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Numeric datatypes

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Numeric datatypes

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The following numeric types are supported:

`long`	A signed 64-bit integer with a minimum value of `-2⁶³` and a maximum value of `2⁶³-1`.
`integer`	A signed 32-bit integer with a minimum value of `-2³¹` and a maximum value of `2³¹-1`.
`short`	A signed 16-bit integer with a minimum value of `-32,768` and a maximum value of `32,767`.
`byte`	A signed 8-bit integer with a minimum value of `-128` and a maximum value of `127`.
`double`	A double-precision 64-bit IEEE 754 floating point number, restricted to finite values.
`float`	A single-precision 32-bit IEEE 754 floating point number, restricted to finite values.
`half_float`	A half-precision 16-bit IEEE 754 floating point number, restricted to finite values.
`scaled_float`	A floating point number that is backed by a `long`, scaled by a fixed `double` scaling factor.

Below is an example of configuring a mapping with numeric fields:

PUT my_index
{
  "mappings": {
    "properties": {
      "number_of_bytes": {
        "type": "integer"
      },
      "time_in_seconds": {
        "type": "float"
      },
      "price": {
        "type": "scaled_float",
        "scaling_factor": 100
      }
    }
  }
}

The double, float and half_float types consider that -0.0 and +0.0 are different values. As a consequence, doing a term query on -0.0 will not match +0.0 and vice-versa. Same is true for range queries: if the upper bound is -0.0 then +0.0 will not match, and if the lower bound is +0.0 then -0.0 will not match.

Which type should I use?

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As far as integer types (byte, short, integer and long) are concerned, you should pick the smallest type which is enough for your use-case. This will help indexing and searching be more efficient. Note however that storage is optimized based on the actual values that are stored, so picking one type over another one will have no impact on storage requirements.

For floating-point types, it is often more efficient to store floating-point data into an integer using a scaling factor, which is what the scaled_float type does under the hood. For instance, a price field could be stored in a scaled_float with a scaling_factor of 100. All APIs would work as if the field was stored as a double, but under the hood Elasticsearch would be working with the number of cents, price*100, which is an integer. This is mostly helpful to save disk space since integers are way easier to compress than floating points. scaled_float is also fine to use in order to trade accuracy for disk space. For instance imagine that you are tracking cpu utilization as a number between 0 and 1. It usually does not matter much whether cpu utilization is 12.7% or 13%, so you could use a scaled_float with a scaling_factor of 100 in order to round cpu utilization to the closest percent in order to save space.

If scaled_float is not a good fit, then you should pick the smallest type that is enough for the use-case among the floating-point types: double, float and half_float. Here is a table that compares these types in order to help make a decision.

Type	Minimum value	Maximum value	Significant bits / digits
`double`	`2^-1074`	`(2-2^-52)·2¹⁰²³`	`53` / `15.95`
`float`	`2^-149`	`(2-2^-23)·2¹²⁷`	`24` / `7.22`
`half_float`	`2^-24`	`65504`	`11` / `3.31`

Mapping numeric identifiers

Not all numeric data should be mapped as a numeric field datatype. Elasticsearch optimizes numeric fields, such as integer or long, for range queries. However, keyword fields are better for term and other term-level queries.

Identifiers, such as an ISBN or a product ID, are rarely used in range queries. However, they are often retrieved using term-level queries.

Consider mapping a numeric identifier as a keyword if:

You don’t plan to search for the identifier data using range queries.
Fast retrieval is important. term query searches on keyword fields are often faster than term searches on numeric fields.

If you’re unsure which to use, you can use a multi-field to map the data as both a keyword and a numeric datatype.

Parameters for numeric fields

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The following parameters are accepted by numeric types:

`coerce`	Try to convert strings to numbers and truncate fractions for integers. Accepts `true` (default) and `false`.
`boost`	Mapping field-level query time boosting. Accepts a floating point number, defaults to `1.0`.
`doc_values`	Should the field be stored on disk in a column-stride fashion, so that it can later be used for sorting, aggregations, or scripting? Accepts `true` (default) or `false`.
`ignore_malformed`	If `true`, malformed numbers are ignored. If `false` (default), malformed numbers throw an exception and reject the whole document.
`index`	Should the field be searchable? Accepts `true` (default) and `false`.
`null_value`	Accepts a numeric value of the same `type` as the field which is substituted for any explicit `null` values. Defaults to `null`, which means the field is treated as missing.
`store`	Whether the field value should be stored and retrievable separately from the `_source` field. Accepts `true` or `false` (default).

Parameters for `scaled_float`

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scaled_float accepts an additional parameter:

scaling_factor

The scaling factor to use when encoding values. Values will be multiplied by this factor at index time and rounded to the closest long value. For instance, a scaled_float with a scaling_factor of 10 would internally store 2.34 as 23 and all search-time operations (queries, aggregations, sorting) will behave as if the document had a value of 2.3. High values of scaling_factor improve accuracy but also increase space requirements. This parameter is required.

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Numeric datatypes

Numeric datatypes

Which type should I use?

Parameters for numeric fields

Parameters for scaled_float

Parameters for `scaled_float`