Search DSL

The Search object

The Search object represents the entire search request:

  • queries

  • filters

  • aggregations

  • k-nearest neighbor searches

  • sort

  • pagination

  • highlighting

  • suggestions

  • collapsing

  • additional parameters

  • associated client

The API is designed to be chainable. With the exception of the aggregations functionality this means that the Search object is immutable - all changes to the object will result in a shallow copy being created which contains the changes. This means you can safely pass the Search object to foreign code without fear of it modifying your objects as long as it sticks to the Search object APIs.

You can pass an instance of the low-level elasticsearch client when instantiating the Search object:

from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search

client = Elasticsearch()

s = Search(using=client)

You can also define the client at a later time (for more options see the Configuration chapter):

s = s.using(client)


All methods return a copy of the object, making it safe to pass to outside code.

The API is chainable, allowing you to combine multiple method calls in one statement:

s = Search().using(client).query("match", title="python")

To send the request to Elasticsearch:

response = s.execute()

If you just want to iterate over the hits returned by your search you can iterate over the Search object:

for hit in s:

Search results will be cached. Subsequent calls to execute or trying to iterate over an already executed Search object will not trigger additional requests being sent to Elasticsearch. To force a request specify ignore_cache=True when calling execute.

For debugging purposes you can serialize the Search object to a dict explicitly:


Delete By Query

You can delete the documents matching a search by calling delete on the Search object instead of execute like this:

s = Search(index='i').query("match", title="python")
response = s.delete()


The library provides classes for all Elasticsearch query types. Pass all the parameters as keyword arguments. The classes accept any keyword arguments, the dsl then takes all arguments passed to the constructor and serializes them as top-level keys in the resulting dictionary (and thus the resulting json being sent to elasticsearch). This means that there is a clear one-to-one mapping between the raw query and its equivalent in the DSL:

from elasticsearch_dsl.query import MultiMatch, Match

# {"multi_match": {"query": "python django", "fields": ["title", "body"]}}
MultiMatch(query='python django', fields=['title', 'body'])

# {"match": {"title": {"query": "web framework", "type": "phrase"}}}
Match(title={"query": "web framework", "type": "phrase"})


In some cases this approach is not possible due to python’s restriction on identifiers - for example if your field is called @timestamp. In that case you have to fall back to unpacking a dictionary: Range(** {'@timestamp': {'lt': 'now'}})

You can use the Q shortcut to construct the instance using a name with parameters or the raw dict:

from elasticsearch_dsl import Q

Q("multi_match", query='python django', fields=['title', 'body'])
Q({"multi_match": {"query": "python django", "fields": ["title", "body"]}})

To add the query to the Search object, use the .query() method:

q = Q("multi_match", query='python django', fields=['title', 'body'])
s = s.query(q)

The method also accepts all the parameters as the Q shortcut:

s = s.query("multi_match", query='python django', fields=['title', 'body'])

If you already have a query object, or a dict representing one, you can just override the query used in the Search object:

s.query = Q('bool', must=[Q('match', title='python'), Q('match', body='best')])

Dotted fields

Sometimes you want to refer to a field within another field, either as a multi-field (title.keyword) or in a structured json document like To make it easier, the Q shortcut (as well as the query, filter, and exclude methods on Search class) allows you to use __ (double underscore) in place of a dot in a keyword argument:

s = Search()
s = s.filter('term', category__keyword='Python')
s = s.query('match', address__city='prague')

Alternatively you can always fall back to python’s kwarg unpacking if you prefer:

s = Search()
s = s.filter('term', **{'category.keyword': 'Python'})
s = s.query('match', **{'': 'prague'})

Query combination

Query objects can be combined using logical operators:

Q("match", title='python') | Q("match", title='django')
# {"bool": {"should": [...]}}

Q("match", title='python') & Q("match", title='django')
# {"bool": {"must": [...]}}

~Q("match", title="python")
# {"bool": {"must_not": [...]}}

When you call the .query() method multiple times, the & operator will be used internally:

s = s.query().query()
# {"query": {"bool": {...}}}

If you want to have precise control over the query form, use the Q shortcut to directly construct the combined query:

q = Q('bool',
    must=[Q('match', title='python')],
    should=[Q(...), Q(...)],
s = Search().query(q)


If you want to add a query in a filter context you can use the filter() method to make things easier:

s = Search()
s = s.filter('terms', tags=['search', 'python'])

Behind the scenes this will produce a Bool query and place the specified terms query into its filter branch, making it equivalent to:

s = Search()
s = s.query('bool', filter=[Q('terms', tags=['search', 'python'])])

If you want to use the post_filter element for faceted navigation, use the .post_filter() method.

