Elasticsearch DSL¶

Elasticsearch DSL is a high-level library whose aim is to help with writing and running queries against Elasticsearch. It is built on top of the official low-level client (elasticsearch-py).

It provides a more convenient and idiomatic way to write and manipulate queries. It stays close to the Elasticsearch JSON DSL, mirroring its terminology and structure. It exposes the whole range of the DSL from Python either directly using defined classes or a queryset-like expressions.

It also provides an optional wrapper for working with documents as Python objects: defining mappings, retrieving and saving documents, wrapping the document data in user-defined classes.

To use the other Elasticsearch APIs (eg. cluster health) just use the underlying client.

Compatibility¶

The library is compatible with all Elasticsearch versions since 1.x but you have to use a matching major version:

For Elasticsearch 2.0 and later, use the major version 2 (2.x.y) of the library.

For Elasticsearch 1.0 and later, use the major version 0 (0.x.y) of the library.

The recommended way to set your requirements in your setup.py or requirements.txt is:

# Elasticsearch 2.x
elasticsearch-dsl>=2.0.0,<3.0.0

# Elasticsearch 1.x
elasticsearch-dsl<2.0.0

The development is happening on master and 1.x branches, respectively.

Search Example¶

Let’s have a typical search request written directly as a dict:

from elasticsearch import Elasticsearch
client = Elasticsearch()

response = client.search(
    index="my-index",
    body={
      "query": {
        "filtered": {
          "query": {
            "bool": {
              "must": [{"match": {"title": "python"}}],
              "must_not": [{"match": {"description": "beta"}}]
            }
          },
          "filter": {"term": {"category": "search"}}
        }
      },
      "aggs" : {
        "per_tag": {
          "terms": {"field": "tags"},
          "aggs": {
            "max_lines": {"max": {"field": "lines"}}
          }
        }
      }
    }
)

for hit in response['hits']['hits']:
    print(hit['_score'], hit['_source']['title'])

for tag in response['aggregations']['per_tag']['buckets']:
    print(tag['key'], tag['max_lines']['value'])

The problem with this approach is that it is very verbose, prone to syntax mistakes like incorrect nesting, hard to modify (eg. adding another filter) and definitely not fun to write.

Let’s rewrite the example using the Python DSL:

from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search, Q

client = Elasticsearch()

s = Search(using=client, index="my-index") \
    .filter("term", category="search") \
    .query("match", title="python")   \
    .query(~Q("match", description="beta"))

s.aggs.bucket('per_tag', 'terms', field='tags') \
    .metric('max_lines', 'max', field='lines')

response = s.execute()

for hit in response:
    print(hit.meta.score, hit.title)

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

As you see, the library took care of:

creating appropriate Query objects by name (eq. “match”)

composing queries into a compound bool query

creating a filtered query since .filter() was used

providing a convenient access to response data

no curly or square brackets everywhere

Persistence Example¶

Let’s have a simple Python class representing an article in a blogging system:

from datetime import datetime
from elasticsearch_dsl import DocType, String, Date, Integer
from elasticsearch_dsl.connections import connections

# Define a default Elasticsearch client
connections.create_connection(hosts=['localhost'])

class Article(DocType):
    title = String(analyzer='snowball', fields={'raw': String(index='not_analyzed')})
    body = String(analyzer='snowball')
    tags = String(index='not_analyzed')
    published_from = Date()
    lines = Integer()

    class Meta:
        index = 'blog'

    def save(self, ** kwargs):
        self.lines = len(self.body.split())
        return super(Article, self).save(** kwargs)

    def is_published(self):
        return datetime.now() < self.published_from

# create the mappings in elasticsearch
Article.init()

# create and save and article
article = Article(meta={'id': 42}, title='Hello world!', tags=['test'])
article.body = ''' looong text '''
article.published_from = datetime.now()
article.save()

article = Article.get(id=42)
print(article.is_published())

# Display cluster health
print(connections.get_connection().cluster.health())

In this example you can see:

providing a Default connection

defining fields with mapping configuration

setting index name

defining custom methods

overriding the built-in .save() method to hook into the persistence life cycle

retrieving and saving the object into Elasticsearch

accessing the underlying client for other APIs

You can see more in the Persistence chapter.

Pre-built Faceted Search¶

If you have your DocTypes defined you can very easily create a faceted search class to simplify searching and filtering.

