Data Types

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

Note

Connectors are not required to support all types. See connector documentation for details on supported types.

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.

DOUBLE

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

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

Note

For compatibility reasons decimal literals without explicit type specifier (e.g. 1.2) are treated as values of the DOUBLE type by default up to version 0.198. After 0.198 they are parsed as DECIMAL.

  • System wide property: parse-decimal-literals-as-double

  • Session wide property: parse_decimal_literals_as_double

String

VARCHAR

Variable length character data with an optional maximum length.

Example type definitions: varchar, varchar(20)

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 instance, 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.

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

DATE

Calendar date (year, month, day).

Example: DATE '2001-08-22'

TIME

Time of day (hour, minute, second, millisecond) without a time zone. Values of this type are parsed and rendered in the session time zone.

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.

Example: TIME '01:02:03.456 America/Los_Angeles'

TIMESTAMP

Instant in time that includes the date and time of day without a time zone. Values of this type are parsed and rendered in the session time zone.

Example: TIMESTAMP '2001-08-22 03:04:05.321'

TIMESTAMP WITH TIME ZONE

Instant in time that includes the date and time of day with a time zone. Values of this type are rendered using the time zone from the value.

Example: TIMESTAMP '2001-08-22 03:04:05.321 America/Los_Angeles'

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 named fields. The fields may be of any SQL type, and are accessed with field reference operator .

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

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'

IPPREFIX

An IP routing prefix that can represent either an IPv4 or IPv6 address.

Internally, an address 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 IPPREFIX, IPv4 addresses will be mapped into that range. Additionally, addresses will be reduced to the first address of a network.

IPPREFIX values will be formatted in CIDR notation, written as an IP address, a slash (‘/’) character, and the bit-length of the prefix. Any address within the IPv4-mapped IPv6 address range will be formatted as an IPv4 address. Other addresses will be formatted as IPv6 using the canonical format defined in RFC 5952.

Examples: IPPREFIX '10.0.1.0/24', IPPREFIX '2001:db8::/48'

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.

KHyperLogLog

KHyperLogLog

A KHyperLogLog is a data sketch that can be used to compactly represents the association of two columns. See KHyperLogLog Functions.

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.