ELASTICSEARCH-DSL(1)

NAME
elasticsearch-dsl - Elasticsearch DSL Documentation

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.

EXAMPLES

Please see the examples directory to see some complex examples using elasticsearch-dsl.

COMPATIBILITY

The library is compatible with all Elasticsearch versions since 2.x but you have to use a matching major version:

For Elasticsearch 7.0 and later, use the major version 7 (7.x.y) of the library.

For Elasticsearch 6.0 and later, use the major version 6 (6.x.y) of the library.

For Elasticsearch 5.0 and later, use the major version 5 (5.x.y) of the library.

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

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

# Elasticsearch 7.x
elasticsearch-dsl>=7.0.0,<8.0.0
# Elasticsearch 6.x
elasticsearch-dsl>=6.0.0,<7.0.0
# Elasticsearch 5.x
elasticsearch-dsl>=5.0.0,<6.0.0
# Elasticsearch 2.x
elasticsearch-dsl>=2.0.0,<3.0.0

The development is happening on master, older branches only get bugfix releases

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
client = Elasticsearch()
s = Search(using=client, index="my-index") \
    .filter("term", category="search") \
    .query("match", title="python")   \
    .exclude("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 Document, Date, Integer, Keyword, Text
from elasticsearch_dsl.connections import connections
# Define a default Elasticsearch client
connections.create_connection(hosts=['localhost'])
class Article(Document):
    title = Text(analyzer='snowball', fields={'raw': Keyword()})
    body = Text(analyzer='snowball')
    tags = Keyword()
    published_from = Date()
    lines = Integer()
    class Index:
        name = 'blog'
        settings = {
          "number_of_shards": 2,
        }
    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 Documents 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, 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')
    }
# 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.

UPDATE BY QUERY EXAMPLE

Let's resume the simple example of articles on a blog, and let's assume that each article has a number of likes. For this example, imagine we want to increment the number of likes by 1 for all articles that match a certain tag and do not match a certain description. Writing this as a dict, we would have the following code:

from elasticsearch import Elasticsearch
client = Elasticsearch()
response = client.update_by_query(
    index="my-index",
    body={
      "query": {
        "bool": {
          "must": [{"match": {"tag": "python"}}],
          "must_not": [{"match": {"description": "beta"}}]
        }
      },
      "script"={
        "source": "ctx._source.likes++",
        "lang": "painless"
      }
    },
  )

Using the DSL, we can now express this query as such:

from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search, UpdateByQuery
client = Elasticsearch()
ubq = UpdateByQuery(using=client, index="my-index") \
      .query("match", title="python")   \
      .exclude("match", description="beta") \
      .script(source="ctx._source.likes++", lang="painless")
response = ubq.execute()

As you can see, the Update By Query object provides many of the savings offered by the Search object, and additionally allows one to update the results of the search based on a script assigned in the same manner.

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

Copyright 2013 Elasticsearch

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 to configure connections for the library. The easiest and most useful approach is to define one default connection that can be used every time an API call is made without explicitly passing in other connections.

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.

  Default connection

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

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

  Single connection with an alias

You can define the alias or name of a connection so you can easily refer to it later. The default value for alias is default.

from elasticsearch_dsl import connections
connections.create_connection(alias='my_new_connection', hosts=['localhost'], timeout=60)

Additional keyword arguments (hosts and timeout in our example) will be passed to the Elasticsearch class from elasticsearch-py.

To see all possible configuration options refer to the documentation.

  Multiple clusters

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

from elasticsearch_dsl 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.

You can alternatively define multiple connections by adding them one by one as shown in the following example:

# if you have configuration options to be passed to Elasticsearch.__init__
# this also shows creating a connection with the alias 'qa'
connections.create_connection('qa', hosts=['esqa1.example.com'], sniff_on_start=True)
# if you already have an Elasticsearch instance ready
connections.add_connection('another_qa', my_client)

  Using aliases

When using multiple connections, you can refer to them using the string alias specified when you created the connection.

This example shows how to use an alias to a connection:

s = Search(using='qa')

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

  Manual

If you don't want to supply a global configuration, you can always pass in your own connection as an instance of elasticsearch.Elasticsearch with the parameter using wherever it is accepted like this:

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'))

NOTE: When using elasticsearch_dsl, it is highly recommended that you use the built-in serializer (elasticsearch_dsl.serializer.serializer) to ensure your objects are correctly serialized into JSON every time. The create_connection method that is described here (and that the configure method uses under the hood) will do that automatically for you, unless you explicitly specify your own serializer. The built-in serializer also allows you to serialize your own objects - just define a to_dict() method on your objects and that method will be automatically called when serializing your custom objects to JSON.

