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:
response = client.indices.create(
index: 'my-index-000001',
body: {
mappings: {
properties: {
post_date: {
type: 'date'
user: {
type: 'keyword'
name: {
type: 'keyword'
age: {
type: 'integer'
puts response
res, err := es.Indices.Create(
"my-index-000001",
es.Indices.Create.WithBody(strings.NewReader(`{
"mappings": {
"properties": {
"post_date": {
"type": "date"
"user": {
"type": "keyword"
"name": {
"type": "keyword"
"age": {
"type": "integer"
}`)),
fmt.Println(res, err)
PUT /my-index-000001
"mappings": {
"properties": {
"post_date": { "type": "date" },
"user": {
"type": "keyword"
"name": {
"type": "keyword"
"age": { "type": "integer" }
response = client.search(
index: 'my-index-000001',
body: {
sort: [
post_date: {
order: 'asc',
format: 'strict_date_optional_time_nanos'
'user',
name: 'desc'
age: 'desc'
'_score'
query: {
term: {
user: 'kimchy'
puts response
GET /my-index-000001/_search
"sort" : [
{ "post_date" : {"order" : "asc", "format": "strict_date_optional_time_nanos"}},
"user",
{ "name" : "desc" },
{ "age" : "desc" },
"_score"
"query" : {
"term" : { "user" : "kimchy" }
_doc has no real use-case besides being the most efficient sort order.
So if you don’t care about the order in which documents are returned, then you
should sort by _doc. This especially helps when scrolling.
The search response includes sort values for each document. Use the format
parameter to specify a date format for the sort
values of date and date_nanos fields. The following
search returns sort values for the post_date field in the
strict_date_optional_time_nanos format.
response = client.search(
index: 'my-index-000001',
body: {
sort: [
post_date: {
format: 'strict_date_optional_time_nanos'
query: {
term: {
user: 'kimchy'
puts response
GET /my-index-000001/_search
"sort" : [
{ "post_date" : {"format": "strict_date_optional_time_nanos"}}
"query" : {
"term" : { "user" : "kimchy" }
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.
response = client.index(
index: 'my-index-000001',
id: 1,
refresh: true,
body: {
product: 'chocolate',
price: [
puts response
response = client.search(
body: {
query: {
term: {
product: 'chocolate'
sort: [
price: {
order: 'asc',
mode: 'avg'
puts response
res, err := es.Index(
"my-index-000001",
strings.NewReader(`{
"product": "chocolate",
"price": [
es.Index.WithDocumentID("1"),
es.Index.WithRefresh("true"),
es.Index.WithPretty(),
fmt.Println(res, err)
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"query": {
"term": {
"product": "chocolate"
"sort": [
"price": {
"order": "asc",
"mode": "avg"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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 searches across multiple data streams or indices where the sort field is mapped differently.
Consider for instance these two indices:
response = client.indices.create(
index: 'index_double',
body: {
mappings: {
properties: {
field: {
type: 'double'
puts response
res, err := es.Indices.Create(
"index_double",
es.Indices.Create.WithBody(strings.NewReader(`{
"mappings": {
"properties": {
"field": {
"type": "double"
}`)),
fmt.Println(res, err)
PUT /index_double
"mappings": {
"properties": {
"field": { "type": "double" }
response = client.indices.create(
index: 'index_long',
body: {
mappings: {
properties: {
field: {
type: 'long'
puts response
res, err := es.Indices.Create(
"index_long",
es.Indices.Create.WithBody(strings.NewReader(`{
"mappings": {
"properties": {
"field": {
"type": "long"
}`)),
fmt.Println(res, err)
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:
$params = [
'index' => 'index_long,index_double',
'body' => [
'sort' => [
'field' => [
'numeric_type' => 'double',
$response = $client->search($params);
resp = client.search(
index=["index_long", "index_double"],
body={"sort": [{"field": {"numeric_type": "double"}}]},
print(resp)
response = client.search(
index: 'index_long,index_double',
body: {
sort: [
field: {
numeric_type: 'double'
puts response
res, err := es.Search(
es.Search.WithIndex("index_long,index_double"),
es.Search.WithBody(strings.NewReader(`{
"sort": [
"field": {
"numeric_type": "double"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
const response = await client.search({
index: 'index_long,index_double',
body: {
sort: [
field: {
numeric_type: 'double'
console.log(response)
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:
response = client.indices.create(
index: 'index_double',
body: {
mappings: {
properties: {
field: {
