OpenSearch/docs/reference/query-dsl/missing-query.asciidoc

[[query-dsl-missing-query]]
=== Missing Query

Returns documents that have only `null` values or no value in the original field:

[source,js]
--------------------------------------------------
{
    "constant_score" : {
        "filter" : {
            "missing" : { "field" : "user" }
        }
    }
}
--------------------------------------------------

For instance, the following docs would match the above filter:

[source,js]
--------------------------------------------------
{ "user": null }
{ "user": [] } <1>
{ "user": [null] } <2>
{ "foo":  "bar" } <3>
--------------------------------------------------
<1> This field has no values.
<2> This field has no non-`null` values.
<3> The `user` field is missing completely.

These documents would *not* match the above filter:

[source,js]
--------------------------------------------------
{ "user": "jane" }
{ "user": "" } <1>
{ "user": "-" } <2>
{ "user": ["jane"] }
{ "user": ["jane", null ] } <3>
--------------------------------------------------
<1> An empty string is a non-`null` value.
<2> Even though the `standard` analyzer would emit zero tokens, the original field is non-`null`.
<3> This field has one non-`null` value.

[float]
==== `null_value` mapping

If the field mapping includes a `null_value` (see <<mapping-core-types>>) then explicit `null` values
are replaced with the specified `null_value`.  For instance, if the `user` field were mapped
as follows:

[source,js]
--------------------------------------------------
  "user": {
    "type": "string",
    "null_value": "_null_"
  }
--------------------------------------------------

then explicit `null` values would be indexed as the string `_null_`, and the
the following docs would *not* match the `missing` filter:

[source,js]
--------------------------------------------------
{ "user": null }
{ "user": [null] }
--------------------------------------------------

However, these docs--without explicit `null` values--would still have
no values in the `user` field and thus would match the `missing` filter:

[source,js]
--------------------------------------------------
{ "user": [] }
{ "foo": "bar" }
--------------------------------------------------

[float]
===== `existence` and `null_value` parameters

When the field being queried has a `null_value` mapping, then the behaviour of
the `missing` filter can be altered with the `existence` and `null_value`
parameters:

[source,js]
--------------------------------------------------
{
    "constant_score" : {
        "filter" : {
            "missing" : {
                "field" : "user",
                "existence" : true,
                "null_value" : false
            }
        }
    }
}
--------------------------------------------------


`existence`::
+
--
When the `existence` parameter is set to `true` (the default), the missing
filter will include documents where the field has *no* values, ie:

[source,js]
--------------------------------------------------
{ "user": [] }
{ "foo": "bar" }
--------------------------------------------------

When set to `false`, these documents will not be included.
--

`null_value`::
+
--
When the `null_value` parameter is set to `true`, the missing
filter will include documents where the field contains a `null` value, ie:

[source,js]
--------------------------------------------------
{ "user": null }
{ "user": [null] }
{ "user": ["jane",null] } <1>
--------------------------------------------------
<1> Matches because the field contains a `null` value, even though it also contains a non-`null` value.

When set to `false` (the default), these documents will not be included.
--

NOTE: Either `existence` or `null_value` or both must be set to `true`.