  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 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)

    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())

  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()

  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:

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 address.city. 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', **{'address.city': '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()
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.

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'])])

  Aggregations

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'
#       }
#     }
#   }
# }

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 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:
        print(fragment)

  Suggestions

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.

  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})

  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.relation)
# eq
print(response.hits.total.value)
# 142
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.

For more comprehensive examples have a look at the examples directory in the repository.

  Document

If you want to create a model-like wrapper around your documents, use the Document class. It can also be used to create all the necessary mappings and settings in elasticsearch (see Document life cycle for details).

from datetime import datetime
from elasticsearch_dsl import Document, Date, Nested, Boolean, \
    analyzer, InnerDoc, Completion, Keyword, Text
html_strip = analyzer('html_strip',
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)
class Comment(InnerDoc):
    author = Text(fields={'raw': Keyword()})
    content = Text(analyzer='snowball')
    created_at = Date()
    def age(self):
        return datetime.now() - self.created_at
class Post(Document):
    title = Text()
    title_suggest = Completion()
    created_at = Date()
    published = Boolean()
    category = Text(
        analyzer=html_strip,
        fields={'raw': Keyword()}
    )
    comments = Nested(Comment)
    class Index:
        name = 'blog'
    def add_comment(self, author, content):
        self.comments.append(
          Comment(author=author, content=content, created_at=datetime.now()))
    def save(self, ** kwargs):
        self.created_at = datetime.now()
        return super().save(** kwargs)

  Data types

The Document instances should be using native python types like datetime. In case of Object or Nested fields an instance of the InnerDoc subclass should be used just like in the add_comment method in the above example where we are creating an instance of the Comment class.

There are some specific types that were created as part of this library to make working with specific field types easier, for example the Range object used in any of the range fields:

from elasticsearch_dsl import Document, DateRange, Keyword, Range
class RoomBooking(Document):
    room = Keyword()
    dates = DateRange()
rb = RoomBooking(
  room='Conference Room II',
  dates=Range(
    gte=datetime(2018, 11, 17, 9, 0, 0),
    lt=datetime(2018, 11, 17, 10, 0, 0)
  )
)
# Range supports the in operator correctly:
datetime(2018, 11, 17, 9, 30, 0) in rb.dates # True
# you can also get the limits and whether they are inclusive or exclusive:
rb.dates.lower # datetime(2018, 11, 17, 9, 0, 0), True
rb.dates.upper # datetime(2018, 11, 17, 10, 0, 0), False
# empty range is unbounded
Range().lower # None, False

  Note on dates

elasticsearch-dsl will always respect the timezone information (or lack thereof) on the datetime objects passed in or stored in Elasticsearch. Elasticsearch itself interprets all datetimes with no timezone information as UTC. If you wish to reflect this in your python code, you can specify default_timezone when instantiating a Date field:

class Post(Document):
    created_at = Date(default_timezone='UTC')

In that case any datetime object passed in (or parsed from elasticsearch) will be treated as if it were in UTC timezone.

  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()

This code will typically be run in the setup for your application during a code deploy, similar to running database migrations.

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, 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
print(post.meta.id)
# override default 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()

The Update API can also be used via the update method. By default any keyword arguments, beyond the parameters of the API, will be considered fields with new values. Those fields will be updated on the local copy of the document and then sent over as partial document to be updated:

# retrieve the document
first = Post.get(id=42)
# you can update just individual fields which will call the update API
# and also update the document in place
first.update(published=True, published_by='me')

In case you wish to use a painless script to perform the update you can pass in the script string as script or the id of a stored script via script_id. All additional keyword arguments to the update method will then be passed in as parameters of the script. The document will not be updated in place.

# retrieve the document
first = Post.get(id=42)
# we execute a script in elasticsearch with additional kwargs being passed
# as params into the script
first.update(script='ctx._source.category.add(params.new_category)',
             new_category='testing')

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 retrieve 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.

    The index associated with the Document is accessible via the _index class property which gives you access to the Index class.

    The _index attribute is also home to the load_mappings method which will update the mapping on the Index 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._index.load_mappings()

    To delete a document just call its delete method:

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

  Analysis

To specify analyzer values for Text 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 our example earlier:

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).

