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Elastic Docs ›Elasticsearch Guide [8.12] ›Search your data ›The search API

Retrieve selected fields from a search

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Retrieve selected fields from a search

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By default, each hit in the search response includes the document _source, which is the entire JSON object that was provided when indexing the document. There are two recommended methods to retrieve selected fields from a search query:

  • Use the fields option to extract the values of fields present in the index mapping
  • Use the _source option if you need to access the original data that was passed at index time

You can use both of these methods, though the fields option is preferred because it consults both the document data and index mappings. In some instances, you might want to use other methods of retrieving data.

The fields option

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To retrieve specific fields in the search response, use the fields parameter. Because it consults the index mappings, the fields parameter provides several advantages over referencing the _source directly. Specifically, the fields parameter:

Other mapping options are also respected, including ignore_above, ignore_malformed, and null_value.

The fields option returns values in the way that matches how Elasticsearch indexes them. For standard fields, this means that the fields option looks in _source to find the values, then parses and formats them using the mappings. Selected fields that can’t be found in _source are skipped.

Retrieve specific fields
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The following search request uses the fields parameter to retrieve values for the user.id field, all fields starting with http.response., and the @timestamp field.

Using object notation, you can pass a format argument to customize the format of returned date or geospatial values.

response = client.search(
  index: 'my-index-000001',
  body: {
    query: {
      match: {
        'user.id' => 'kimchy'
      }
    },
    fields: [
      'user.id',
      'http.response.*',
      {
        field: '@timestamp',
        format: 'epoch_millis'
      }
    ],
    _source: false
  }
)
puts response
POST my-index-000001/_search
{
  "query": {
    "match": {
      "user.id": "kimchy"
    }
  },
  "fields": [
    "user.id",
    "http.response.*",         
    {
      "field": "@timestamp",
      "format": "epoch_millis" 
    }
  ],
  "_source": false
}

Both full field names and wildcard patterns are accepted.

Use the format parameter to apply a custom format for the field’s values.

By default, document metadata fields like _id or _index are not returned when the requested fields option uses wildcard patterns like *. However, when explicitly requested using the field name, the _id, _routing, _ignored, _index and _version metadata fields can be retrieved.

Response always returns an array
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The fields response always returns an array of values for each field, even when there is a single value in the _source. This is because Elasticsearch has no dedicated array type, and any field could contain multiple values. The fields parameter also does not guarantee that array values are returned in a specific order. See the mapping documentation on arrays for more background.

The response includes values as a flat list in the fields section for each hit. Because the fields parameter doesn’t fetch entire objects, only leaf fields are returned.

{
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "0",
        "_score" : 1.0,
        "fields" : {
          "user.id" : [
            "kimchy"
          ],
          "@timestamp" : [
            "4098435132000"
          ],
          "http.response.bytes": [
            1070000
          ],
          "http.response.status_code": [
            200
          ]
        }
      }
    ]
  }
}
Retrieve nested fields
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Details

The fields response for nested fields is slightly different from that of regular object fields. While leaf values inside regular object fields are returned as a flat list, values inside nested fields are grouped to maintain the independence of each object inside the original nested array. For each entry inside a nested field array, values are again returned as a flat list unless there are other nested fields inside the parent nested object, in which case the same procedure is repeated again for the deeper nested fields.

Given the following mapping where user is a nested field, after indexing the following document and retrieving all fields under the user field:

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        group: {
          type: 'keyword'
        },
        user: {
          type: 'nested',
          properties: {
            first: {
              type: 'keyword'
            },
            last: {
              type: 'keyword'
            }
          }
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 1,
  refresh: true,
  body: {
    group: 'fans',
    user: [
      {
        first: 'John',
        last: 'Smith'
      },
      {
        first: 'Alice',
        last: 'White'
      }
    ]
  }
)
puts response

response = client.search(
  index: 'my-index-000001',
  body: {
    fields: [
      '*'
    ],
    _source: false
  }
)
puts response
PUT my-index-000001
{
  "mappings": {
    "properties": {
      "group" : { "type" : "keyword" },
      "user": {
        "type": "nested",
        "properties": {
          "first" : { "type" : "keyword" },
          "last" : { "type" : "keyword" }
        }
      }
    }
  }
}

PUT my-index-000001/_doc/1?refresh=true
{
  "group" : "fans",
  "user" : [
    {
      "first" : "John",
      "last" :  "Smith"
    },
    {
      "first" : "Alice",
      "last" :  "White"
    }
  ]
}

POST my-index-000001/_search
{
  "fields": ["*"],
  "_source": false
}

The response will group first and last name instead of returning them as a flat list.

