Data types#

Trino has a set of built-in data types, described below. Additional types can be provided by plugins.

Trino type support and mapping#

Connectors to data sources are not required to support all Trino data types described on this page. If there are data types similar to Trino’s that are used on the data source, the connector may map the Trino and remote data types to each other as needed.

Depending on the connector and the data source, type mapping may apply in either direction as follows:

  • Data source to Trino mapping applies to any operation where columns in the data source are read by Trino, such as a SELECT statement, and the underlying source data type needs to be represented by a Trino data type.

  • Trino to data source mapping applies to any operation where the columns or expressions in Trino need to be translated into data types or expressions compatible with the underlying data source. For example, CREATE TABLE AS statements specify Trino types that are then mapped to types on the remote data source. Predicates like WHERE also use these mappings in order to ensure that the predicate is translated to valid syntax on the remote data source.

Data type support and mappings vary depending on the connector. Refer to the connector documentation for more information.

Boolean#

BOOLEAN#

This type captures boolean values true and false.

Integer#

TINYINT#

A 8-bit signed two’s complement integer with a minimum value of -2^7 and a maximum value of 2^7 - 1.

SMALLINT#

A 16-bit signed two’s complement integer with a minimum value of -2^15 and a maximum value of 2^15 - 1.

INTEGER#

A 32-bit signed two’s complement integer with a minimum value of -2^31 and a maximum value of 2^31 - 1. The name INT is also available for this type.

BIGINT#

A 64-bit signed two’s complement integer with a minimum value of -2^63 and a maximum value of 2^63 - 1.

Floating-point#

REAL#

A real is a 32-bit inexact, variable-precision implementing the IEEE Standard 754 for Binary Floating-Point Arithmetic.

Example literals: REAL '10.3', REAL '10.3e0', REAL '1.03e1'

DOUBLE#

A double is a 64-bit inexact, variable-precision implementing the IEEE Standard 754 for Binary Floating-Point Arithmetic.

Example literals: DOUBLE '10.3', DOUBLE '1.03e1', 10.3e0, 1.03e1

Fixed-precision#

DECIMAL#

A fixed precision decimal number. Precision up to 38 digits is supported but performance is best up to 18 digits.

The decimal type takes two literal parameters:

  • precision - total number of digits

  • scale - number of digits in fractional part. Scale is optional and defaults to 0.

Example type definitions: DECIMAL(10,3), DECIMAL(20)

Example literals: DECIMAL '10.3', DECIMAL '1234567890', 1.1

String#

VARCHAR#

Variable length character data with an optional maximum length.

Example type definitions: varchar, varchar(20)

SQL statements support simple literal, as well as Unicode usage:

  • literal string : 'Hello winter !'

  • Unicode string with default escape character: U&'Hello winter \2603 !'

  • Unicode string with custom escape character: U&'Hello winter #2603 !' UESCAPE '#'

A Unicode string is prefixed with U& and requires an escape character before any Unicode character usage with 4 digits. In the examples above \2603 and #2603 represent a snowman character. Long Unicode codes with 6 digits require usage of the plus symbol before the code. For example, you need to use \+01F600 for a grinning face emoji.

CHAR#

Fixed length character data. A CHAR type without length specified has a default length of 1. A CHAR(x) value always has x characters. For example, casting dog to CHAR(7) adds 4 implicit trailing spaces. Leading and trailing spaces are included in comparisons of CHAR values. As a result, two character values with different lengths (CHAR(x) and CHAR(y) where x != y) will never be equal.

Example type definitions: char, char(20)

VARBINARY#

Variable length binary data.

SQL statements support usage of binary data with the prefix X. The binary data has to use hexadecimal format. For example, the binary form of eh? is X'65683F'.

Note

Binary strings with length are not yet supported: varbinary(n)

JSON#

JSON value type, which can be a JSON object, a JSON array, a JSON number, a JSON string, true, false or null.

Date and time#

See also Date and time functions and operators

DATE#

Calendar date (year, month, day).

Example: DATE '2001-08-22'

TIME#

TIME is an alias for TIME(3) (millisecond precision).

TIME(P)#

Time of day (hour, minute, second) without a time zone with P digits of precision for the fraction of seconds. A precision of up to 12 (picoseconds) is supported.

Example: TIME '01:02:03.456'

TIME WITH TIME ZONE#

Time of day (hour, minute, second, millisecond) with a time zone. Values of this type are rendered using the time zone from the value. Time zones are expressed as the numeric UTC offset value:

SELECT TIME '01:02:03.456 -08:00';
-- 1:02:03.456-08:00

TIMESTAMP#

TIMESTAMP is an alias for TIMESTAMP(3) (millisecond precision).

TIMESTAMP(P)#

Calendar date and time of day without a time zone with P digits of precision for the fraction of seconds. A precision of up to 12 (picoseconds) is supported. This type is effectively a combination of the DATE and TIME(P) types.

TIMESTAMP(P) WITHOUT TIME ZONE is an equivalent name.

Timestamp values can be constructed with the TIMESTAMP literal expression. Alternatively, language constructs such as localtimestamp(p), or a number of date and time functions and operators can return timestamp values.

