unicode) data.

Input STRING values must be UTF-8 encoded and output STRING values will be UTF-8 encoded. Alternate encodings like CESU-8 and Modified UTF-8 are not treated as valid UTF-8.

All functions and operators that act on STRING values operate on Unicode characters rather than bytes. For example, functions like SUBSTR and LENGTH applied to STRING input count Unicode characters, not bytes. Comparisons are defined on Unicode characters. Comparisons for less than and ORDER BY compare character by character, and lower unicode code points are considered lower characters.

Most functions on STRING are also defined on BYTES. The BYTES version operates on raw bytes rather than Unicode characters. STRING and BYTES are separate types that cannot be used interchangeably. There is no implicit casting in either direction. Explicit casting between STRING and BYTES does UTF-8 encoding and decoding. Casting BYTES to STRING returns an error if the bytes are not valid UTF-8.

Bytes type

Name	Description
BYTES	Variable-length binary data.

STRING and BYTES are separate types that cannot be used interchangeably. Most functions on STRING are also defined on BYTES. The BYTES version operates on raw bytes rather than Unicode characters. Casts between STRING and BYTES enforce that the bytes are encoded using UTF-8.

Date type

Name	Description	Range
DATE	Represents a logical calendar date.	0001-01-01 to 9999-12-31.

The DATE type represents a logical calendar date, independent of time zone. A DATE value does not represent a specific 24-hour time period. Rather, a given DATE value represents a different 24-hour period when interpreted in different time zones, and may represent a shorter or longer day during Daylight Savings Time transitions. To represent an absolute point in time, use a timestamp.

Canonical format

'YYYY-[M]M-[D]D'

• YYYY: Four-digit year
• [M]M: One or two digit month
• [D]D: One or two digit day

Datetime type

Name	Description	Range
DATETIME	Represents a year, month, day, hour, minute, second, and subsecond.	0001-01-01 00:00:00 to 9999-12-31 23:59:59.999999.

A DATETIME represents a point in time. Each DATETIME contains the following:

• year
• month
• day
• hour
• minute
• second
• subsecond

Unlike Timestamps, a DATETIME object does not refer to an absolute instance in time. Instead, it is the civil time, or the time that a user would see on a watch or calendar.

Canonical format

YYYY-[M]M-[D]D[( |T)[H]H:[M]M:[S]S[.DDDDDD]]

• YYYY: Four-digit year
• [M]M: One or two digit month
• [D]D: One or two digit day
• ( |T): A space or a T separator
• [H]H: One or two digit hour (valid values from 00 to 23)
• [M]M: One or two digit minutes (valid values from 00 to 59)
• [S]S: One or two digit seconds (valid values from 00 to 59)
• [.DDDDDD]: Up to six fractional digits (i.e. up to microsecond precision)

Time type

Name	Description	Range
TIME	Represents a time, independent of a specific date.	00:00:00 to 23:59:59.999999.

A TIME data type represents a time, independent of a specific date.

Canonical format

[H]H:[M]M:[S]S[.DDDDDD]

• [H]H: One or two digit hour (valid values from 00 to 23)
• [M]M: One or two digit minutes (valid values from 00 to 59)
• [S]S: One or two digit seconds (valid values from 00 to 59)
• [.DDDDDD]: Up to six fractional digits (i.e. up to microsecond precision)

Timestamp type

Name	Description	Range
TIMESTAMP	Represents an absolute point in time, with microsecond precision.	0001-01-01 00:00:00 to 9999-12-31 23:59:59.999999 UTC.

A timestamp represents an absolute point in time, independent of any time zone or convention such as Daylight Savings Time.

TIMESTAMP provides microsecond precision.

Canonical format

YYYY-[M]M-[D]D[( |T)[H]H:[M]M:[S]S[.DDDDDD]][time zone]

• YYYY: Four-digit year
• [M]M: One or two digit month
• [D]D: One or two digit day
• ( |T): A space or a T separator
• [H]H: One or two digit hour (valid values from 00 to 23)
• [M]M: One or two digit minutes (valid values from 00 to 59)
• [S]S: One or two digit seconds (valid values from 00 to 59)
• [.DDDDDD]: Up to six fractional digits (i.e. up to microsecond precision)
• [time zone]: String representing the time zone. See the time zones section for details.

Time zones are used when parsing timestamps or formatting timestamps for display. The timestamp value itself does not store a specific time zone. A string-formatted timestamp may include a time zone. When a time zone is not explicitly specified, the default time zone, UTC, is used.

Time zones

Time zones are represented by strings in one of these two canonical formats:

• Offset from Coordinated Universal Time (UTC), or the letter Z for UTC
• Time zone name from the tz database

Offset from Coordinated Universal Time (UTC)

Offset Format

(+|-)H[H][:M[M]]
Z

Examples

-08:00
-8:15
+3:00
+07:30
-7
Z

When using this format, no space is allowed between the time zone and the rest of the timestamp.

2014-09-27 12:30:00.45-8:00
2014-09-27T12:30:00.45Z

Time zone name

Time zone names are from the tz database. For a less comprehensive but simpler reference, see the List of tz database time zones on Wikipedia.

Format

continent/[region/]city

Examples

America/Los_Angeles
America/Argentina/Buenos_Aires

When using a time zone name, a space is required between the name and the rest of the timestamp:

2014-09-27 12:30:00.45 America/Los_Angeles

Note that not all time zone names are interchangeable even if they do happen to report the same time during a given part of the year. For example, America/Los_Angeles reports the same time as UTC-7:00 during Daylight Savings Time, but reports the same time as UTC-8:00 outside of Daylight Savings Time.

If a time zone is not specified, the default time zone value is used.