Once you have an instance of a custom analyzer you can also call the analyze API on it by using the simulate method:

response = my_analyzer.simulate('Hello World!')
# ['hel', 'ell', 'llo', 'lo ', 'o w', ' wo', 'wor', 'orl', 'rld', 'ld!']
tokens = [t.token for t in response.tokens]

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

  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 post 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 Document 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'})
response = s.execute()
for result in response.suggest.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:

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, dynamic 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(Document):
        title = Text()
        class Meta:
            all = MetaField(enabled=False)
            dynamic = MetaField('strict')

  class Index options

This section of the Document definition can contain any information about the index, its name, settings and other attributes:

name
    name of the index to use, if it contains a wildcard (*) then it cannot be used for any write operations and an index kwarg will have to be passed explicitly when calling methods like .save().

using
    default connection alias to use, defaults to 'default'

settings
    dictionary containing any settings for the Index object like number_of_shards.

analyzers
    additional list of analyzers that should be defined on an index (see Analysis for details).

aliases
    dictionary with any aliases definitions

  Document Inheritance

You can use standard Python inheritance to extend models, this can be useful in a few scenarios. For example if you want to have a BaseDocument defining some common fields that several different Document classes should share:

class User(InnerDoc):
    username = Text(fields={'keyword': Keyword()})
    email = Text()
class BaseDocument(Document):
    created_by = Object(User)
    created_date = Date()
    last_updated = Date()
    def save(**kwargs):
        if not self.created_date:
            self.created_date = datetime.now()
        self.last_updated = datetime.now()
        return super(BaseDocument, self).save(**kwargs)
class BlogPost(BaseDocument):
    class Index:
        name = 'blog'

Another use case would be using the join type to have multiple different entities in a single index. You can see an example of this approach. Note that in this case, if the subclasses don't define their own Index classes, the mappings are merged and shared between all the subclasses.

  Index

In typical scenario using class Index on a Document class is sufficient to perform any action. In a few cases though it can be useful to manipulate an Index object directly.

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, Document, Text, 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 document with the index
blogs.document(Post)
# can also be used as class decorator when defining the Document
@blogs.document
class Post(Document):
    title = Text()
# You can attach custom analyzers to the index
html_strip = analyzer('html_strip',
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)
blogs.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.document(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)

  IndexTemplate

elasticsearch-dsl also exposes an option to manage index templates in elasticsearch using the IndexTemplate class which has very similar API to Index.

Once an index template is saved in elasticsearch it's contents will be automatically applied to new indices (existing indices are completely unaffected by templates) that match the template pattern (any index starting with blogs- in our example), even if the index is created automatically upon indexing a document into that index.

Potential workflow for a set of time based indices governed by a single template:

from datetime import datetime
from elasticsearch_dsl import Document, Date, Text
class Log(Document):
    content = Text()
    timestamp = Date()
    class Index:
        name = "logs-*"
        settings = {
          "number_of_shards": 2
        }
    def save(self, **kwargs):
        # assign now if no timestamp given
        if not self.timestamp:
            self.timestamp = datetime.now()
        # override the index to go to the proper timeslot
        kwargs['index'] = self.timestamp.strftime('logs-%Y%m%d')
        return super().save(**kwargs)
# once, as part of application setup, during deploy/migrations:
logs = Log._index.as_template('logs', order=0)
logs.save()
# to perform search across all logs:
search = Log.search()

  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 Document 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')}

sort
    tuple or list of fields on which the results should be sorted. The format of the individual fields are to be the same as those passed to sort().

  Facets

There are several different facets available:

TermsFacet
    provides an option to split documents into groups based on a value of a field, for example TermsFacet(field='category')

DateHistogramFacet
    split documents into time intervals, example: DateHistogramFacet(field="published_date", calendar_interval="day")

HistogramFacet
    similar to DateHistogramFacet but for numerical values: HistogramFacet(field="rating", interval=2)

RangeFacet
    allows you to define your own ranges for a numerical fields: RangeFacet(field="comment_count", ranges=[("few", (None, 2)), ("lots", (2, None))])

NestedFacet
    is just a simple facet that wraps another to provide access to nested documents: NestedFacet('variants', TermsFacet(field='variants.color'))

    By default facet results will only calculate document count, if you wish for a different metric you can pass in any single value metric aggregation as the metric kwarg (TermsFacet(field='tags', metric=A('max', field=timestamp))). When specifying metric the results will be, by default, sorted in descending order by that metric. To change it to ascending specify metric_sort="asc" and to just sort by document count use metric_sort=False.

  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 position 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
total = response.hits.total
print('total hits', total.relation, total.value)
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)

  Update By Query

  The Update By Query object

The Update By Query object enables the use of the _update_by_query endpoint to perform an update on documents that match a search query.

The object is implemented as a modification of the Search object, containing a subset of its query methods, as well as a script method, which is used to make updates.

The Update By Query object implements the following Search query types:

•   queries

•   filters

•   excludes

    For more information on queries, see the search_dsl chapter.

    Like the Search object, the API is designed to be chainable. This means that the Update By Query 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 Update By Query object to foreign code without fear of it modifying your objects as long as it sticks to the Update By Query object APIs.