Casting to lower precision causes the value to be rounded, and not truncated. Casting to higher precision appends zeros for the additional digits.

The following examples illustrate the behavior:

SELECT TIMESTAMP '2020-06-10 15:55:23';
-- 2020-06-10 15:55:23

SELECT TIMESTAMP '2020-06-10 15:55:23.383345';
-- 2020-06-10 15:55:23.383345

SELECT typeof(TIMESTAMP '2020-06-10 15:55:23.383345');
-- timestamp(6)

SELECT cast(TIMESTAMP '2020-06-10 15:55:23.383345' as TIMESTAMP(1));
 -- 2020-06-10 15:55:23.4

SELECT cast(TIMESTAMP '2020-06-10 15:55:23.383345' as TIMESTAMP(12));
-- 2020-06-10 15:55:23.383345000000

TIMESTAMP WITH TIME ZONE#

TIMESTAMP WITH TIME ZONE is an alias for TIMESTAMP(3) WITH TIME ZONE (millisecond precision).

TIMESTAMP(P) WITH TIME ZONE#

Instant in time that includes the date and time of day with P digits of precision for the fraction of seconds and with a time zone. Values of this type are rendered using the time zone from the value. Time zones are expressed as the numeric UTC offset value:

TIMESTAMP '2001-08-22 03:04:05.321 -08:00';
-- 2001-08-22 03:04:05.321-08:00

INTERVAL YEAR TO MONTH#

Span of years and months.

Example: INTERVAL '3' MONTH

INTERVAL DAY TO SECOND#

Span of days, hours, minutes, seconds and milliseconds.

Example: INTERVAL '2' DAY

Structural#

ARRAY#

An array of the given component type.

Example: ARRAY[1, 2, 3]

MAP#

A map between the given component types.

Example: MAP(ARRAY['foo', 'bar'], ARRAY[1, 2])

ROW#

A structure made up of fields that allows mixed types. The fields may be of any SQL type.

By default, row fields are not named, but names can be assigned.

Example: CAST(ROW(1, 2e0) AS ROW(x BIGINT, y DOUBLE))

Named row fields are accessed with field reference operator (.).

Example: CAST(ROW(1, 2.0) AS ROW(x BIGINT, y DOUBLE)).x

Named or unnamed row fields are accessed by position with the subscript operator ([]). The position starts at 1 and must be a constant.

Example: ROW(1, 2.0)[1]

Network address#

IPADDRESS#

An IP address that can represent either an IPv4 or IPv6 address. Internally, the type is a pure IPv6 address. Support for IPv4 is handled using the IPv4-mapped IPv6 address range (RFC 4291#section-2.5.5.2). When creating an IPADDRESS, IPv4 addresses will be mapped into that range. When formatting an IPADDRESS, any address within the mapped range will be formatted as an IPv4 address. Other addresses will be formatted as IPv6 using the canonical format defined in RFC 5952.

Examples: IPADDRESS '10.0.0.1', IPADDRESS '2001:db8::1'

UUID#

UUID#

This type represents a UUID (Universally Unique IDentifier), also known as a GUID (Globally Unique IDentifier), using the format defined in RFC 4122.

Example: UUID '12151fd2-7586-11e9-8f9e-2a86e4085a59'

HyperLogLog#

Calculating the approximate distinct count can be done much more cheaply than an exact count using the HyperLogLog data sketch. See HyperLogLog functions.

HyperLogLog#

A HyperLogLog sketch allows efficient computation of approx_distinct(). It starts as a sparse representation, switching to a dense representation when it becomes more efficient.

P4HyperLogLog#

A P4HyperLogLog sketch is similar to HyperLogLog, but it starts (and remains) in the dense representation.

SetDigest#

SetDigest#

A SetDigest (setdigest) is a data sketch structure used in calculating Jaccard similarity coefficient between two sets.

SetDigest encapsulates the following components:

The HyperLogLog structure is used for the approximation of the distinct elements in the original set.

The MinHash structure is used to store a low memory footprint signature of the original set. The similarity of any two sets is estimated by comparing their signatures.

SetDigests are additive, meaning they can be merged together.

Quantile digest#

QDigest#

A quantile digest (qdigest) is a summary structure which captures the approximate distribution of data for a given input set, and can be queried to retrieve approximate quantile values from the distribution. The level of accuracy for a qdigest is tunable, allowing for more precise results at the expense of space.

A qdigest can be used to give approximate answer to queries asking for what value belongs at a certain quantile. A useful property of qdigests is that they are additive, meaning they can be merged together without losing precision.

A qdigest may be helpful whenever the partial results of approx_percentile can be reused. For example, one may be interested in a daily reading of the 99th percentile values that are read over the course of a week. Instead of calculating the past week of data with approx_percentile, qdigests could be stored daily, and quickly merged to retrieve the 99th percentile value.

T-Digest#

TDigest#

A T-digest (tdigest) is a summary structure which, similarly to qdigest, captures the approximate distribution of data for a given input set. It can be queried to retrieve approximate quantile values from the distribution.

TDigest has the following advantages compared to QDigest:

  • higher performance

  • lower memory usage

  • higher accuracy at high and low percentiles

T-digests are additive, meaning they can be merged together.