133 lines
3.6 KiB
Plaintext
133 lines
3.6 KiB
Plaintext
[[query-dsl-missing-query]]
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=== Missing Query
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Returns documents that have only `null` values or no value in the original field:
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[source,js]
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--------------------------------------------------
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{
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"constant_score" : {
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"filter" : {
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"missing" : { "field" : "user" }
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}
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}
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}
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--------------------------------------------------
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For instance, the following docs would match the above filter:
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[source,js]
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--------------------------------------------------
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{ "user": null }
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{ "user": [] } <1>
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{ "user": [null] } <2>
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{ "foo": "bar" } <3>
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--------------------------------------------------
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<1> This field has no values.
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<2> This field has no non-`null` values.
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<3> The `user` field is missing completely.
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These documents would *not* match the above filter:
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[source,js]
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--------------------------------------------------
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{ "user": "jane" }
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{ "user": "" } <1>
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{ "user": "-" } <2>
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{ "user": ["jane"] }
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{ "user": ["jane", null ] } <3>
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--------------------------------------------------
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<1> An empty string is a non-`null` value.
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<2> Even though the `standard` analyzer would emit zero tokens, the original field is non-`null`.
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<3> This field has one non-`null` value.
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[float]
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==== `null_value` mapping
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If the field mapping includes a `null_value` (see <<mapping-core-types>>) then explicit `null` values
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are replaced with the specified `null_value`. For instance, if the `user` field were mapped
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as follows:
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[source,js]
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--------------------------------------------------
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"user": {
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"type": "string",
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"null_value": "_null_"
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}
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--------------------------------------------------
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then explicit `null` values would be indexed as the string `_null_`, and the
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the following docs would *not* match the `missing` filter:
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[source,js]
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--------------------------------------------------
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{ "user": null }
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{ "user": [null] }
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--------------------------------------------------
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However, these docs--without explicit `null` values--would still have
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no values in the `user` field and thus would match the `missing` filter:
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[source,js]
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--------------------------------------------------
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{ "user": [] }
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{ "foo": "bar" }
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--------------------------------------------------
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[float]
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===== `existence` and `null_value` parameters
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When the field being queried has a `null_value` mapping, then the behaviour of
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the `missing` filter can be altered with the `existence` and `null_value`
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parameters:
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[source,js]
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--------------------------------------------------
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{
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"constant_score" : {
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"filter" : {
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"missing" : {
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"field" : "user",
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"existence" : true,
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"null_value" : false
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}
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}
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}
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}
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--------------------------------------------------
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`existence`::
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+
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--
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When the `existence` parameter is set to `true` (the default), the missing
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filter will include documents where the field has *no* values, ie:
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[source,js]
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--------------------------------------------------
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{ "user": [] }
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{ "foo": "bar" }
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--------------------------------------------------
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When set to `false`, these documents will not be included.
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--
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`null_value`::
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+
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--
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When the `null_value` parameter is set to `true`, the missing
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filter will include documents where the field contains a `null` value, ie:
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[source,js]
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--------------------------------------------------
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{ "user": null }
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{ "user": [null] }
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{ "user": ["jane",null] } <1>
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--------------------------------------------------
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<1> Matches because the field contains a `null` value, even though it also contains a non-`null` value.
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When set to `false` (the default), these documents will not be included.
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--
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NOTE: Either `existence` or `null_value` or both must be set to `true`.
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