You can also exclude() items from your query like this:

s = Search()
s = s.exclude('terms', tags=['search', 'python'])

which is shorthand for: s = s.query('bool', filter=[~Q('terms', tags=['search', 'python'])])


To define an aggregation, you can use the A shortcut:

from elasticsearch_dsl import A

A('terms', field='tags')
# {"terms": {"field": "tags"}}

To nest aggregations, you can use the .bucket(), .metric() and .pipeline() methods:

a = A('terms', field='category')
# {'terms': {'field': 'category'}}

a.metric('clicks_per_category', 'sum', field='clicks')\
    .bucket('tags_per_category', 'terms', field='tags')
# {
#   'terms': {'field': 'category'},
#   'aggs': {
#     'clicks_per_category': {'sum': {'field': 'clicks'}},
#     'tags_per_category': {'terms': {'field': 'tags'}}
#   }
# }

To add aggregations to the Search object, use the .aggs property, which acts as a top-level aggregation:

s = Search()
a = A('terms', field='category')
s.aggs.bucket('category_terms', a)
# {
#   'aggs': {
#     'category_terms': {
#       'terms': {
#         'field': 'category'
#       }
#     }
#   }
# }


s = Search()
s.aggs.bucket('articles_per_day', 'date_histogram', field='publish_date', interval='day')\
    .metric('clicks_per_day', 'sum', field='clicks')\
    .pipeline('moving_click_average', 'moving_avg', buckets_path='clicks_per_day')\
    .bucket('tags_per_day', 'terms', field='tags')

# {
#   "aggs": {
#     "articles_per_day": {
#       "date_histogram": { "interval": "day", "field": "publish_date" },
#       "aggs": {
#         "clicks_per_day": { "sum": { "field": "clicks" } },
#         "moving_click_average": { "moving_avg": { "buckets_path": "clicks_per_day" } },
#         "tags_per_day": { "terms": { "field": "tags" } }
#       }
#     }
#   }
# }

You can access an existing bucket by its name:

s = Search()

s.aggs.bucket('per_category', 'terms', field='category')
s.aggs['per_category'].metric('clicks_per_category', 'sum', field='clicks')
s.aggs['per_category'].bucket('tags_per_category', 'terms', field='tags')


When chaining multiple aggregations, there is a difference between what .bucket() and .metric() methods return - .bucket() returns the newly defined bucket while .metric() returns its parent bucket to allow further chaining.

As opposed to other methods on the Search objects, defining aggregations is done in-place (does not return a copy).

K-Nearest Neighbor Searches

To issue a kNN search, use the .knn() method:

s = Search()
vector = get_embedding("search text")

s = s.knn(

The field, k and num_candidates arguments can be given as positional or keyword arguments and are required. In addition to these, query_vector or query_vector_builder must be given as well.

The .knn() method can be invoked multiple times to include multiple kNN searches in the request.


To specify sorting order, use the .sort() method:

s = Search().sort(
    {"lines" : {"order" : "asc", "mode" : "avg"}}

It accepts positional arguments which can be either strings or dictionaries. String value is a field name, optionally prefixed by the - sign to specify a descending order.

To reset the sorting, just call the method with no arguments:

s = s.sort()


To specify the from/size parameters, use the Python slicing API:

s = s[10:20]
# {"from": 10, "size": 10}

If you want to access all the documents matched by your query you can use the scan method which uses the scan/scroll elasticsearch API:

for hit in s.scan():

Note that in this case the results won’t be sorted.