    You can define your client in a number of ways, but the preferred method is to use a global configuration. For more information on defining a client, see the configuration chapter.

    Once your client is defined, you can instantiate a copy of the Update By Query object as seen below:

    from elasticsearch_dsl import UpdateByQuery
    ubq = UpdateByQuery().using(client)
    # or
    ubq = UpdateByQuery(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:

    ubq = UpdateByQuery().using(client).query("match", title="python")

    To send the request to Elasticsearch:

    response = ubq.execute()

    It should be noted, that there are limits to the chaining using the script method: calling script multiple times will overwrite the previous value. That is, only a single script can be sent with a call. An attempt to use two scripts will result in only the second script being stored.

    Given the below example:

    ubq = UpdateByQuery().using(client).script(source="ctx._source.likes++").script(source="ctx._source.likes+=2")

    This means that the stored script by this client will be 'source': 'ctx._source.likes+=2' and the previous call will not be stored.

    For debugging purposes you can serialize the Update By Query object to a dict explicitly:

    print(ubq.to_dict())

    Also, to use variables in script see below example:

    ubq.script(
      source="ctx._source.messages.removeIf(x -> x.somefield == params.some_var)",
      params={
        'some_var': 'some_string_val'
      }
    )

  Serialization and Deserialization

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

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

ubq = UpdateByQuery.from_dict({"query": {"match": {"title": "python"}}})

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

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

  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:

ubq = ubq.extra(explain=True)

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

ubq = ubq.params(routing="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 = ubq.execute()
print(response.success())
# True
print(response.took)
# 12

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.

  API Documentation

Below please find the documentation for the public classes and functions of elasticsearch_dsl.

  Search

class elasticsearch_dsl.Search(**kwargs)
    Search request to elasticsearch.

Parameters

•   using -- Elasticsearch instance to use

•   index -- limit the search to index

•   doc_type -- only query this type.

    All the parameters supplied (or omitted) at creation type can be later overridden by methods (using, index and doc_type respectively).

count()
    Return the number of hits matching the query and filters. Note that only the actual number is returned.

delete() executes the query by delegating to delete_by_query()

execute(ignore_cache=False)
    Execute the search and return an instance of Response wrapping all the data.

Parameters
    ignore_cache -- if set to True, consecutive calls will hit ES, while cached result will be ignored. Defaults to False

classmethod from_dict(d)
    Construct a new Search instance from a raw dict containing the search body. Useful when migrating from raw dictionaries.

    Example:

    s = Search.from_dict({
        "query": {
            "bool": {
                "must": [...]
            }
        },
        "aggs": {...}
    })
    s = s.filter('term', published=True)

highlight(*fields, **kwargs)
    Request highlighting of some fields. All keyword arguments passed in will be used as parameters for all the fields in the fields parameter. Example:

    Search().highlight('title', 'body', fragment_size=50)

    will produce the equivalent of:

    {
        "highlight": {
            "fields": {
                "body": {"fragment_size": 50},
                "title": {"fragment_size": 50}
            }
        }
    }

    If you want to have different options for different fields you can call highlight twice:

    Search().highlight('title', fragment_size=50).highlight('body', fragment_size=100)

    which will produce:

    {
        "highlight": {
            "fields": {
                "body": {"fragment_size": 100},
                "title": {"fragment_size": 50}
            }
        }
    }

highlight_options(**kwargs)
    Update the global highlighting options used for this request. For example:

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

response_class(cls)
    Override the default wrapper used for the response.

scan()
    Turn the search into a scan search and return a generator that will iterate over all the documents matching the query.

    Use params method to specify any additional arguments you with to pass to the underlying scan helper from elasticsearch-py - https://elasticsearch-py.readthedocs.io/en/master/helpers.html#elasticsearch.helpers.scan

script_fields(**kwargs)
    Define script fields to be calculated on hits. See https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-script-fields.html for more details.

    Example:

    s = Search()
    s = s.script_fields(times_two="doc['field'].value * 2")
    s = s.script_fields(
        times_three={
            'script': {
                'lang': 'painless',
                'source': "doc['field'].value * params.n",
                'params': {'n': 3}
            }
        }
    )

sort(*keys)
    Add sorting information to the search request. If called without arguments it will remove all sort requirements. Otherwise it will replace them. Acceptable arguments are:

    'some.field'
    '-some.other.field'
    {'different.field': {'any': 'dict'}}

    so for example:

    s = Search().sort(
        'category',
        '-title',
        {"price" : {"order" : "asc", "mode" : "avg"}}
    )

    will sort by category, title (in descending order) and price in ascending order using the avg mode.

    The API returns a copy of the Search object and can thus be chained.

source(fields=None, **kwargs)
    Selectively control how the _source field is returned.