type: 'date'
puts response
res, err := es.Indices.Create(
"index_double",
es.Indices.Create.WithBody(strings.NewReader(`{
"mappings": {
"properties": {
"field": {
"type": "date"
}`)),
fmt.Println(res, err)
PUT /index_double
"mappings": {
"properties": {
"field": { "type": "date" }
response = client.indices.create(
index: 'index_long',
body: {
mappings: {
properties: {
field: {
type: 'date_nanos'
puts response
res, err := es.Indices.Create(
"index_long",
es.Indices.Create.WithBody(strings.NewReader(`{
"mappings": {
"properties": {
"field": {
"type": "date_nanos"
}`)),
fmt.Println(res, err)
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:
$params = [
'index' => 'index_long,index_double',
'body' => [
'sort' => [
'field' => [
'numeric_type' => 'date_nanos',
$response = $client->search($params);
resp = client.search(
index=["index_long", "index_double"],
body={"sort": [{"field": {"numeric_type": "date_nanos"}}]},
print(resp)
res, err := es.Search(
es.Search.WithIndex("index_long,index_double"),
es.Search.WithBody(strings.NewReader(`{
"sort": [
"field": {
"numeric_type": "date_nanos"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
const response = await client.search({
index: 'index_long,index_double',
body: {
sort: [
field: {
numeric_type: 'date_nanos'
console.log(response)
POST /index_long,index_double/_search
"sort" : [
"field" : {
"numeric_type" : "date_nanos"
To avoid overflow, the conversion to date_nanos cannot be applied on dates before
1970 and after 2262 as nanoseconds are represented as longs.
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:
Defines on which nested object to sort. The actual
sort field must be a direct field inside this nested object.
When sorting by nested field, this field is mandatory.
filter
A filter that 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 filter is active.
max_children
The maximum number of children to consider per root document
when picking the sort value. Defaults to unlimited.
nested
Same as top-level nested but applies to another nested path within the
current nested object.
Elasticsearch will throw an error if a nested field is defined in a sort without
a nested context.
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.
$params = [
'body' => [
'query' => [
'term' => [
'product' => 'chocolate',
'sort' => [
'offer.price' => [
'mode' => 'avg',
'order' => 'asc',
'nested' => [
'path' => 'offer',
'filter' => [
'term' => [
'offer.color' => 'blue',
$response = $client->search($params);
resp = client.search(
body={
"query": {"term": {"product": "chocolate"}},
"sort": [
"offer.price": {
"mode": "avg",
"order": "asc",
"nested": {
"path": "offer",
"filter": {"term": {"offer.color": "blue"}},
print(resp)
response = client.search(
body: {
query: {
term: {
product: 'chocolate'
sort: [
"offer.price": {
mode: 'avg',
order: 'asc',
nested: {
path: 'offer',
filter: {
term: {
"offer.color": 'blue'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"query": {
"term": {
"product": "chocolate"
"sort": [
"offer.price": {
"mode": "avg",
"order": "asc",
"nested": {
"path": "offer",
"filter": {
"term": {
"offer.color": "blue"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
const response = await client.search({
body: {
query: {
term: {
product: 'chocolate'
sort: [
'offer.price': {
mode: 'avg',
order: 'asc',
nested: {
path: 'offer',
filter: {
term: {
'offer.color': 'blue'
console.log(response)
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.
$params = [
'body' => [
'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',
$response = $client->search($params);
resp = client.search(
body={
"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"}
print(resp)
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"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"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
const response = await client.search({
body: {
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'
console.log(response)
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:
response = client.search(
body: {
sort: [
price: {
missing: '_last'
query: {
term: {
product: 'chocolate'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"price": {
"missing": "_last"
"query": {
"term": {
"product": "chocolate"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
GET /_search
"sort" : [
{ "price" : {"missing" : "_last"} }
"query" : {
"term" : { "product" : "chocolate" }
If a nested inner object doesn’t match with
the nested.filter then a missing value is used.