Leap seconds

A timestamp is simply an offset from 1970-01-01 00:00:00 UTC, assuming there are exactly 60 seconds per minute. Leap seconds are not represented as part of a stored timestamp.

If your input contains values that use ":60" in the seconds field to represent a leap second, that leap second is not preserved when converting to a timestamp value. Instead that value is interpreted as a timestamp with ":00" in the seconds field of the following minute.

Leap seconds do not affect timestamp computations. All timestamp computations are done using Unix-style timestamps, which do not reflect leap seconds. Leap seconds are only observable through functions that measure real-world time. In these functions, it is possible for a timestamp second to be skipped or repeated when there is a leap second.

Array type

Name	Description
ARRAY	Ordered list of zero or more elements of any non-ARRAY type.

An ARRAY is an ordered list of zero or more elements of non-ARRAY values. ARRAYs of ARRAYs are not allowed. Queries that would produce an ARRAY of ARRAYs will return an error. Instead a STRUCT must be inserted between the ARRAYs using the SELECT AS STRUCT construct.

Currently, BigQuery has two following limitations with respect to NULLs and ARRAYs:

• BigQuery raises an error if query result has ARRAYs which contain NULL elements, although such ARRAYs can be used inside the query.
• BigQuery translates NULL ARRAY into empty ARRAY in the query result, although inside the query NULL and empty ARRAYs are two distinct values.

Declaring an ARRAY type

ARRAY types are declared using the angle brackets (< and >). The type of the elements of an ARRAY can be arbitrarily complex with the exception that an ARRAY cannot directly contain another ARRAY.

Format

ARRAY<T>

Examples

Type Declaration	Meaning
ARRAY<INT64>	Simple ARRAY of 64-bit integers.
ARRAY<STRUCT<INT64, INT64>>	An ARRAY of STRUCTs, each of which contains two 64-bit integers.
ARRAY<ARRAY<INT64>> (not supported)	This is an invalid type declaration which is included here just in case you came looking for how to create a multi-level ARRAY. ARRAYs cannot contain ARRAYs directly. Instead see the next example.
ARRAY<STRUCT<ARRAY<INT64>>>	An ARRAY of ARRAYS of 64-bit integers. Notice that there is a STRUCT between the two ARRAYs because ARRAYs cannot hold other ARRAYs directly.

Struct type

Name	Description
STRUCT	Container of ordered fields each with a type (required) and field name (optional).

Declaring a STRUCT type

STRUCT types are declared using the angle brackets (< and >). The type of the elements of a STRUCT can be arbitrarily complex.

Format

STRUCT<T>

Examples

Type Declaration	Meaning
STRUCT<INT64>	Simple STRUCT with a single unnamed 64-bit integer field.
STRUCT<x STRUCT<y INT64, z INT64>>	A STRUCT with a nested STRUCT named x inside it. The STRUCT x has two fields, y and z, both of which are 64-bit integers.
STRUCT<inner_array ARRAY<INT64>>	A STRUCT containing an ARRAY named inner_array that holds 64-bit integer elements.

Constructing a STRUCT

Tuple syntax

Format

(expr1, expr2 [, ... ])

The output type is an anonymous STRUCT type with anonymous fields with types matching the types of the input expressions. There must be at least two expressions specified. Otherwise this syntax is indistinguishable from an expression wrapped with parentheses.

Examples

Syntax	Output Type	Notes
(x, x+y)	STRUCT<?,?>	If column names are used (unquoted strings), the STRUCT field data type is derived from the column data type. x and y are columns, so the data types of the STRUCT fields are derived from the column types and the output type of the addition operator.

This syntax can also be used with STRUCT comparison for comparison expressions using multi-part keys, e.g. in a WHERE clause:

WHERE (Key1,Key2) IN ( (12,34), (56,78) )

Typeless struct syntax

Format

STRUCT( expr1 [AS field_name] [, ... ])

Duplicate field names are allowed. Fields without names are considered anonymous fields and cannot be referenced by name. STRUCT values can be NULL, or can have NULL field values.

Examples

Syntax	Output Type
STRUCT(1,2,3)	STRUCT<int64,int64,int64>
STRUCT()	STRUCT<>
STRUCT('abc')	STRUCT<string>
STRUCT(1, t.str_col)	STRUCT<int64, str_col string>
STRUCT(1 AS a, 'abc' AS b)	STRUCT<a int64, b string>
STRUCT(str_col AS abc)	STRUCT<abc string>

Typed struct syntax

Format

STRUCT<[field_name] field_type, ...>( expr1 [, ... ])

Typed syntax allows constructing STRUCTs with an explicit STRUCT data type. The output type is exactly the field_type provided. The input expression is coerced to field_type if the two types are not the same, and an error is produced if the types are not compatible. AS alias is not allowed on the input expressions. The number of expressions must match the number of fields in the type, and the expression types must be coercible or literal-coercible to the field types.

Examples

Syntax	Output Type
STRUCT<int64>(5)	STRUCT<int64>
STRUCT<date>("2011-05-05")	STRUCT<date>
STRUCT<x int64, y string>(1, t.str_col)	STRUCT<x int64, y string>
STRUCT<int64>(int_col)	STRUCT<int64>
STRUCT<x int64>(5 AS x)	Error - Typed syntax does not allow AS

Limited comparisons for STRUCT

STRUCTs can be directly compared using equality operators:

• Equal (=)
• Not Equal (!= or <>)
• [NOT] IN

Notice, though, that these direct equality comparisons compare the fields of the STRUCT pairwise in ordinal order ignoring any field names. If instead you want to compare identically named fields of a STRUCT, you can compare the individual fields directly.