Data Types¶

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

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.

Presto strays from the IEEE standard when handling NaNs. In Presto, NaN is considered larger than any other value for all comparison and sorting operations. Additionally, NaN=NaN will be true for all equality and distinctness purposes.

DOUBLE¶

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

Presto strays from the IEEE standard when handling NaNs. In Presto, NaN is considered larger than any other value for all comparison and sorting operations. Additionally, NaN=NaN will be true for all equality and distinctness purposes.

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 supports simple and Unicode string literals:

• 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 these examples \2603 and #2603 represent a snowman character. Long Unicode codes with 6 digits require a plus symbol + before the code. For example, use \+01F600 for a grinning face emoji.

Single quotes in string literals can be escaped by using another single quote: 'It''s a beautiful day!'

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) are never equal, but comparison of such values implicitly converts the types to the same length and pads with spaces so that the following query returns true:

SELECT cast('example' AS char(20)) = cast('example    ' AS char(25));

As with VARCHAR, a single quote in a CHAR literal can be escaped with another single quote:

SELECT CHAR 'All right, Mr. DeMille, I''m ready for my close-up.'

VARBINARY¶

Variable length binary data.

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 the 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'

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.

KHyperLogLog¶

KHyperLogLog¶

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

SetDigest¶

SetDigest¶

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

SetDigest encapsulates the following components:

• HyperLogLog

• MinHash with a single hash function

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.

SFM Sketch¶

SfmSketch¶

The Sketch-Flip-Merge (SFM) data sketch is a noisy, random distinct-counting sketch similar to HyperLogLog. See noisy_approx_set_sfm().

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.

See Quantile Digest Functions.

T-Digest¶

TDigest¶

A t-digest is similar to qdigest, but it uses a different algorithm to represent the approximate distribution of a set of numbers. T-digest has better performance than quantile digests but only supports the DOUBLE type. See T-Digest Functions.

KLL Sketch¶

KLL Sketch¶

A KLL sketch is similar to the qdigest, but, like the T-Digest uses a different algorithm to represent the approximate distribution of a set of values. The KLL sketch in Presto supports int, bigint, double, varchar, and boolean types. See Sketch Functions for more information. In serialized form, the kllsketch type stored by Presto can be read directly by any other application which utilizes the Apache DataSketches library to read KLL sketches.

Geospatial¶

Geospatial¶

Geospatial types in Presto are designed to handle and analyze spatial data efficiently, adhering to the SQL/MM specification and the Open Geospatial Consortium standards. These types include POINT, LINESTRING, POLYGON, MULTIPOINT, MULTILINESTRING, MULTIPOLYGON, and GEOMETRYCOLLECTION, which can be expressed in Well-Known Text (WKT) and Well-Known Binary (WKB) formats. The types support operations such as spatial measurements and relationship checks, crucial for geographic information systems (GIS) and other applications requiring spatial data manipulation. The geospatial types ensure data integrity and provide robust tools for complex spatial querying and analysis.

See Geospatial Functions.