Parameters
    fields -- wildcard string, array of wildcards, or dictionary of includes and excludes

    If fields is None, the entire document will be returned for each hit. If fields is a dictionary with keys of 'includes' and/or 'excludes' the fields will be either included or excluded appropriately.

    Calling this multiple times with the same named parameter will override the previous values with the new ones.

    Example:

    s = Search()
    s = s.source(includes=['obj1.*'], excludes=["*.description"])
    s = Search()
    s = s.source(includes=['obj1.*']).source(excludes=["*.description"])

suggest(name, text, **kwargs)
    Add a suggestions request to the search.

Parameters

•   name -- name of the suggestion

•   text -- text to suggest on

    All keyword arguments will be added to the suggestions body. For example:

    s = Search()
    s = s.suggest('suggestion-1', 'Elasticsearch', term={'field': 'body'})

to_dict(count=False, **kwargs)
    Serialize the search into the dictionary that will be sent over as the request's body.

Parameters
    count -- a flag to specify if we are interested in a body for count - no aggregations, no pagination bounds etc.

    All additional keyword arguments will be included into the dictionary.

update_from_dict(d)
    Apply options from a serialized body to the current instance. Modifies the object in-place. Used mostly by from_dict.

class elasticsearch_dsl.MultiSearch(**kwargs)
    Combine multiple Search objects into a single request.

add(search)
    Adds a new Search object to the request:

    ms = MultiSearch(index='my-index')
    ms = ms.add(Search(doc_type=Category).filter('term', category='python'))
    ms = ms.add(Search(doc_type=Blog))

execute(ignore_cache=False, raise_on_error=True)
    Execute the multi search request and return a list of search results.

  Document

class elasticsearch_dsl.Document(meta=None, **kwargs)
    Model-like class for persisting documents in elasticsearch.

delete(using=None, index=None, **kwargs)
    Delete the instance in elasticsearch.

Parameters

•   index -- elasticsearch index to use, if the Document is associated with an index this can be omitted.

•   using -- connection alias to use, defaults to 'default'

    Any additional keyword arguments will be passed to Elasticsearch.delete unchanged.

classmethod exists(id, using=None, index=None, **kwargs)
    check if exists a single document from elasticsearch using its id.

Parameters

•   id -- id of the document to check if exists

•   index -- elasticsearch index to use, if the Document is associated with an index this can be omitted.

•   using -- connection alias to use, defaults to 'default'

    Any additional keyword arguments will be passed to Elasticsearch.exists unchanged.

classmethod get(id, using=None, index=None, **kwargs)
    Retrieve a single document from elasticsearch using its id.

Parameters

•   id -- id of the document to be retrieved

•   index -- elasticsearch index to use, if the Document is associated with an index this can be omitted.

•   using -- connection alias to use, defaults to 'default'

    Any additional keyword arguments will be passed to Elasticsearch.get unchanged.

classmethod init(index=None, using=None)
    Create the index and populate the mappings in elasticsearch.

classmethod mget(docs, using=None, index=None, raise_on_error=True, missing='none', **kwargs)
    Retrieve multiple document by their ids. Returns a list of instances in the same order as requested.

Parameters

•   docs -- list of ids of the documents to be retrieved or a list of document specifications as per https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-multi-get.html

•   index -- elasticsearch index to use, if the Document is associated with an index this can be omitted.

•   using -- connection alias to use, defaults to 'default'

•   missing -- what to do when one of the documents requested is not found. Valid options are 'none' (use None), 'raise' (raise NotFoundError) or 'skip' (ignore the missing document).

    Any additional keyword arguments will be passed to Elasticsearch.mget unchanged.

save(using=None, index=None, validate=True, skip_empty=True, return_doc_meta=False, **kwargs)
    Save the document into elasticsearch. If the document doesn't exist it is created, it is overwritten otherwise. Returns True if this operations resulted in new document being created.

Parameters

•   index -- elasticsearch index to use, if the Document is associated with an index this can be omitted.

•   using -- connection alias to use, defaults to 'default'

•   validate -- set to False to skip validating the document

•   skip_empty -- if set to False will cause empty values (None, [], {}) to be left on the document. Those values will be stripped out otherwise as they make no difference in elasticsearch.

•   return_doc_meta -- set to True to return all metadata from the update API call instead of only the operation result

    Any additional keyword arguments will be passed to Elasticsearch.index unchanged.

    :return operation result created/updated

classmethod search(using=None, index=None)
    Create an Search instance that will search over this Document.

to_dict(include_meta=False, skip_empty=True)
    Serialize the instance into a dictionary so that it can be saved in elasticsearch.