Note

This feature is experimental and may be subject to change.

from elasticsearch_dsl import FacetedSearch
from elasticsearch_dsl.aggs import Terms, DateHistogram

class BlogSearch(FacetedSearch):
    doc_types = [Article, ]
    # fields that should be searched
    fields = ['tags', 'title', 'body']

    facets = {
        # use bucket aggregations to define facets
        'tags': Terms(field='tags'),
        'publishing_frequency': DateHistogram(field='published_from', interval='month')
    }

# empty search
bs = BlogSearch()
response = bs.execute()

for hit in response:
    print(hit.meta.score, hit.title)

for (tag, count, selected) in response.facets.tags:
    print(tag, ' (SELECTED):' if selected else ':', count)

for (month, count, selected) in response.facets.publishing_frequency:
    print(month.strftime('%B %Y'), ' (SELECTED):' if selected else ':', count)

You can find more details in the Faceted Search chapter.

Migration from `elasticsearch-py`¶

You don’t have to port your entire application to get the benefits of the Python DSL, you can start gradually by creating a Search object from your existing dict, modifying it using the API and serializing it back to a dict:

body = {...} # insert complicated query here

# Convert to Search object
s = Search.from_dict(body)

# Add some filters, aggregations, queries, ...
s.filter("term", tags="python")

# Convert back to dict to plug back into existing code
body = s.to_dict()

License¶

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Contents¶

Configuration¶

There are several ways how to configure connections for the library. Easiest option, and most useful, is to just define one default connection that will be used every time an API call is made without explicitly passing in other connection.

When using elasticsearch_dsl it is highly recommended to use the attached serializer (elasticsearch_dsl.serializer.serializer) that will make sure your objects are correctly serialized into json every time. The create_connection method that is described here (and that configure method uses under the hood) will do that automatically for you, unless you explicitly specify your own serializer. The serializer we use will also allow you to serialize your own objects - just define a to_dict() method on your objects and it will automatically be called when serializing to json.

Note

Unless you want to access multiple clusters from your application it is highly recommended that you use the create_connection method and all operations will use that connection automatically.

Manual¶

If you don’t wish to supply global configuration you can always pass in your own connection (instance of elasticsearch.Elasticsearch) as parameter using wherever it is accepted:

s = Search(using=Elasticsearch('localhost'))

You can even use this approach to override any connection the object might be already associated with:

s = s.using(Elasticsearch('otherhost:9200'))

Default connection¶

To define a default connection that will be used globally, use the connections module and the create_connection method:

from elasticsearch_dsl.connections import connections

connections.create_connection(hosts=['localhost'], timeout=20)

Any keyword arguments (hosts and timeout in our example) will be passed to the Elasticsearch class from elasticsearch-py. To see all the possible configuration options see the documentation.

Multiple clusters¶

You can define multiple connections to multiple clusters, either at the same time using the configure method:

from elasticsearch_dsl.connections import connections

connections.configure(
    default={'hosts': 'localhost'},
    dev={
        'hosts': ['esdev1.example.com:9200'],
        'sniff_on_start': True
    }
)

Such connections will be constructed lazily when requested for the first time.

Or just add them one by one:

# if you have configuration to be passed to Elasticsearch.__init__
connections.create_connection('qa', hosts=['esqa1.example.com'], sniff_on_start=True)

# if you already have an Elasticsearch instance ready
connections.add_connection('qa', my_client)

Using aliases¶

When using multiple connections you can just refer to them using the string alias you registered them under:

s = Search(using='qa')

KeyError will be raised if there is no connection registered under that alias.

Search DSL¶

The `Search` object¶

The Search object represents the entire search request:

queries

filters

aggregations

sort

pagination

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 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.

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 ~:ref:connections chapter):

s = s.using(client)

Note

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:
    print(hit.title)

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:

print(s.to_dict())

Queries¶

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"})

Note

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:

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')])

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()
print(s.to_dict())
# {"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(...)],
    minimum_should_match=1
)
s = Search().query(q)

Filters¶

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.

Aggregations¶

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

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'
#       }
#     }
#   }
# }

or

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')

s.to_dict()
# {
#   "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')

Note

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).

Sorting¶

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

s = Search().sort(
    'category',
    '-title',
    {"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()

Pagination¶

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():
    print(hit.title)

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

Highlighting¶

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 reach 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:
        print(fragment)

Suggestions¶

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

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.