{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1.0,
    "hits": [{
      "_index": "my-index-000001",
      "_id": "1",
      "_score": 1.0,
      "fields": {
        "group" : ["fans"],
        "user": [{
            "first": ["John"],
            "last": ["Smith"]
          },
          {
            "first": ["Alice"],
            "last": ["White"]
          }
        ]
      }
    }]
  }
}

Nested fields will be grouped by their nested paths, no matter the pattern used to retrieve them. For example, if you query only for the user.first field from the previous example:

response = client.search(
  index: 'my-index-000001',
  body: {
    fields: [
      'user.first'
    ],
    _source: false
  }
)
puts response
POST my-index-000001/_search
{
  "fields": ["user.first"],
  "_source": false
}

The response returns only the user’s first name, but still maintains the structure of the nested user array:

{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1.0,
    "hits": [{
      "_index": "my-index-000001",
      "_id": "1",
      "_score": 1.0,
      "fields": {
        "user": [{
            "first": ["John"]
          },
          {
            "first": ["Alice"]
          }
        ]
      }
    }]
  }
}

However, when the fields pattern targets the nested user field directly, no values will be returned because the pattern doesn’t match any leaf fields.

Retrieve unmapped fields
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Details

By default, the fields parameter returns only values of mapped fields. However, Elasticsearch allows storing fields in _source that are unmapped, such as setting dynamic field mapping to false or by using an object field with enabled: false. These options disable parsing and indexing of the object content.

To retrieve unmapped fields in an object from _source, use the include_unmapped option in the fields section:

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      enabled: false
    }
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 1,
  refresh: true,
  body: {
    user_id: 'kimchy',
    session_data: {
      object: {
        some_field: 'some_value'
      }
    }
  }
)
puts response

response = client.search(
  index: 'my-index-000001',
  body: {
    fields: [
      'user_id',
      {
        field: 'session_data.object.*',
        include_unmapped: true
      }
    ],
    _source: false
  }
)
puts response
PUT my-index-000001
{
  "mappings": {
    "enabled": false 
  }
}

PUT my-index-000001/_doc/1?refresh=true
{
  "user_id": "kimchy",
  "session_data": {
     "object": {
       "some_field": "some_value"
     }
   }
}

POST my-index-000001/_search
{
  "fields": [
    "user_id",
    {
      "field": "session_data.object.*",
      "include_unmapped" : true 
    }
  ],
  "_source": false
}

Disable all mappings.

Include unmapped fields matching this field pattern.

The response will contain field results under the session_data.object.* path, even if the fields are unmapped. The user_id field is also unmapped, but it won’t be included in the response because include_unmapped isn’t set to true for that field pattern.

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "1",
        "_score" : 1.0,
        "fields" : {
          "session_data.object.some_field": [
            "some_value"
          ]
        }
      }
    ]
  }
}
Ignored field values
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Details

The fields section of the response only returns values that were valid when indexed. If your search request asks for values from a field that ignored certain values because they were malformed or too large these values are returned separately in an ignored_field_values section.

In this example we index a document that has a value which is ignored and not added to the index so is shown separately in search results:

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        "my-small": {
          type: 'keyword',
          ignore_above: 2
        },
        "my-large": {
          type: 'keyword'
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 1,
  refresh: true,
  body: {
    "my-small": [
      'ok',
      'bad'
    ],
    "my-large": 'ok content'
  }
)
puts response

response = client.search(
  index: 'my-index-000001',
  body: {
    fields: [
      'my-*'
    ],
    _source: false
  }
)
puts response
PUT my-index-000001
{
  "mappings": {
    "properties": {
      "my-small" : { "type" : "keyword", "ignore_above": 2 }, 
      "my-large" : { "type" : "keyword" }
    }
  }
}

PUT my-index-000001/_doc/1?refresh=true
{
  "my-small": ["ok", "bad"], 
  "my-large": "ok content"
}

POST my-index-000001/_search
{
  "fields": ["my-*"],
  "_source": false
}

This field has a size restriction

This document field has a value that exceeds the size restriction so is ignored and not indexed

The response will contain ignored field values under the ignored_field_values path. These values are retrieved from the document’s original JSON source and are raw so will not be formatted or treated in any way, unlike the successfully indexed fields which are returned in the fields section.

