Numeric datatypesedit

The following numeric types are supported:


A signed 64-bit integer with a minimum value of -263 and a maximum value of 263-1.


A signed 32-bit integer with a minimum value of -231 and a maximum value of 231-1.


A signed 16-bit integer with a minimum value of -32,768 and a maximum value of 32,767.


A signed 8-bit integer with a minimum value of -128 and a maximum value of 127.


A double-precision 64-bit IEEE 754 floating point.


A single-precision 32-bit IEEE 754 floating point.


A half-precision 16-bit IEEE 754 floating point.


A floating point that is backed by a long and a fixed scaling factor.

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

PUT my_index
  "mappings": {
    "my_type": {
      "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 given that storage is optimized based on the actual values that are stored, 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




53 / 15.95




24 / 7.22




11 / 3.31

Parameters for numeric fieldsedit

The following parameters are accepted by numeric types:


Try to convert strings to numbers and truncate fractions for integers. Accepts true (default) and false.


Mapping field-level query time boosting. Accepts a floating point number, defaults to 1.0.


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.


If true, malformed numbers are ignored. If false (default), malformed numbers throw an exception and reject the whole document.


Should the field be searchable? Accepts true (default) and false.


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.


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

Parameters for scaled_floatedit

scaled_float accepts an additional parameter:


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.