If you only wish to run the suggestion part of the search (via the _suggest endpoint) you can do so via execute_suggest:

s = s.suggest('my_suggestion', 'pyhton', term={'field': 'title'})
suggestions = s.execute_suggest()

print(suggestions.my_suggestion)

Extra properties and parameters¶

To set extra properties of the search request, use the .extra() method:

s = s.extra(explain=True)

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

s = s.params(search_type="count")

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

# only return the selected fields
s = s.fields(['title', 'body'])
# reset the field selection
s = s.fields()
# don't return any fields, just the metadata
s = s.fields([])

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})

Response¶

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()

print(response.success())
# True

print(response.took)
# 12

print(response.hits.total)

print(response.suggest.my_suggestions)

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.

Hits¶

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.' % response.hits.total)
for h in response:
    print(h.title, h.body)

Result¶

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.id, h.meta.score))

Note

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

Aggregations¶

Aggregations are available through the aggregations property:

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

`MultiSearch`¶

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." % response.search.query)
    for hit in response:
        print(hit.title)

Persistence¶

You can use the dsl library to define your mappings and a basic persistent layer for your application.

Mappings¶

The mapping definition follows a similar pattern to the query dsl:

from elasticsearch_dsl import Mapping, String, Nested

# name your type
m = Mapping('my-type')

# add fields
m.field('title', 'string')

# you can use multi-fields easily
m.field('category', 'string', fields={'raw': String(index='not_analyzed')})

# you can also create a field manually
comment = Nested()
comment.field('author', String())
comment.field('created_at', Date())

# and attach it to the mapping
m.field('comments', comment)

# you can also define mappings for the meta fields
m.meta('_all', enabled=False)

# save the mapping into index 'my-index'
m.save('my-index')

Note

By default all fields (with the exception of Nested) will expect single values. You can always override this expectation during the field creation/definition by passing in multi=True into the constructor (m.field('tags', String(index='not_analyzed', multi=True))). Then the value of the field, even if the field hasn’t been set, will be an empty list enabling you to write doc.tags.append('search').

Especially if you are using dynamic mappings it might be useful to update the mapping based on an existing type in Elasticsearch, or create the mapping directly from an existing type:

# get the mapping from our production cluster
m = Mapping.from_es('my-index', 'my-type', using='prod')

# update based on data in QA cluster
m.update_from_es('my-index', using='qa')

# update the mapping on production
m.save('my-index', using='prod')

Common field options:

multi: If set to True the field’s value will be set to [] at first access.
required: Indicates if a field requires a value for the document to be valid.

Analysis¶

To specify analyzer values for String fields you can just use the name of the analyzer (as a string) and either rely on the analyzer being defined (like built-in analyzers) or define the analyzer yourself manually.

Alternatively you can create your own analyzer and have the persistence layer handle its creation:

from elasticsearch_dsl import analyzer, tokenizer

my_analyzer = analyzer('my_analyzer',
    tokenizer=tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
    filter=['lowercase']
)

Each analysis object needs to have a name (my_analyzer and trigram in our example) and tokenizers, token filters and char filters also need to specify type (nGram in our example).

Note

When creating a mapping which relies on a custom analyzer the index must either not exist or be closed. To create multiple DocType-defined mappings you can use the Index object.

DocType¶

If you want to create a model-like wrapper around your documents, use the DocType class:

from datetime import datetime
from elasticsearch_dsl import DocType, String, Date, Nested, Boolean, \
    analyzer, InnerObjectWrapper, Completion

html_strip = analyzer('html_strip',
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)

class Comment(InnerObjectWrapper):
    def age(self):
        return datetime.now() - self.created_at

class Post(DocType):
    title = String()
    title_suggest = Completion(payloads=True)
    created_at = Date()
    published = Boolean()
    category = String(
        analyzer=html_strip,
        fields={'raw': String(index='not_analyzed')}
    )

    comments = Nested(
        doc_class=Comment,
        properties={
            'author': String(fields={'raw': String(index='not_analyzed')}),
            'content': String(analyzer='snowball'),
            'created_at': Date()
        }
    )

    class Meta:
        index = 'blog'

    def add_comment(self, author, content):
        self.comments.append(
          {'author': author, 'content': content})

    def save(self, ** kwargs):
        self.created_at = datetime.now()
        return super().save(** kwargs)

