Sort search results

Allows you to add one or more sorts on specific fields. Each sort can be reversed as well. The sort is defined on a per field level, with special field name for _score to sort by score, and _doc to sort by index order.

Assuming the following index mapping:

PUT /my-index-000001
{
  "mappings": {
    "properties": {
      "post_date": { "type": "date" },
      "user": {
        "type": "keyword"
      },
      "name": {
        "type": "keyword"
      },
      "age": { "type": "integer" }
    }
  }
}

GET /my-index-000001/_search
{
  "sort" : [
    { "post_date" : {"order" : "asc"}},
    "user",
    { "name" : "desc" },
    { "age" : "desc" },
    "_score"
  ],
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

The sort values for each document returned are also returned as part of the response.

The order option can have the following values:

The order defaults to desc when sorting on the _score, and defaults to asc when sorting on anything else.

Elasticsearch supports sorting by array or multi-valued fields. The mode option controls what array value is picked for sorting the document it belongs to. The mode option can have the following values:

The default sort mode in the ascending sort order is min — the lowest value is picked. The default sort mode in the descending order is max — the highest value is picked.

In the example below the field price has multiple prices per document. In this case the result hits will be sorted by price ascending based on the average price per document.

PUT /my-index-000001/_doc/1?refresh
{
   "product": "chocolate",
   "price": [20, 4]
}

POST /_search
{
   "query" : {
      "term" : { "product" : "chocolate" }
   },
   "sort" : [
      {"price" : {"order" : "asc", "mode" : "avg"}}
   ]
}

For numeric fields it is also possible to cast the values from one type to another using the numeric_type option. This option accepts the following values: ["double", "long", "date", "date_nanos"] and can be useful for cross-index search if the sort field is mapped differently on some indices.

Consider for instance these two indices:

PUT /index_double
{
  "mappings": {
    "properties": {
      "field": { "type": "double" }
    }
  }
}

PUT /index_long
{
  "mappings": {
    "properties": {
      "field": { "type": "long" }
    }
  }
}

Since field is mapped as a double in the first index and as a long in the second index, it is not possible to use this field to sort requests that query both indices by default. However you can force the type to one or the other with the numeric_type option in order to force a specific type for all indices:

POST /index_long,index_double/_search
{
   "sort" : [
      {
        "field" : {
            "numeric_type" : "double"
        }
      }
   ]
}

In the example above, values for the index_long index are casted to a double in order to be compatible with the values produced by the index_double index. It is also possible to transform a floating point field into a long but note that in this case floating points are replaced by the largest value that is less than or equal (greater than or equal if the value is negative) to the argument and is equal to a mathematical integer.

This option can also be used to convert a date field that uses millisecond resolution to a date_nanos field with nanosecond resolution. Consider for instance these two indices:

PUT /index_double
{
  "mappings": {
    "properties": {
      "field": { "type": "date" }
    }
  }
}

PUT /index_long
{
  "mappings": {
    "properties": {
      "field": { "type": "date_nanos" }
    }
  }
}

Values in these indices are stored with different resolutions so sorting on these fields will always sort the date before the date_nanos (ascending order). With the numeric_type type option it is possible to set a single resolution for the sort, setting to date will convert the date_nanos to the millisecond resolution while date_nanos will convert the values in the date field to the nanoseconds resolution:

POST /index_long,index_double/_search
{
   "sort" : [
      {
        "field" : {
            "numeric_type" : "date_nanos"
        }
      }
   ]
}

Elasticsearch also supports sorting by fields that are inside one or more nested objects. The sorting by nested field support has a nested sort option with the following properties:

In the below example offer is a field of type nested. The nested path needs to be specified; otherwise, Elasticsearch doesn’t know on what nested level sort values need to be captured.

POST /_search
{
   "query" : {
      "term" : { "product" : "chocolate" }
   },
   "sort" : [
       {
          "offer.price" : {
             "mode" :  "avg",
             "order" : "asc",
             "nested": {
                "path": "offer",
                "filter": {
                   "term" : { "offer.color" : "blue" }
                }
             }
          }
       }
    ]
}

In the below example parent and child fields are of type nested. The nested_path needs to be specified at each level; otherwise, Elasticsearch doesn’t know on what nested level sort values need to be captured.