Parameters

•   include_meta -- if set to True will include all the metadata (_index, _id etc). Otherwise just the document's data is serialized. This is useful when passing multiple instances into elasticsearch.helpers.bulk.

•   skip_empty -- if set to False will cause empty values (None, [], {}) to be left on the document. Those values will be stripped out otherwise as they make no difference in elasticsearch.

update(using=None, index=None, detect_noop=True, doc_as_upsert=False, refresh=False, retry_on_conflict=None, script=None, script_id=None, scripted_upsert=False, upsert=None, return_doc_meta=False, **fields)
    Partial update of the document, specify fields you wish to update and both the instance and the document in elasticsearch will be updated:

    doc = MyDocument(title='Document Title!')
    doc.save()
    doc.update(title='New Document Title!')

Parameters

•   index -- elasticsearch index to use, if the Document is associated with an index this can be omitted.

•   using -- connection alias to use, defaults to 'default'

•   detect_noop -- Set to False to disable noop detection.

•   refresh -- Control when the changes made by this request are visible to search. Set to True for immediate effect.

•   retry_on_conflict -- In between the get and indexing phases of the update, it is possible that another process might have already updated the same document. By default, the update will fail with a version conflict exception. The retry_on_conflict parameter controls how many times to retry the update before finally throwing an exception.

•   doc_as_upsert -- Instead of sending a partial doc plus an upsert doc, setting doc_as_upsert to true will use the contents of doc as the upsert value

•   return_doc_meta -- set to True to return all metadata from the index API call instead of only the operation result

    :return operation result noop/updated

  Index

class elasticsearch_dsl.Index(name, using='default')

Parameters

•   name -- name of the index

•   using -- connection alias to use, defaults to 'default'

aliases(**kwargs)
    Add aliases to the index definition:

    i = Index('blog-v2')
    i.aliases(blog={}, published={'filter': Q('term', published=True)})

analyze(using=None, **kwargs)
    Perform the analysis process on a text and return the tokens breakdown of the text.

    Any additional keyword arguments will be passed to Elasticsearch.indices.analyze unchanged.

analyzer(*args, **kwargs)
    Explicitly add an analyzer to an index. Note that all custom analyzers defined in mappings will also be created. This is useful for search analyzers.

    Example:

    from elasticsearch_dsl import analyzer, tokenizer
    my_analyzer = analyzer('my_analyzer',
        tokenizer=tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
        filter=['lowercase']
    )
    i = Index('blog')
    i.analyzer(my_analyzer)

clear_cache(using=None, **kwargs)
    Clear all caches or specific cached associated with the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.clear_cache unchanged.

clone(name=None, using=None)
    Create a copy of the instance with another name or connection alias. Useful for creating multiple indices with shared configuration:

    i = Index('base-index')
    i.settings(number_of_shards=1)
    i.create()
    i2 = i.clone('other-index')
    i2.create()

Parameters

•   name -- name of the index

•   using -- connection alias to use, defaults to 'default'

close(using=None, **kwargs)
    Closes the index in elasticsearch.

    Any additional keyword arguments will be passed to Elasticsearch.indices.close unchanged.

create(using=None, **kwargs)
    Creates the index in elasticsearch.

    Any additional keyword arguments will be passed to Elasticsearch.indices.create unchanged.

delete(using=None, **kwargs)
    Deletes the index in elasticsearch.

    Any additional keyword arguments will be passed to Elasticsearch.indices.delete unchanged.

delete_alias(using=None, **kwargs)
    Delete specific alias.

    Any additional keyword arguments will be passed to Elasticsearch.indices.delete_alias unchanged.

document(document)
    Associate a Document subclass with an index. This means that, when this index is created, it will contain the mappings for the Document. If the Document class doesn't have a default index yet (by defining class Index), this instance will be used. Can be used as a decorator:

    i = Index('blog')
    @i.document
    class Post(Document):
        title = Text()
    # create the index, including Post mappings
    i.create()
    # .search() will now return a Search object that will return
    # properly deserialized Post instances
    s = i.search()

exists(using=None, **kwargs)
    Returns True if the index already exists in elasticsearch.

    Any additional keyword arguments will be passed to Elasticsearch.indices.exists unchanged.

exists_alias(using=None, **kwargs)
    Return a boolean indicating whether given alias exists for this index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.exists_alias unchanged.

exists_type(using=None, **kwargs)
    Check if a type/types exists in the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.exists_type unchanged.

flush(using=None, **kwargs)
    Performs a flush operation on the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.flush unchanged.

flush_synced(using=None, **kwargs)
    Perform a normal flush, then add a generated unique marker (sync_id) to all shards.