Document life cycle¶

Before you first use the Post document type, you need to create the mappings in Elasticsearch. For that you can either use the Index object or create the mappings directly by calling the init class method:

# create the mappings in Elasticsearch
Post.init()

To create a new Post document just instantiate the class and pass in any fields you wish to set, you can then use standard attribute setting to change/add more fields. Note that you are not limited to the fields defined explicitly:

# instantiate the document
first = Post(title='My First Blog Post, yay!', published=True)
# assign some field values, can be values or lists of values
first.category = ['everything', 'nothing']
# every document has an id in meta
first.meta.id = 47


# save the document into the cluster
first.save()

All the metadata fields (id, parent, routing, index etc) can be accessed (and set) via a meta attribute or directly using the underscored variant:

post = Post(meta={'id': 42})

# prints 42, same as post._id
print(post.meta.id)

# override default index, same as post._index
post.meta.index = 'my-blog'

Note

Having all metadata accessible through meta means that this name is reserved and you shouldn’t have a field called meta on your document. If you, however, need it you can still access the data using the get item (as opposed to attribute) syntax: post['meta'].

To retrieve an existing document use the get class method:

# retrieve the document
first = Post.get(id=42)
# now we can call methods, change fields, ...
first.add_comment('me', 'This is nice!')
# and save the changes into the cluster again
first.save()

# you can also update just individual fields which will call the update API
# and also update the document in place
first.update(published=True, published_by='me')

If the document is not found in elasticsearch an exception (elasticsearch.NotFoundError) will be raised. If you wish to return None instead just pass in ignore=404 to suppress the exception:

p = Post.get(id='not-in-es', ignore=404)
p is None

When you wish to retrive multiple documents at the same time by their id you can use the mget method:

posts = Post.mget([42, 47, 256])

mget will, by default, raise a NotFoundError if any of the documents wasn’t found and RequestError if any of the document had resulted in error. You can control this behavior by setting parameters:

raise_on_error: If True (default) then any error will cause an exception to be raised. Otherwise all documents containing errors will be treated as missing.
missing: Can have three possible values: 'none' (default), 'raise' and 'skip'. If a document is missing or errored it will either be replaced with None, an exception will be raised or the document will be skipped in the output list entirely.

All the information about the DocType, including its Mapping can be accessed through the _doc_type attribute of the class:

# name of the type and index in elasticsearch
Post._doc_type.name
Post._doc_type.index

# the raw Mapping object
Post._doc_type.mapping

# the optional name of the parent type (if defined)
Post._doc_type.parent

The _doc_type attribute is also home to the refresh method which will update the mapping on the DocType from elasticsearch. This is very useful if you use dynamic mappings and want the class to be aware of those fields (for example if you wish the Date fields to be properly (de)serialized):

Post._doc_type.refresh()

To delete a document just call its delete method:

first = Post.get(id=42)
first.delete()

Search¶

To search for this document type, use the search class method:

# by calling .search we get back a standard Search object
s = Post.search()
# the search is already limited to the index and doc_type of our document
s = s.filter('term', published=True).query('match', title='first')


results = s.execute()

# when you execute the search the results are wrapped in your document class (Post)
for posts in results:
    print(post.meta.score, post.title)

Alternatively you can just take a Search object and restrict it to return our document type, wrapped in correct class:

s = Search()
s = s.doc_type(Post)

You can also combine document classes with standard doc types (just strings), which will be treated as before. You can also pass in multiple DocType subclasses and each document in the response will be wrapped in it’s class.

If you want to run suggestions, just use the suggest method on the Search object:

s = Post.search()
s = s.suggest('title_suggestions', 'pyth', completion={'field': 'title_suggest'})

# you can even execute just the suggestions via the _suggest API
suggestions = s.execute_suggest()

for result in suggestions.title_suggestions:
    print('Suggestions for %s:' % result.text)
    for option in result.options:
        print('  %s (%r)' % (option.text, option.payload))

`class Meta` options¶

In the Meta class inside your document definition you can define various metadata for your document:

doc_type: name of the doc_type in elasticsearch. By default it will be constructed from the class name (MyDocument -> my_document)
index: default index for the document, by default it is empty and every operation such as get or save requires an explicit index parameter
using: default connection alias to use, defaults to 'default'
mapping: optional instance of Mapping class to use as base for the mappings created from the fields on the document class itself.