POST /_search
{
   "query": {
      "nested": {
         "path": "parent",
         "query": {
            "bool": {
                "must": {"range": {"parent.age": {"gte": 21}}},
                "filter": {
                    "nested": {
                        "path": "parent.child",
                        "query": {"match": {"parent.child.name": "matt"}}
                    }
                }
            }
         }
      }
   },
   "sort" : [
      {
         "parent.child.age" : {
            "mode" :  "min",
            "order" : "asc",
            "nested": {
               "path": "parent",
               "filter": {
                  "range": {"parent.age": {"gte": 21}}
               },
               "nested": {
                  "path": "parent.child",
                  "filter": {
                     "match": {"parent.child.name": "matt"}
                  }
               }
            }
         }
      }
   ]
}

Nested sorting is also supported when sorting by scripts and sorting by geo distance.

The missing parameter specifies how docs which are missing the sort field should be treated: The missing value can be set to _last, _first, or a custom value (that will be used for missing docs as the sort value). The default is _last.

For example:

GET /_search
{
  "sort" : [
    { "price" : {"missing" : "_last"} }
  ],
  "query" : {
    "term" : { "product" : "chocolate" }
  }
}

By default, the search request will fail if there is no mapping associated with a field. The unmapped_type option allows you to ignore fields that have no mapping and not sort by them. The value of this parameter is used to determine what sort values to emit. Here is an example of how it can be used:

GET /_search
{
  "sort" : [
    { "price" : {"unmapped_type" : "long"} }
  ],
  "query" : {
    "term" : { "product" : "chocolate" }
  }
}

If any of the indices that are queried doesn’t have a mapping for price then Elasticsearch will handle it as if there was a mapping of type long, with all documents in this index having no value for this field.

Allow to sort by _geo_distance. Here is an example, assuming pin.location is a field of type geo_point:

GET /_search
{
  "sort" : [
    {
      "_geo_distance" : {
          "pin.location" : [-70, 40],
          "order" : "asc",
          "unit" : "km",
          "mode" : "min",
          "distance_type" : "arc",
          "ignore_unmapped": true
      }
    }
  ],
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

The following formats are supported in providing the coordinates:

GET /_search
{
  "sort" : [
    {
      "_geo_distance" : {
        "pin.location" : {
          "lat" : 40,
          "lon" : -70
        },
        "order" : "asc",
        "unit" : "km"
      }
    }
  ],
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

Format in lat,lon.

GET /_search
{
  "sort": [
    {
      "_geo_distance": {
        "pin.location": "40,-70",
        "order": "asc",
        "unit": "km"
      }
    }
  ],
  "query": {
    "term": { "user": "kimchy" }
  }
}

GET /_search
{
  "sort": [
    {
      "_geo_distance": {
        "pin.location": "drm3btev3e86",
        "order": "asc",
        "unit": "km"
      }
    }
  ],
  "query": {
    "term": { "user": "kimchy" }
  }
}

Format in [lon, lat], note, the order of lon/lat here in order to conform with GeoJSON.

GET /_search
{
  "sort": [
    {
      "_geo_distance": {
        "pin.location": [ -70, 40 ],
        "order": "asc",
        "unit": "km"
      }
    }
  ],
  "query": {
    "term": { "user": "kimchy" }
  }
}

Multiple geo points can be passed as an array containing any geo_point format, for example

GET /_search
{
  "sort": [
    {
      "_geo_distance": {
        "pin.location": [ [ -70, 40 ], [ -71, 42 ] ],
        "order": "asc",
        "unit": "km"
      }
    }
  ],
  "query": {
    "term": { "user": "kimchy" }
  }
}

and so forth.

The final distance for a document will then be min/max/avg (defined via mode) distance of all points contained in the document to all points given in the sort request.

Allow to sort based on custom scripts, here is an example:

GET /_search
{
  "query": {
    "term": { "user": "kimchy" }
  },
  "sort": {
    "_script": {
      "type": "number",
      "script": {
        "lang": "painless",
        "source": "doc['field_name'].value * params.factor",
        "params": {
          "factor": 1.1
        }
      },
      "order": "asc"
    }
  }
}

When sorting on a field, scores are not computed. By setting track_scores to true, scores will still be computed and tracked.

GET /_search
{
  "track_scores": true,
  "sort" : [
    { "post_date" : {"order" : "desc"} },
    { "name" : "desc" },
    { "age" : "desc" }
  ],
  "query" : {
    "term" : { "user" : "kimchy" }
  }
}

When sorting, the relevant sorted field values are loaded into memory. This means that per shard, there should be enough memory to contain them. For string based types, the field sorted on should not be analyzed / tokenized. For numeric types, if possible, it is recommended to explicitly set the type to narrower types (like short, integer and float).