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Data Types

Table of contents

Overview

OpenSearch SQL Data Types

The OpenSearch SQL Engine support the following data types.

OpenSearch SQL Type
boolean
byte
short
integer
long
float
double
keyword
text
timestamp
date
date_nanos
time
interval
ip
geo_point
binary
struct
array

Data Types Mapping

The table below list the mapping between OpenSearch Data Type, OpenSearch SQL Data Type and SQL Type.

OpenSearch Type OpenSearch SQL Type SQL Type
boolean boolean BOOLEAN
byte byte TINYINT
short byte SMALLINT
integer integer INTEGER
long long BIGINT
float float REAL
half_float float FLOAT
scaled_float float DOUBLE
double double DOUBLE
keyword keyword VARCHAR
text text VARCHAR
date* timestamp TIMESTAMP
date_nanos timestamp TIMESTAMP
ip ip VARCHAR
binary binary VARBINARY
object struct STRUCT
nested array STRUCT

Notes: * Not all the OpenSearch SQL Type has correspond OpenSearch Type. e.g. data and time. To use function which required such data type, user should explicitly convert the data type. * date*: Maps to timestamp by default. Based on the "format" property date can map to date or time. See list of supported named formats here. For example, basic_date will map to a date type, and basic_time will map to a time type.

Data Type Conversion

A data type can be converted to another, implicitly or explicitly or impossibly, according to type precedence defined and whether the conversion is supported by query engine.

The general rules and design tenets for data type conversion include:

  1. Implicit conversion is defined by type precedence which is represented by the type hierarchy tree. See Data Type Conversion in SQL/PPL for more details.
  2. Explicit conversion defines the complete set of conversion allowed. If no explicit conversion defined, implicit conversion should be impossible too.
  3. On the other hand, if implicit conversion can occur between 2 types, then explicit conversion should be allowed too.
  4. Conversion within a data type family is considered as conversion between different data representation and should be supported as much as possible.
  5. Conversion across two data type families is considered as data reinterpretation and should be enabled with strong motivation.

Type Conversion Matrix

The following matrix illustrates the conversions allowed by our query engine for all the built-in data types as well as types provided by OpenSearch storage engine.

Data Types Numeric Type Family BOOLEAN String Type Family Datetime Type Family OpenSearch Type Family Complex Type Family
BYTE SHORT INTEGER LONG FLOAT DOUBLE BOOLEAN TEXT_KEYWORD TEXT STRING TIMESTAMP DATE TIME INTERVAL GEO_POINT IP BINARY STRUCT ARRAY
UNDEFINED IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE
BYTE N/A IE IE IE IE IE X X X E X X X X X X X X X
SHORT E N/A IE IE IE IE X X X E X X X X X X X X X
INTEGER E E N/A IE IE IE X X X E X X X X X X X X X
LONG E E E N/A IE IE X X X E X X X X X X X X X
FLOAT E E E E N/A IE X X X E X X X X X X X X X
DOUBLE E E E E E N/A X X X E X X X X X X | X X X
BOOLEAN E E E E E E N/A X X E X X X X X X | X X X
TEXT_KEYWORD               N/A   IE       X X X X X X
TEXT                 N/A IE       X X X X X X
STRING E E E E E E IE X X N/A IE IE IE X X X X X X
TIMESTAMP X X X X X X X X X E N/A IE IE X X X X X X
DATE X X X X X X X X X E E N/A IE X X X X X X
TIME X X X X X X X X X E E E N/A X X X X X X
INTERVAL X X X X X X X X X E X X X N/A X X X X X
GEO_POINT X X X X X X X X X   X X X X N/A X X X X
IP X X X X X X X X X   X X X X X N/A X X X
BINARY X X X X X X X X X   X X X X X X N/A X X
STRUCT X X X X X X X X X   X X X X X X X N/A X
ARRAY X X X X X X X X X   X X X X X X X X N/A

Note that:

  1. I means if implicit conversion will occur automatically. E stands for explicit conversion by CAST function. X for impossible to convert. Empty means not clear and need more test.
  2. There is no UNDEFINED column because it's only for NULL literal at runtime and should not be present in function signature definition.
  3. OpenSearch and complex types are not supported by CAST function, so it's impossible to convert a type to it for now.

Examples

Here are a few examples for implicit type conversion:

os> SELECT
...  1 = 1.0,
...  'True' = true,
...  DATE('2021-06-10') < '2021-06-11';
fetched rows / total rows = 1/1
+-----------+-----------------+-------------------------------------+
| 1 = 1.0   | 'True' = true   | DATE('2021-06-10') < '2021-06-11'   |
|-----------+-----------------+-------------------------------------|
| True      | True            | True                                |
+-----------+-----------------+-------------------------------------+

Here are a few examples for explicit type conversion:

os> SELECT
...  CAST(true AS INT),
...  CAST(1.2 AS STRING),
...  CAST('2021-06-10 00:00:00' AS TIMESTAMP);
fetched rows / total rows = 1/1
+---------------------+-----------------------+--------------------------------------------+
| CAST(true AS INT)   | CAST(1.2 AS STRING)   | CAST('2021-06-10 00:00:00' AS TIMESTAMP)   |
|---------------------+-----------------------+--------------------------------------------|
| 1                   | 1.2                   | 2021-06-10 00:00:00                        |
+---------------------+-----------------------+--------------------------------------------+

Undefined Data Type

The type of a null literal is special and different from any existing one. In this case, an UNDEFINED type is in use when the type cannot be inferred at "compile time" (during query parsing and analyzing). The corresponding SQL type is NULL according to JDBC specification. Because this undefined type is compatible with any other type by design, a null literal can be accepted as a valid operand or function argument.

Here are examples for NULL literal and expressions with NULL literal involved:

os> SELECT NULL, NULL = NULL, 1 + NULL, LENGTH(NULL);
fetched rows / total rows = 1/1
+--------+---------------+------------+----------------+
| NULL   | NULL = NULL   | 1 + NULL   | LENGTH(NULL)   |
|--------+---------------+------------+----------------|
| null   | null          | null       | null           |
+--------+---------------+------------+----------------+

Numeric Data Types

Numeric values ranged from -2147483648 to +2147483647 are recognized as integer with type name INTEGER. For others outside the range, LONG integer will be the data type after parsed.

Date and Time Data Types

The datetime types supported by the SQL plugin are DATE, TIME, TIMESTAMP, and INTERVAL, with date and time being used to represent temporal values. By default, the OpenSearch DSL uses date type as the only date and time related type as it contains all information about an absolute time point. To integrate with SQL language each of the types other than timestamp hold part of the temporal or timezone information. This information can be used to explicitly clarify the date and time types reflected in the datetime functions (see Functions for details), where some functions might have restrictions in the input argument type.

Date

Date represents the calendar date regardless of the time zone. A given date value represents a 24-hour period, or say a day, but this period varies in different timezones and might have flexible hours during Daylight Savings Time programs. Besides, the date type does not contain time information as well. The supported range is '1000-01-01' to '9999-12-31'.

Type Syntax Range
Date 'yyyy-MM-dd' '0001-01-01' to '9999-12-31'

Time

Time represents the time on the clock or watch with no regard for which timezone it might be related with. Time type data does not have date information.

Type Syntax Range
Time 'hh:mm:ss[.fraction]' '00:00:00.000000000' to '23:59:59.999999999'

Timestamp

A timestamp instance is an absolute instant independent of timezone or convention. For example, for a given point of time, if we set the timestamp of this time point into another timezone, the value should also be different accordingly. Besides, the storage of timestamp type is also different from the other types. The timestamp is converted from the current timezone to UTC for storage, and is converted back to the set timezone from UTC when retrieving.

Type Syntax Range
Timestamp 'yyyy-MM-dd hh:mm:ss[.fraction]' '0001-01-01 00:00:01.000000000' UTC to '9999-12-31 23:59:59.999999999'

Interval

Interval data type represents a temporal duration or a period. The syntax is as follows:

Type Syntax
Interval INTERVAL expr unit

The expr is any expression that can be iterated to a quantity value eventually, see Expressions for details. The unit represents the unit for interpreting the quantity, including MICROSECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER and YEAR. The INTERVAL keyword and the unit specifier are not case sensitive. Note that there are two classes of intervals. Year-week intervals can store years, quarters, months and weeks. Day-time intervals can store days, hours, minutes, seconds and microseconds. Year-week intervals are comparable only with another year-week intervals. These two types of intervals can only comparable with the same type of themselves.

Conversion between date and time types

Basically the date and time types except interval can be converted to each other, but might suffer some alteration of the value or some information loss, for example extracting the time value from a timestamp value, or convert a date value to a timestamp value and so forth. Here lists the summary of the conversion rules that SQL plugin supports for each of the types:

Conversion from DATE

  • Since the date value does not have any time information, conversion to Time type is not useful, and will always return a zero time value '00:00:00'.
  • Conversion to timestamp is to alternate both the time value and the timezone information, and it attaches the zero time value '00:00:00' and the session timezone (UTC by default) to the date. For example, the result to covert date '2020-08-17' to timestamp type with session timezone UTC is timestamp '2020-08-17 00:00:00' UTC.

Conversion from TIME

  • When time value is converted to any other datetime types, the date part of the new value is filled up with today's date, like with the CURDATE function. For example, a time value X converted to a timestamp would produce today's date at time X.

Conversion from TIMESTAMP

  • Conversion from timestamp is much more straightforward. To convert it to date is to extract the date value, and conversion to time is to extract the time value. For example, the result to convert timestamp '2020-08-17 14:09:00' UTC to date is date '2020-08-17', to time is '14:09:00'.

Conversion from string to date and time types

A string can also represent and be converted to date and time types (except to interval type). As long as the string value is of valid format required by the target date and time types, the conversion can happen implicitly or explicitly as follows:

os> SELECT
...  TIMESTAMP('2021-06-17 00:00:00') = '2021-06-17 00:00:00',
...  '2021-06-18' < DATE('2021-06-17'),
...  '10:20:00' <= TIME('11:00:00');
fetched rows / total rows = 1/1
+------------------------------------------------------------+-------------------------------------+----------------------------------+
| TIMESTAMP('2021-06-17 00:00:00') = '2021-06-17 00:00:00'   | '2021-06-18' < DATE('2021-06-17')   | '10:20:00' <= TIME('11:00:00')   |
|------------------------------------------------------------+-------------------------------------+----------------------------------|
| True                                                       | False                               | True                             |
+------------------------------------------------------------+-------------------------------------+----------------------------------+

Please, see more examples here.

Date formats

SQL plugin supports all named formats for OpenSearch date data type, custom formats and their combination. Please, refer to OpenSearch docs for format description. Plugin detects which type of data is stored in date field according to formats given and returns results in the corresponding SQL types. Given an index with the following mapping.

{
    "mappings" : {
        "properties" : {
            "date1" : {
                "type" : "date",
                "format": "yyyy-MM-dd"
            },
            "date2" : {
                "type" : "date",
                "format": "date_time_no_millis"
            },
            "date3" : {
                "type" : "date",
                "format": "hour_minute_second"
            },
            "date4" : {
                "type" : "date"
            },
            "date5" : {
                "type" : "date",
                "format": "yyyy-MM-dd || time"
            }
        }
    }
}

Querying such index will provide a response with schema block as shown below.

{
    "query" : "SELECT * from date_formats LIMIT 0;"
}
{
    "schema": [
        {
            "name": "date5",
            "type": "timestamp"
        },
        {
            "name": "date4",
            "type": "timestamp"
        },
        {
            "name": "date3",
            "type": "time"
        },
        {
            "name": "date2",
            "type": "timestamp"
        },
        {
            "name": "date1",
            "type": "date"
        },
    ],
    "datarows": [],
    "total": 0,
    "size": 0,
    "status": 200
}

String Data Types

A string is a sequence of characters enclosed in either single or double quotes. For example, both 'text' and "text" will be treated as string literal. To use quote characters in a string literal, you can use two quotes of the same type as the enclosing quotes or a backslash symbol (\):

os> SELECT 'hello', "world", '"hello"', "'world'", '''hello''', """world""", 'I\'m', 'I''m', "I\"m"
fetched rows / total rows = 1/1
+-----------+-----------+-------------+-------------+---------------+---------------+----------+----------+----------+
| 'hello'   | "world"   | '"hello"'   | "'world'"   | '''hello'''   | """world"""   | 'I\'m'   | 'I''m'   | "I\"m"   |
|-----------+-----------+-------------+-------------+---------------+---------------+----------+----------+----------|
| hello     | world     | "hello"     | 'world'     | 'hello'       | "world"       | I'm      | I'm      | I"m      |
+-----------+-----------+-------------+-------------+---------------+---------------+----------+----------+----------+

Boolean Data Types

A boolean can be represented by constant value TRUE or FALSE. Besides, certain string representation is also accepted by function with boolean input. For example, string 'true', 'TRUE', 'false', 'FALSE' are all valid representation and can be converted to boolean implicitly or explicitly:

os> SELECT
...  true, FALSE,
...  CAST('TRUE' AS boolean), CAST('false' AS boolean);
fetched rows / total rows = 1/1
+--------+---------+---------------------------+----------------------------+
| true   | FALSE   | CAST('TRUE' AS boolean)   | CAST('false' AS boolean)   |
|--------+---------+---------------------------+----------------------------|
| True   | False   | True                      | False                      |
+--------+---------+---------------------------+----------------------------+