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Sort

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Sort

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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.

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

Sort Values

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

Sort Order

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The order option can have the following values:

`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

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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:

`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.

Sort mode example usage

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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.

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

Sorting within nested objects.

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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

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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.

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

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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:

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

If a nested inner object doesn’t match with the nested_filter then a missing value is used.

Ignoring Unmapped Fields

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Before 1.4.0 there was the ignore_unmapped boolean parameter, which was not enough information to decide on the sort values to emit, and didn’t work for cross-index search. It is still supported but users are encouraged to migrate to the new unmapped_type instead.

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:

{
    "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 Distance Sorting

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Allow to sort by _geo_distance. Here is an example:

{
    "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

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

Lat Lon as String

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Format in lat,lon.

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

Geohash

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

Lat Lon as Array

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

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

Multiple reference points

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

"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

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

{
    "query" : {
        ....
    },
    "sort" : {
        "_script" : {
            "script" : "doc['field_name'].value * factor",
            "type" : "number",
            "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

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

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

Memory Considerations

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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 six_hun types (like short, integer and float).

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