Any attributes on the Meta class that are instance of MetaField will be used to control the mapping of the meta fields (_all, _parent etc). Just name the parameter (without the leading underscore) as the field you wish to map and pass any parameters to the MetaField class:

class Post(DocType):
    title = String()

    class Meta:
        all = MetaField(enabled=False)
        parent = MetaField(type='blog')
        dynamic = MetaField('strict')

Index¶

Index is a class responsible for holding all the metadata related to an index in elasticsearch - mappings and settings. It is most useful when defining your mappings since it allows for easy creation of multiple mappings at the same time. This is especially useful when setting up your elasticsearch objects in a migration:

from elasticsearch_dsl import Index, DocType, String, analyzer

blogs = Index('blogs')

# define custom settings
blogs.settings(
    number_of_shards=1,
    number_of_replicas=0
)

# define aliases
blogs.aliases(
    old_blogs={}
)

# register a doc_type with the index
blogs.doc_type(Post)

# can also be used as class decorator when defining the DocType
@blogs.doc_type
class Post(DocType):
    title = String()

# You can attach custom analyzers to the index

html_strip = analyzer('html_strip',
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)

blog.analyzer(html_strip)

# delete the index, ignore if it doesn't exist
blogs.delete(ignore=404)

# create the index in elasticsearch
blogs.create()

You can also set up a template for your indices and use the clone method to create specific copies:

blogs = Index('blogs', using='production')
blogs.settings(number_of_shards=2)
blogs.doc_type(Post)

# create a copy of the index with different name
company_blogs = blogs.clone('company-blogs')

# create a different copy on different cluster
dev_blogs = blogs.clone('blogs', using='dev')
# and change its settings
dev_blogs.setting(number_of_shards=1)

Faceted Search¶

The library comes with a simple abstraction aimed at helping you develop faceted navigation for your data.

Note

This API is experimental and will be subject to change. Any feedback is welcome.

Configuration¶

You can provide several configuration options (as class attributes) when declaring a FacetedSearch subclass:

index: the name of the index (as string) to search through, defaults to '_all'.
doc_types: list of DocType subclasses or strings to be used, defaults to ['_all'].
fields: list of fields on the document type to search through. The list will be passes to MultiMatch query so can contain boost values ('title^5'), defaults to ['*'].
facets: dictionary of facets to display/filter on. The key is the name displayed and values should be instances of any Facet subclass, for example: {'tags': TermsFacet(field='tags')}

Advanced¶

If you require any custom behavior or modifications simply override one or more of the methods responsible for the class’ functions:

search(self): is responsible for constructing the Search object used. Override this if you want to customize the search object (for example by adding a global filter for published articles only).
query(self, search): adds the query postion of the search (if search input specified), by default using MultiField query. Override this if you want to modify the query type used.
highlight(self, search): defines the highlighting on the Search object and returns a new one. Default behavior is to highlight on all fields specified for search.

Usage¶

The custom subclass can be instantiated empty to provide an empty search (matching everything) or with query and filters.

query: is used to pass in the text of the query to be performed. If None is passed in (default) a MatchAll query will be used. For example 'python web'
filters: is a dictionary containing all the facet filters that you wish to apply. Use the name of the facet (from .facets attribute) as the key and one of the possible values as value. For example {'tags': 'python'}.

Response¶

the response returned from the FacetedSearch object (by calling .execute()) is a subclass of the standard Response class that adds a property called facets which contains a dictionary with lists of buckets - each represented by a tuple of key, document count and a flag indicating whether this value has been filtered on.

Example¶

from datetime import date

from elasticsearch_dsl import FacetedSearch, TermsFacet, DateHistogramFacet

class BlogSearch(FacetedSearch):
    doc_types = [Article, ]
    # fields that should be searched
    fields = ['tags', 'title', 'body']

    facets = {
        # use bucket aggregations to define facets
        'tags': TermsFacet(field='tags'),
        'publishing_frequency': DateHistogramFacet(field='published_from', interval='month')
    }

    def search(self):
        # override methods to add custom pieces
        s = super().search()
        return s.filter('range', publish_from={'lte': 'now/h'})

bs = BlogSearch('python web', {'publishing_frequency': date(2015, 6)})
response = bs.execute()

# access hits and other attributes as usual
print(response.hits.total, 'hits total')
for hit in response:
    print(hit.meta.score, hit.title)

for (tag, count, selected) in response.facets.tags:
    print(tag, ' (SELECTED):' if selected else ':', count)

for (month, count, selected) in response.facets.publishing_frequency:
    print(month.strftime('%B %Y'), ' (SELECTED):' if selected else ':', count)