    Any additional keyword arguments will be passed to Elasticsearch.indices.flush_synced unchanged.

forcemerge(using=None, **kwargs)
    The force merge API allows to force merging of the index through an API. The merge relates to the number of segments a Lucene index holds within each shard. The force merge operation allows to reduce the number of segments by merging them.

    This call will block until the merge is complete. If the http connection is lost, the request will continue in the background, and any new requests will block until the previous force merge is complete.

    Any additional keyword arguments will be passed to Elasticsearch.indices.forcemerge unchanged.

get(using=None, **kwargs)
    The get index API allows to retrieve information about the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.get unchanged.

get_alias(using=None, **kwargs)
    Retrieve a specified alias.

    Any additional keyword arguments will be passed to Elasticsearch.indices.get_alias unchanged.

get_field_mapping(using=None, **kwargs)
    Retrieve mapping definition of a specific field.

    Any additional keyword arguments will be passed to Elasticsearch.indices.get_field_mapping unchanged.

get_mapping(using=None, **kwargs)
    Retrieve specific mapping definition for a specific type.

    Any additional keyword arguments will be passed to Elasticsearch.indices.get_mapping unchanged.

get_settings(using=None, **kwargs)
    Retrieve settings for the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.get_settings unchanged.

get_upgrade(using=None, **kwargs)
    Monitor how much of the index is upgraded.

    Any additional keyword arguments will be passed to Elasticsearch.indices.get_upgrade unchanged.

mapping(mapping)
    Associate a mapping (an instance of Mapping) with this index. This means that, when this index is created, it will contain the mappings for the document type defined by those mappings.

open(using=None, **kwargs)
    Opens the index in elasticsearch.

    Any additional keyword arguments will be passed to Elasticsearch.indices.open unchanged.

put_alias(using=None, **kwargs)
    Create an alias for the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.put_alias unchanged.

put_mapping(using=None, **kwargs)
    Register specific mapping definition for a specific type.

    Any additional keyword arguments will be passed to Elasticsearch.indices.put_mapping unchanged.

put_settings(using=None, **kwargs)
    Change specific index level settings in real time.

    Any additional keyword arguments will be passed to Elasticsearch.indices.put_settings unchanged.

recovery(using=None, **kwargs)
    The indices recovery API provides insight into on-going shard recoveries for the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.recovery unchanged.

refresh(using=None, **kwargs)
    Performs a refresh operation on the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.refresh unchanged.

save(using=None)
    Sync the index definition with elasticsearch, creating the index if it doesn't exist and updating its settings and mappings if it does.

    Note some settings and mapping changes cannot be done on an open index (or at all on an existing index) and for those this method will fail with the underlying exception.

search(using=None)
    Return a Search object searching over the index (or all the indices belonging to this template) and its Documents.

segments(using=None, **kwargs)
    Provide low level segments information that a Lucene index (shard level) is built with.

    Any additional keyword arguments will be passed to Elasticsearch.indices.segments unchanged.

settings(**kwargs)
    Add settings to the index:

    i = Index('i')
    i.settings(number_of_shards=1, number_of_replicas=0)

    Multiple calls to settings will merge the keys, later overriding the earlier.

shard_stores(using=None, **kwargs)
    Provides store information for shard copies of the index. Store information reports on which nodes shard copies exist, the shard copy version, indicating how recent they are, and any exceptions encountered while opening the shard index or from earlier engine failure.

    Any additional keyword arguments will be passed to Elasticsearch.indices.shard_stores unchanged.

shrink(using=None, **kwargs)
    The shrink index API allows you to shrink an existing index into a new index with fewer primary shards. The number of primary shards in the target index must be a factor of the shards in the source index. For example an index with 8 primary shards can be shrunk into 4, 2 or 1 primary shards or an index with 15 primary shards can be shrunk into 5, 3 or 1. If the number of shards in the index is a prime number it can only be shrunk into a single primary shard. Before shrinking, a (primary or replica) copy of every shard in the index must be present on the same node.

    Any additional keyword arguments will be passed to Elasticsearch.indices.shrink unchanged.

stats(using=None, **kwargs)
    Retrieve statistics on different operations happening on the index.

    Any additional keyword arguments will be passed to Elasticsearch.indices.stats unchanged.

updateByQuery(using=None)
    Return a UpdateByQuery object searching over the index (or all the indices belonging to this template) and updating Documents that match the search criteria.

    For more information, see here: https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-update-by-query.html

upgrade(using=None, **kwargs)
    Upgrade the index to the latest format.

    Any additional keyword arguments will be passed to Elasticsearch.indices.upgrade unchanged.

validate_query(using=None, **kwargs)
    Validate a potentially expensive query without executing it.

    Any additional keyword arguments will be passed to Elasticsearch.indices.validate_query unchanged.

