Datatypes

Overview of the data types supported in DuneSQL.

DuneSQL has a set of built-in data types, described below.

Implicit Conversion and Casting to other datatypes is described in the conversion function documentation.

Boolean

BOOLEAN

This type captures boolean values true and false.

Binary

VARBINARY

Variable length binary data. In Dune, we store addresses, hashes, calldata and logs as varbinary data types.

SQL statements support usage of binary data with the prefix 0x. The binary data has to use hexadecimal format. For example, the binary form of eh? is X'65683F'.

We have built custom functions to make it easier to work with varbinaries in DuneSQL. Check the varbinary functions page for more information.

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.

UINT256 (Dune SQL)

A 256-bit unsigned integer with a minimum value of 0 and a maximum value of 2^256 - 1. This data type can represent only non-negative integers, including very large positive integers, as well as zero. Since there is no sign bit, all 256 bits can be used to represent the magnitude of the number. This data type is commonly used in EVM smart contracts to represent balances and other quantities.

INT256 (Dune SQL)

A 256-bit signed two’s complement integer with a minimum value of -2^255 and a maximum value of 2^255 - 1. This data type can represent a wide range of values, including very large negative and positive integers, as well as zero.

This data type is commonly used in EVM smart contracts to represent balances and other quantities, more specifically when the value can be negative.

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)

    Select * from ethereum.transactions where "from" = 0xc8ebccc5f5689fa8659d83713341e5ad19349448

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.

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-data-type}

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 can be expressed in the following ways:

• UTC, with GMT, Z, or UT usable as aliases for UTC.
• +hh:mm or -hh:mm with hh:mm as an hour and minute offset from UTC. Can be written with or without UTC, GMT, or UT as an alias for UTC.
• An IANA time zone name.

The following examples demonstrate some of these syntax options:

    SELECT TIMESTAMP '2001-08-22 03:04:05.321 UTC';
    -- 2001-08-22 03:04:05.321 UTC

    SELECT TIMESTAMP '2001-08-22 03:04:05.321 -08:30';
    -- 2001-08-22 03:04:05.321 -08:30

    SELECT TIMESTAMP '2001-08-22 03:04:05.321 GMT-08:30';
    -- 2001-08-22 03:04:05.321 -08:30

    SELECT TIMESTAMP '2001-08-22 03:04:05.321 America/New_York';
    -- 2001-08-22 03:04:05.321 America/New_York

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 (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 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 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.

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_type, 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:

• 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.

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.