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:
response = client.search(
body: {
sort: [
price: {
unmapped_type: 'long'
query: {
term: {
product: 'chocolate'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"price": {
"unmapped_type": "long"
"query": {
"term": {
"product": "chocolate"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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:
response = client.search(
body: {
sort: [
_geo_distance: {
"pin.location": [
order: 'asc',
unit: 'km',
mode: 'min',
distance_type: 'arc',
ignore_unmapped: true
query: {
term: {
user: 'kimchy'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"_geo_distance": {
"pin.location": [
"order": "asc",
"unit": "km",
"mode": "min",
"distance_type": "arc",
"ignore_unmapped": true
"query": {
"term": {
"user": "kimchy"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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" }
What to do in case a field has several geo points. By default, the shortest
distance is taken into account when sorting in ascending order and the
longest distance when sorting in descending order. Supported values are
min, max, median and avg.
The unit to use when computing sort values. The default is m (meters).
ignore_unmapped
Indicates if the unmapped field should be treated as a missing value. Setting it to true is equivalent to specifying
an unmapped_type in the field sort. The default is false (unmapped field cause the search to fail).
geo distance sorting does not support configurable missing values: the
distance will always be considered equal to Infinity when a document does not
have values for the field that is used for distance computation.
The following formats are supported in providing the coordinates:
response = client.search(
body: {
sort: [
_geo_distance: {
"pin.location": {
lat: 40,
lon: -70
order: 'asc',
unit: 'km'
query: {
term: {
user: 'kimchy'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"_geo_distance": {
"pin.location": {
"lat": 40,
"lon": -70
"order": "asc",
"unit": "km"
"query": {
"term": {
"user": "kimchy"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
GET /_search
"sort" : [
"_geo_distance" : {
"pin.location" : {
"lat" : 40,
"lon" : -70
"order" : "asc",
"unit" : "km"
"query" : {
"term" : { "user" : "kimchy" }
Format in Well-Known Text.
response = client.search(
body: {
sort: [
_geo_distance: {
"pin.location": 'POINT (-70 40)',
order: 'asc',
unit: 'km'
query: {
term: {
user: 'kimchy'
puts response
GET /_search
"sort": [
"_geo_distance": {
"pin.location": "POINT (-70 40)",
"order": "asc",
"unit": "km"
"query": {
"term": { "user": "kimchy" }
response = client.search(
body: {
sort: [
_geo_distance: {
"pin.location": 'drm3btev3e86',
order: 'asc',
unit: 'km'
query: {
term: {
user: 'kimchy'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"_geo_distance": {
"pin.location": "drm3btev3e86",
"order": "asc",
"unit": "km"
"query": {
"term": {
"user": "kimchy"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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.
response = client.search(
body: {
sort: [
_geo_distance: {
"pin.location": [
order: 'asc',
unit: 'km'
query: {
term: {
user: 'kimchy'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"_geo_distance": {
"pin.location": [
"order": "asc",
"unit": "km"
"query": {
"term": {
"user": "kimchy"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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
response = client.search(
body: {
sort: [
_geo_distance: {
"pin.location": [
order: 'asc',
unit: 'km'
query: {
term: {
user: 'kimchy'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"sort": [
"_geo_distance": {
"pin.location": [
"order": "asc",
"unit": "km"
"query": {
"term": {
"user": "kimchy"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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:
response = client.search(
body: {
query: {
term: {
user: 'kimchy'
sort: {
_script: {
type: 'number',
script: {
lang: 'painless',
source: "doc['field_name'].value * params.factor",
params: {
factor: 1.1
order: 'asc'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"query": {
"term": {
"user": "kimchy"
"sort": {
"_script": {
"type": "number",
"script": {
"lang": "painless",
"source": "doc['field_name'].value * params.factor",
"params": {
"factor": 1.1
"order": "asc"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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.
response = client.search(
body: {
track_scores: true,
sort: [
post_date: {
order: 'desc'
name: 'desc'
age: 'desc'
query: {
term: {
user: 'kimchy'
puts response
res, err := es.Search(
es.Search.WithBody(strings.NewReader(`{
"track_scores": true,
"sort": [
"post_date": {
"order": "desc"
"name": "desc"
"age": "desc"
"query": {
"term": {
"user": "kimchy"
}`)),
es.Search.WithPretty(),
fmt.Println(res, err)
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).
« Search with synonyms
k-nearest neighbor (kNN) search »