Changelog¶

2.2.0 (2016-11-04)¶

accessing missing string fields no longer returnd '' but returns None instead.

fix issues with bool’s | and & operators and minimum_should_match

2.1.0 (2016-06-29)¶

inner_hits are now also wrapped in Response

+ operator is deprecated, .query() now uses & to combine queries

added mget method to DocType

fixed validation for “empty” values like '' and []

2.0.0 (2016-02-18)¶

Compatibility with Elasticsearch 2.x:

Filters have been removed and additional queries have been added. Instead of F objects you can now use Q.

Search.filter is now just a shortcut to add queries in filter context

support for pipeline aggregations added

Backwards incompatible changes:

list of analysis objects and classes was removed, any string used as tokenizer, char or token filter or analyzer will be treated as a builtin

internal method Field.to_python has been renamed to deserialize and an optional serialization mechanic for fields has been added.

Custom response class is now set by response_class method instead of a kwarg to Search.execute

Other changes:

FacetedSearch now supports pagination via slicing

0.0.10 (2016-01-24)¶

Search can now be iterated over to get back hits

Search now caches responses from Elasticsearch

DateHistogramFacet now defaults to returning empty intervals

Search no longer accepts positional parameters

Experimental MultiSearch API

added option to talk to _suggest endpoint (execute_suggest)

0.0.9 (2015-10-26)¶

FacetedSearch now uses its own Facet class instead of built in aggregations

0.0.8 (2015-08-28)¶

0.0.5 and 0.0.6 was released with broken .tar.gz on pypi, just a build fix

0.0.5 (2015-08-27)¶

added support for (index/search)_analyzer via #143, thanks @wkiser!

even keys accessed via ['field'] on AttrDict will be wrapped in Attr[Dict|List] for consistency

Added a convenient option to specify a custom doc_class to wrap inner/Nested documents

blank option has been removed

AttributeError is no longer raised when accessing an empty field.

added required flag to fields and validation hooks to fields and (sub)documents

removed get method from AttrDict. Use getattr(d, key, default) instead.

added FacetedSearch for easy declarative faceted navigation

0.0.4 (2015-04-24)¶

Metadata fields (such as id, parent, index, version etc) must be stored (and retrieved) using the meta attribute (#58) on both Result and DocType objects or using their underscored variants (_id, _parent etc)

query on Search can now be directly assigned

suggest method added to Search

Search.doc_type now accepts DocType subclasses directly

Properties.property method renamed to field for consistency

Date field now raises ValidationException on incorrect data

0.0.3 (2015-01-23)¶

Added persistence layer (Mapping and DocType), various fixes and improvements.

0.0.2 (2014-08-27)¶

Fix for python 2

0.0.1 (2014-08-27)¶

Initial release.

Elasticsearch DSL¶

Compatibility¶

Search Example¶

Persistence Example¶

Pre-built Faceted Search¶

Migration from elasticsearch-py¶

License¶

Contents¶

Configuration¶

Manual¶

Default connection¶

Multiple clusters¶

Using aliases¶

Search DSL¶

The Search object¶

Queries¶

Query combination¶

Filters¶

Aggregations¶

Sorting¶

Pagination¶

Highlighting¶

Suggestions¶

Extra properties and parameters¶

Serialization and Deserialization¶

Response¶

Hits¶

Result¶

Aggregations¶

MultiSearch¶

Persistence¶

Mappings¶

Analysis¶

DocType¶

Document life cycle¶

Search¶

class Meta options¶

Index¶

Faceted Search¶

Configuration¶

Facets¶

Advanced¶

Usage¶

Response¶

Example¶

Changelog¶

2.2.0 (2016-11-04)¶

2.1.0 (2016-06-29)¶

2.0.0 (2016-02-18)¶

0.0.10 (2016-01-24)¶

0.0.9 (2015-10-26)¶

0.0.8 (2015-08-28)¶

0.0.5 (2015-08-27)¶

0.0.4 (2015-04-24)¶

0.0.3 (2015-01-23)¶

0.0.2 (2014-08-27)¶

0.0.1 (2014-08-27)¶

Migration from `elasticsearch-py`¶

The `Search` object¶

`MultiSearch`¶

`class Meta` options¶