Numeric field types | Elasticsearch Guide

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Numeric field typesedit

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.
`unsigned_long`	An unsigned 64-bit integer with a minimum value of 0 and a maximum value of `2⁶⁴-1`.

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

PUT my-index-000001
{
  "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?edit

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	Example precision loss
`double`	`2^-1074`	`(2-2^-52)·2¹⁰²³`	`53` / `15.95`	`1.2345678912345678`→ `1.234567891234568`
`float`	`2^-149`	`(2-2^-23)·2¹²⁷`	`24` / `7.22`	`1.23456789`→ `1.2345679`
`half_float`	`2^-24`	`65504`	`11` / `3.31`	`1.2345`→ `1.234375`

Mapping numeric identifiers

Not all numeric data should be mapped as a numeric field data type. 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 data type.

Parameters for numeric fieldsedit

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. Not applicable for unsigned_long. Note that this cannot be set if the script parameter is used.

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. Note that this cannot be set if the script parameter is used.

index

Should the field be quickly searchable? Accepts true (default) and false. Numeric fields that only have doc_values enabled can also be queried, albeit slower.

meta

Metadata about the field.

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. Note that this cannot be set if the script parameter is used.

on_script_error

Defines what to do if the script defined by the script parameter throws an error at indexing time. Accepts fail (default), which will cause the entire document to be rejected, and continue, which will register the field in the document’s _ignored metadata field and continue indexing. This parameter can only be set if the script field is also set.

script

If this parameter is set, then the field will index values generated by this script, rather than reading the values directly from the source. If a value is set for this field on the input document, then the document will be rejected with an error. Scripts are in the same format as their runtime equivalent. Scripts can only be configured on long and double field types.

store

Whether the field value should be stored and retrievable separately from the _source field. Accepts true or false (default).

time_series_dimension

(Optional, Boolean)

Marks the field as a time series dimension. Defaults to false.

The index.mapping.dimension_fields.limit index setting limits the number of dimensions in an index.

Dimension fields have the following constraints:

The doc_values and index mapping parameters must be true.
Field values cannot be an array or multi-value.

Of the numeric field types, only byte, short, integer, long, and unsigned_long fields support this parameter.

A numeric field can’t be both a time series dimension and a time series metric.

time_series_metric

(Optional, string) Marks the field as a time series metric. The value is the metric type. You can’t update this parameter for existing fields.

Valid time_series_metric values for numeric fields

counter: A cumulative metric that only monotonically increases or resets to 0 (zero). For example, a count of errors or completed tasks.
gauge: A metric that represents a single numeric that can arbitrarily increase or decrease. For example, a temperature or available disk space.
null (Default): Not a time series metric.

For a numeric time series metric, the doc_values parameter must be true. A numeric field can’t be both a time series dimension and a time series metric.

Parameters for `scaled_float`edit

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.

Synthetic `_source`edit

Synthetic _source is Generally Available only for TSDB indices (indices that have index.mode set to time_series). For other indices synthetic _source is in technical preview. Features in technical preview may be changed or removed in a future release. Elastic will apply best effort to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

All numeric fields except unsigned_long support synthetic _source in their default configuration. Synthetic _source cannot be used together with ignore_malformed, copy_to, or with doc_values disabled.

Synthetic source always sorts numeric fields. For example:

PUT idx
{
  "mappings": {
    "_source": { "mode": "synthetic" },
    "properties": {
      "long": { "type": "long" }
    }
  }
}
PUT idx/_doc/1
{
  "long": [0, 0, -123466, 87612]
}

Will become:

{
  "long": [-123466, 0, 0, 87612]
}

Scaled floats will always apply their scaling factor so:

PUT idx
{
  "mappings": {
    "_source": { "mode": "synthetic" },
    "properties": {
      "f": { "type": "scaled_float", "scaling_factor": 0.01 }
    }
  }
}
PUT idx/_doc/1
{
  "f": 123
}