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "1",
        "_score" : 1.0,
        "_ignored" : [ "my-small"],
        "fields" : {
          "my-large": [
            "ok content"
          ],
          "my-small": [
            "ok"
          ]
        },
        "ignored_field_values" : {
          "my-small": [
            "bad"
          ]
        }
      }
    ]
  }
}

The _source option

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You can use the _source parameter to select what fields of the source are returned. This is called source filtering.

The following search API request sets the _source request body parameter to false. The document source is not included in the response.

response = client.search(
  body: {
    _source: false,
    query: {
      match: {
        'user.id' => 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "_source": false,
  "query": {
    "match": {
      "user.id": "kimchy"
    }
  }
}

To return only a subset of source fields, specify a wildcard (*) pattern in the _source parameter. The following search API request returns the source for only the obj field and its properties.

response = client.search(
  body: {
    _source: 'obj.*',
    query: {
      match: {
        'user.id' => 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "_source": "obj.*",
  "query": {
    "match": {
      "user.id": "kimchy"
    }
  }
}

You can also specify an array of wildcard patterns in the _source field. The following search API request returns the source for only the obj1 and obj2 fields and their properties.

response = client.search(
  body: {
    _source: [
      'obj1.*',
      'obj2.*'
    ],
    query: {
      match: {
        'user.id' => 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "_source": [ "obj1.*", "obj2.*" ],
  "query": {
    "match": {
      "user.id": "kimchy"
    }
  }
}

For finer control, you can specify an object containing arrays of includes and excludes patterns in the _source parameter.

If the includes property is specified, only source fields that match one of its patterns are returned. You can exclude fields from this subset using the excludes property.

If the includes property is not specified, the entire document source is returned, excluding any fields that match a pattern in the excludes property.

The following search API request returns the source for only the obj1 and obj2 fields and their properties, excluding any child description fields.

response = client.search(
  body: {
    _source: {
      includes: [
        'obj1.*',
        'obj2.*'
      ],
      excludes: [
        '*.description'
      ]
    },
    query: {
      term: {
        'user.id' => 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "_source": {
    "includes": [ "obj1.*", "obj2.*" ],
    "excludes": [ "*.description" ]
  },
  "query": {
    "term": {
      "user.id": "kimchy"
    }
  }
}

Other methods of retrieving data

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Using fields is typically better

These options are usually not required. Using the fields option is typically the better choice, unless you absolutely need to force loading a stored or docvalue_fields.

A document’s _source is stored as a single field in Lucene. This structure means that the whole _source object must be loaded and parsed even if you’re only requesting part of it. To avoid this limitation, you can try other options for loading fields:

  • Use the docvalue_fields parameter to get values for selected fields. This can be a good choice when returning a fairly small number of fields that support doc values, such as keywords and dates.
  • Use the stored_fields parameter to get the values for specific stored fields (fields that use the store mapping option).

Elasticsearch always attempts to load values from _source. This behavior has the same implications of source filtering where Elasticsearch needs to load and parse the entire _source to retrieve just one field.

Doc value fields
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You can use the docvalue_fields parameter to return doc values for one or more fields in the search response.

Doc values store the same values as the _source but in an on-disk, column-based structure that’s optimized for sorting and aggregations. Since each field is stored separately, Elasticsearch only reads the field values that were requested and can avoid loading the whole document _source.

Doc values are stored for supported fields by default. However, doc values are not supported for text or text_annotated fields.

The following search request uses the docvalue_fields parameter to retrieve doc values for the user.id field, all fields starting with http.response., and the @timestamp field:

response = client.search(
  index: 'my-index-000001',
  body: {
    query: {
      match: {
        'user.id' => 'kimchy'
      }
    },
    docvalue_fields: [
      'user.id',
      'http.response.*',
      {
        field: 'date',
        format: 'epoch_millis'
      }
    ]
  }
)
puts response
GET my-index-000001/_search
{
  "query": {
    "match": {
      "user.id": "kimchy"
    }
  },
  "docvalue_fields": [
    "user.id",
    "http.response.*", 
    {
      "field": "date",
      "format": "epoch_millis" 
    }
  ]
}

Both full field names and wildcard patterns are accepted.