To set common attributes for highlighting use the highlight_options method:

s = s.highlight_options(order='score')

Enabling highlighting for individual fields is done using the highlight method:

s = s.highlight('title')
# or, including parameters:
s = s.highlight('title', fragment_size=50)

The fragments in the response will then be available on each Result object as .meta.highlight.FIELD which will contain the list of fragments:

response = s.execute()
for hit in response:
    for fragment in hit.meta.highlight.title:


To specify a suggest request on your Search object use the suggest method:

# check for correct spelling
s = s.suggest('my_suggestion', 'pyhton', term={'field': 'title'})

The first argument is the name of the suggestions (name under which it will be returned), second is the actual text you wish the suggester to work on and the keyword arguments will be added to the suggest’s json as-is which means that it should be one of term, phrase or completion to indicate which type of suggester should be used.


To collapse search results use the collapse method on your Search object:

s = Search().query("match", message="GET /search")
# collapse results by user_id
s = s.collapse("user_id")

The top hits will only include one result per user_id. You can also expand each collapsed top hit with the inner_hits parameter, max_concurrent_group_searches being the number of concurrent requests allowed to retrieve the inner hits per group:

inner_hits = {"name": "recent_search", "size": 5, "sort": [{"@timestamp": "desc"}]}
s = s.collapse("user_id", inner_hits=inner_hits, max_concurrent_group_searches=4)

More Like This Query

To use Elasticsearch’s more_like_this functionality, you can use the MoreLikeThis query type.

A simple example is below

from elasticsearch_dsl.query import MoreLikeThis
from elasticsearch_dsl import Search

my_text = 'I want to find something similar'

s = Search()
# We're going to match based only on two fields, in this case text and title
s = s.query(MoreLikeThis(like=my_text, fields=['text', 'title']))
# You can also exclude fields from the result to make the response quicker in the normal way
s = s.source(exclude=["text"])
response = s.execute()

for hit in response:

Extra properties and parameters

To set extra properties of the search request, use the .extra() method. This can be used to define keys in the body that cannot be defined via a specific API method like explain or search_after:

s = s.extra(explain=True)

To set query parameters, use the .params() method:

s = s.params(routing="42")

If you need to limit the fields being returned by elasticsearch, use the source() method:

# only return the selected fields
s = s.source(['title', 'body'])
# don't return any fields, just the metadata
s = s.source(False)
# explicitly include/exclude fields
s = s.source(includes=["title"], excludes=["user.*"])
# reset the field selection
s = s.source(None)

Serialization and Deserialization

The search object can be serialized into a dictionary by using the .to_dict() method.

You can also create a Search object from a dict using the from_dict class method. This will create a new Search object and populate it using the data from the dict:

s = Search.from_dict({"query": {"match": {"title": "python"}}})

If you wish to modify an existing Search object, overriding it’s properties, instead use the update_from_dict method that alters an instance in-place:

s = Search(index='i')
s.update_from_dict({"query": {"match": {"title": "python"}}, "size": 42})


You can execute your search by calling the .execute() method that will return a Response object. The Response object allows you access to any key from the response dictionary via attribute access. It also provides some convenient helpers:

response = s.execute()

# True

# 12

# eq
# 142


If you want to inspect the contents of the response objects, just use its to_dict method to get access to the raw data for pretty printing.


To access to the hits returned by the search, access the hits property or just iterate over the Response object:

response = s.execute()
print('Total %d hits found.' %
for h in response:
    print(h.title, h.body)


If you are only seeing partial results (e.g. 10000 or even 10 results), consider using the option s.extra(track_total_hits=True) to get a full hit count.


The individual hits is wrapped in a convenience class that allows attribute access to the keys in the returned dictionary. All the metadata for the results are accessible via meta (without the leading _):

response = s.execute()
h = response.hits[0]
print('/%s/%s/%s returned with score %f' % (
    h.meta.index, h.meta.doc_type,, h.meta.score))


If your document has a field called meta you have to access it using the get item syntax: hit['meta'].


Aggregations are available through the aggregations property:

for tag in response.aggregations.per_tag.buckets:
    print(tag.key, tag.max_lines.value)


If you need to execute multiple searches at the same time you can use the MultiSearch class which will use the _msearch API:

from elasticsearch_dsl import MultiSearch, Search

ms = MultiSearch(index='blogs')

ms = ms.add(Search().filter('term', tags='python'))
ms = ms.add(Search().filter('term', tags='elasticsearch'))

responses = ms.execute()

for response in responses:
    print("Results for query %r." %
    for hit in response: