Data Types


Elasticsearch type	Elasticsearch SQL type	SQL type	SQL precision
Core types
`null`	`null`	NULL	0
`boolean`	`boolean`	BOOLEAN	1
`byte`	`byte`	TINYINT	3
`short`	`short`	SMALLINT	5
`integer`	`integer`	INTEGER	10
`long`	`long`	BIGINT	19
`unsigned_long`	`[preview] unsigned_long`	BIGINT	20
`double`	`double`	DOUBLE	15
`float`	`float`	REAL	7
`half_float`	`half_float`	FLOAT	3
`scaled_float`	`scaled_float`	DOUBLE	15
keyword type family	`keyword`	VARCHAR	32,766
`text`	`text`	VARCHAR	2,147,483,647
`binary`	`binary`	VARBINARY	2,147,483,647
`date`	`datetime`	TIMESTAMP	29
`ip`	`ip`	VARCHAR	39
`version`	`version`	VARCHAR	32,766
Complex types
`object`	`object`	STRUCT	0
`nested`	`nested`	STRUCT	0
Unsupported types
types not mentioned above	`unsupported`	OTHER	0

Note

Most of Elasticsearch data types are available in Elasticsearch SQL, as indicated above. As one can see, all of Elasticsearch data types are mapped to the data type with the same name in Elasticsearch SQL, with the exception of date data type which is mapped to datetime* in Elasticsearch SQL. This is to avoid confusion with the ANSI SQL types *DATE (date only) and TIME (time only), which are also supported by Elasticsearch SQL in queries (with the use of CAST/CONVERT), but don’t correspond to an actual mapping in Elasticsearch (see the table below).

Obviously, not all types in Elasticsearch have an equivalent in SQL and vice-versa hence why, Elasticsearch SQL uses the data type particularities of the former over the latter as ultimately Elasticsearch is the backing store.

In addition to the types above, Elasticsearch SQL also supports at runtime SQL-specific types that do not have an equivalent in Elasticsearch. Such types cannot be loaded from Elasticsearch (as it does not know about them) however can be used inside Elasticsearch SQL in queries or their results.

The table below indicates these types:


SQL type	SQL precision
`date`	29
`time`	18
`interval_year`	7
`interval_month`	7
`interval_day`	23
`interval_hour`	23
`interval_minute`	23
`interval_second`	23
`interval_year_to_month`	7
`interval_day_to_hour`	23
`interval_day_to_minute`	23
`interval_day_to_second`	23
`interval_hour_to_minute`	23
`interval_hour_to_second`	23
`interval_minute_to_second`	23
`geo_point`	52
`geo_shape`	2,147,483,647
`shape`	2,147,483,647

SQL and multi-fields

A core concept in Elasticsearch is that of an analyzed field, that is a full-text value that is interpreted in order to be effectively indexed. These fields are of type text and are not used for sorting or aggregations as their actual value depends on the analyzer used hence why Elasticsearch also offers the keyword type for storing the exact value.

In most case, and the default actually, is to use both types for strings which Elasticsearch supports through multi-fields, that is the ability to index the same string in multiple ways; for example index it both as text for search but also as keyword for sorting and aggregations.

As SQL requires exact values, when encountering a text field Elasticsearch SQL will search for an exact multi-field that it can use for comparisons, sorting and aggregations. To do that, it will search for the first keyword that it can find that is not normalized and use that as the original field exact value.

Consider the following string mapping:

		{
  "first_name": {
    "type": "text",
    "fields": {
      "raw": {
        "type": "keyword"
      }
    }
  }
}

	

The following SQL query:

SELECT first_name FROM index WHERE first_name = 'John'

is identical to:

SELECT first_name FROM index WHERE first_name.raw = 'John'

as Elasticsearch SQL automatically picks up the raw multi-field from raw for exact matching.