Using object notation, you can pass a format parameter to apply a custom format for the field’s doc values. Date fields support a date format. Numeric fields support a DecimalFormat pattern. Other field datatypes do not support the format parameter.

You cannot use the docvalue_fields parameter to retrieve doc values for nested objects. If you specify a nested object, the search returns an empty array ([ ]) for the field. To access nested fields, use the inner_hits parameter’s docvalue_fields property.

Stored fields
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It’s also possible to store an individual field’s values by using the store mapping option. You can use the stored_fields parameter to include these stored values in the search response.

The stored_fields parameter is for fields that are explicitly marked as stored in the mapping, which is off by default and generally not recommended. Use source filtering instead to select subsets of the original source document to be returned.

Allows to selectively load specific stored fields for each document represented by a search hit.

response = client.search(
  body: {
    stored_fields: [
      'user',
      'postDate'
    ],
    query: {
      term: {
        user: 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "stored_fields" : ["user", "postDate"],
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

* can be used to load all stored fields from the document.

An empty array will cause only the _id and _type for each hit to be returned, for example:

response = client.search(
  body: {
    stored_fields: [],
    query: {
      term: {
        user: 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "stored_fields" : [],
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

If the requested fields are not stored (store mapping set to false), they will be ignored.

Stored field values fetched from the document itself are always returned as an array. On the contrary, metadata fields like _routing are never returned as an array.

Also only leaf fields can be returned via the stored_fields option. If an object field is specified, it will be ignored.

On its own, stored_fields cannot be used to load fields in nested objects — if a field contains a nested object in its path, then no data will be returned for that stored field. To access nested fields, stored_fields must be used within an inner_hits block.

Disable stored fields
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To disable the stored fields (and metadata fields) entirely use: _none_:

response = client.search(
  body: {
    stored_fields: '_none_',
    query: {
      term: {
        user: 'kimchy'
      }
    }
  }
)
puts response
GET /_search
{
  "stored_fields": "_none_",
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

_source and version parameters cannot be activated if _none_ is used.

Script fields
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You can use the script_fields parameter to retrieve a script evaluation (based on different fields) for each hit. For example:

response = client.search(
  body: {
    query: {
      match_all: {}
    },
    script_fields: {
      "test1": {
        script: {
          lang: 'painless',
          source: "doc['price'].value * 2"
        }
      },
      "test2": {
        script: {
          lang: 'painless',
          source: "doc['price'].value * params.factor",
          params: {
            factor: 2
          }
        }
      }
    }
  }
)
puts response
GET /_search
{
  "query": {
    "match_all": {}
  },
  "script_fields": {
    "test1": {
      "script": {
        "lang": "painless",
        "source": "doc['price'].value * 2"
      }
    },
    "test2": {
      "script": {
        "lang": "painless",
        "source": "doc['price'].value * params.factor",
        "params": {
          "factor": 2.0
        }
      }
    }
  }
}

Script fields can work on fields that are not stored (price in the above case), and allow to return custom values to be returned (the evaluated value of the script).

Script fields can also access the actual _source document and extract specific elements to be returned from it by using params['_source']. Here is an example:

response = client.search(
  body: {
    query: {
      match_all: {}
    },
    script_fields: {
      "test1": {
        script: "params['_source']['message']"
      }
    }
  }
)
puts response
GET /_search
{
  "query": {
    "match_all": {}
  },
  "script_fields": {
    "test1": {
      "script": "params['_source']['message']"
    }
  }
}

Note the _source keyword here to navigate the json-like model.

It’s important to understand the difference between doc['my_field'].value and params['_source']['my_field']. The first, using the doc keyword, will cause the terms for that field to be loaded to memory (cached), which will result in faster execution, but more memory consumption. Also, the doc[...] notation only allows for simple valued fields (you can’t return a json object from it) and makes sense only for non-analyzed or single term based fields. However, using doc is still the recommended way to access values from the document, if at all possible, because _source must be loaded and parsed every time it’s used. Using _source is very slow.

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