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Elastic Docs ›Elasticsearch Guide [8.0] ›Mapping ›Field data types

Numeric field types

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Numeric field types

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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.

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:

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        number_of_bytes: {
          type: 'integer'
        },
        time_in_seconds: {
          type: 'float'
        },
        price: {
          type: 'scaled_float',
          scaling_factor: 100
        }
      }
    }
  }
)
puts response
res, err := es.Indices.Create(
	"my-index-000001",
	es.Indices.Create.WithBody(strings.NewReader(`{
	  "mappings": {
	    "properties": {
	      "number_of_bytes": {
	        "type": "integer"
	      },
	      "time_in_seconds": {
	        "type": "float"
	      },
	      "price": {
	        "type": "scaled_float",
	        "scaling_factor": 100
	      }
	    }
	  }
	}`)),
)
fmt.Println(res, err)
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?

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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 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 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. 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 searchable? Accepts true (default) and false.
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

[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. (Optional, Boolean)

For internal use by Elastic only.

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

[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. (Optional, string)

For internal use by Elastic only.

Marks the field as a time series metric. The value is the metric type. Defaults to null (Not a time series metric).

For numeric fields, this parameter accepts gauge and counter. You can’t update this parameter for existing fields.

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

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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.

scaled_float saturation

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scaled_float is stored as a single long value, which is the product of multiplying the original value by the scaling factor. If the multiplication results in a value that is outside the range of a long, the value is saturated to the minimum or maximum value of a long. For example, if the scaling factor is 100 and the value is 92233720368547758.08, the expected value is 9223372036854775808. However, the value that is stored is 9223372036854775807, the maximum value for a long.

This can lead to unexpected results with range queries when the scaling factor or provided float value are exceptionally large.

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