OpenSearch/docs/reference/search/request/sort.asciidoc

[[search-request-sort]]
=== Sort

Allows to add one or more sort 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.

[source,js]
--------------------------------------------------
{
    "sort" : [
        { "post_date" : {"order" : "asc"}},
        "user",
        { "name" : "desc" },
        { "age" : "desc" },
        "_score"
    ],
    "query" : {
        "term" : { "user" : "kimchy" }
    }
}
--------------------------------------------------

==== Sort Values

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

==== Sort Order

The `order` option can have the following values:

[horizontal]
`asc`:: Sort in ascending order
`desc`:: Sort in descending order

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

==== Sort mode option

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:

[horizontal]
`min`:: Pick the lowest value.
`max`:: Pick the highest value.
`sum`:: Use the sum of all values as sort value. Only applicable for
        number based array fields.
`avg`:: Use the average of all values as sort value. Only applicable
        for number based array fields.
`median`:: Use the median of all values as sort value.  Only applicable
           for number based array fields.

===== Sort mode example usage

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

[source,js]
--------------------------------------------------
curl -XPOST 'localhost:9200/_search' -d '{
   "query" : {
    ...
   },
   "sort" : [
      {"price" : {"order" : "asc", "mode" : "avg"}}
   ]
}'
--------------------------------------------------

[[nested-sorting]]
==== Sorting within nested objects.

Elasticsearch also supports sorting by
fields that are inside one or more nested objects. The sorting by nested
field support has the following parameters on top of the already
existing sort options:

`nested_path`::
    Defines the on what nested object to sort. The actual
    sort field must be a direct field inside this nested object. The default
    is to use the most immediate inherited nested object from the sort
    field.

`nested_filter`::
    A filter the inner objects inside the nested path
    should match with in order for its field values to be taken into account
    by sorting. Common case is to repeat the query / filter inside the
    nested filter or query. By default no `nested_filter` is active.

===== Nested sorting example

In the below example `offer` is a field of type `nested`. Because
`offer` is the closest inherited nested field, it is picked as
`nested_path`. Only the inner objects that have color blue will
participate in sorting.

[source,js]
--------------------------------------------------
curl -XPOST 'localhost:9200/_search' -d '{
   "query" : {
    ...
   },
   "sort" : [
       {
          "offer.price" : {
             "mode" :  "avg",
             "order" : "asc",
             "nested_filter" : {
                "term" : { "offer.color" : "blue" }
             }
          }
       }
    ]
}'
--------------------------------------------------

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

==== Missing Values

The `missing` parameter specifies how docs which are missing
the 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). For example:

[source,js]
--------------------------------------------------
{
    "sort" : [
        { "price" : {"missing" : "_last"} },
    ],
    "query" : {
        "term" : { "user" : "kimchy" }
    }
}
--------------------------------------------------

NOTE: If a nested inner object doesn't match with
the `nested_filter` then a missing value is used.

==== Ignoring Unmapped Fields

By default, the search request will fail if there is no mapping
associated with a field. The `unmapped_type` option allows 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:

[source,js]
--------------------------------------------------
{
    "sort" : [
        { "price" : {"unmapped_type" : "long"} },
    ],
    "query" : {
        "term" : { "user" : "kimchy" }
    }
}
--------------------------------------------------

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.

[[geo-sorting]]
==== Geo Distance Sorting

Allow to sort by `_geo_distance`. Here is an example:

[source,js]
--------------------------------------------------
{
    "sort" : [
        {
            "_geo_distance" : {
                "pin.location" : [-70, 40],
                "order" : "asc",
                "unit" : "km",
		"mode" : "min",
		"distance_type" : "sloppy_arc"
            }
        }
    ],
    "query" : {
        "term" : { "user" : "kimchy" }
    }
}
--------------------------------------------------



`distance_type`::

    How to compute the distance. Can either be `sloppy_arc` (default), `arc` (slightly more precise but significantly slower) or `plane` (faster, but inaccurate on long distances and close to the poles).

Note: the geo distance sorting supports `sort_mode` options: `min`,
`max` and `avg`.

The following formats are supported in providing the coordinates:

===== Lat Lon as Properties

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

===== Lat Lon as String

Format in `lat,lon`.

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

===== Geohash

[source,js]
--------------------------------------------------
{
    "sort" : [
        {
            "_geo_distance" : {
                "pin.location" : "drm3btev3e86",
                "order" : "asc",
                "unit" : "km"
            }
        }
    ],
    "query" : {
        "term" : { "user" : "kimchy" }
    }
}
--------------------------------------------------

===== Lat Lon as Array

Format in `[lon, lat]`, note, the order of lon/lat here in order to
conform with http://geojson.org/[GeoJSON].

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


==== Multiple reference points

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

[source,js]
--------------------------------------------------
"pin.location" : [[-70, 40], [-71, 42]]
"pin.location" : [{"lat": -70, "lon": 40}, {"lat": -71, "lon": 42}]
--------------------------------------------------

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.



==== Script Based Sorting

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

[source,js]
--------------------------------------------------
{
    "query" : {
        ....
    },
    "sort" : {
        "_script" : {
            "type" : "number",
            "script" : {
                "inline": "doc['field_name'].value * factor",
                "params" : {
                    "factor" : 1.1
                }
            },
            "order" : "asc"
        }
    }
}
--------------------------------------------------

Note, it is recommended, for single custom based script based sorting,
to use `function_score` query instead as sorting based on score is faster.

==== Track Scores

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

[source,js]
--------------------------------------------------
{
    "track_scores": true,
    "sort" : [
        { "post_date" : {"reverse" : true} },
        { "name" : "desc" },
        { "age" : "desc" }
    ],
    "query" : {
        "term" : { "user" : "kimchy" }
    }
}
--------------------------------------------------

==== Memory Considerations

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`).