  Faceted Search

class elasticsearch_dsl.FacetedSearch(query=None, filters={}, sort=())
    Abstraction for creating faceted navigation searches that takes care of composing the queries, aggregations and filters as needed as well as presenting the results in an easy-to-consume fashion:

    class BlogSearch(FacetedSearch):
        index = 'blogs'
        doc_types = [Blog, Post]
        fields = ['title^5', 'category', 'description', 'body']
        facets = {
            'type': TermsFacet(field='_type'),
            'category': TermsFacet(field='category'),
            'weekly_posts': DateHistogramFacet(field='published_from', interval='week')
        }
        def search(self):
            ' Override search to add your own filters '
            s = super(BlogSearch, self).search()
            return s.filter('term', published=True)
    # when using:
    blog_search = BlogSearch("web framework", filters={"category": "python"})
    # supports pagination
    blog_search[10:20]
    response = blog_search.execute()
    # easy access to aggregation results:
    for category, hit_count, is_selected in response.facets.category:
        print(
            "Category %s has %d hits%s." % (
                category,
                hit_count,
                ' and is chosen' if is_selected else ''
            )
        )

Parameters

•   query -- the text to search for

•   filters -- facet values to filter

•   sort -- sort information to be passed to Search

add_filter(name, filter_values)
    Add a filter for a facet.

aggregate(search)
    Add aggregations representing the facets selected, including potential filters.

build_search()
    Construct the Search object.

execute()
    Execute the search and return the response.

filter(search)
    Add a post_filter to the search request narrowing the results based on the facet filters.

highlight(search)
    Add highlighting for all the fields

query(search, query)
    Add query part to search.

    Override this if you wish to customize the query used.

search()
    Returns the base Search object to which the facets are added.

    You can customize the query by overriding this method and returning a modified search object.

sort(search)
    Add sorting information to the request.

  Update By Query

class elasticsearch_dsl.UpdateByQuery(**kwargs)
    Update by query request to elasticsearch.

Parameters

•   using -- Elasticsearch instance to use

•   index -- limit the search to index

•   doc_type -- only query this type.

    All the parameters supplied (or omitted) at creation type can be later overridden by methods (using, index and doc_type respectively).

execute()
    Execute the search and return an instance of Response wrapping all the data.

classmethod from_dict(d)
    Construct a new UpdateByQuery instance from a raw dict containing the search body. Useful when migrating from raw dictionaries.

    Example:

    ubq = UpdateByQuery.from_dict({
        "query": {
            "bool": {
                "must": [...]
            }
        },
        "script": {...}
    })
    ubq = ubq.filter('term', published=True)

response_class(cls)
    Override the default wrapper used for the response.

script(**kwargs)
    Define update action to take: https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-scripting-using.html for more details.

    Note: the API only accepts a single script, so calling the script multiple times will overwrite.

    Example:

    ubq = Search()
    ubq = ubq.script(source="ctx._source.likes++"")
    ubq = ubq.script(source="ctx._source.likes += params.f"",
                 lang="expression",
                 params={'f': 3})

to_dict(**kwargs)
    Serialize the search into the dictionary that will be sent over as the request'ubq body.

    All additional keyword arguments will be included into the dictionary.

update_from_dict(d)
    Apply options from a serialized body to the current instance. Modifies the object in-place. Used mostly by from_dict.

  Mappings

If you wish to create mappings manually you can use the Mapping class, for more advanced use cases, however, we recommend you use the doc_type abstraction in combination with index (or index-template) to define index-level settings and properties. The mapping definition follows a similar pattern to the query dsl:

from elasticsearch_dsl import Keyword, Mapping, Nested, Text
# name your type
m = Mapping()
# add fields
m.field('title', 'text')
# you can use multi-fields easily
m.field('category', 'text', fields={'raw': Keyword()})
# you can also create a field manually
comment = Nested(
                 properties={
                    'author': Text(),
                    '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', Keyword(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', 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.

  Contribution Guide

If you have a bugfix or new feature that you would like to contribute to elasticsearch-dsl-py, please find or open an issue about it first. Talk about what you would like to do. It may be that somebody is already working on it, or that there are particular issues that you should know about before implementing the change.

If you want to be rewarded for your contributions, sign up for the Elastic Contributor Program. Each time you make a valid contribution, you’ll earn points that increase your chances of winning prizes and being recognized as a top contributor.

We enjoy working with contributors to get their code accepted. There are many approaches to fixing a problem and it is important to find the best approach before writing too much code.

The process for contributing to any of the Elasticsearch repositories is similar.

1.  Please make sure you have signed the Contributor License Agreement. We are not asking you to assign copyright to us, but to give us the right to distribute your code without restriction. We ask this of all contributors in order to assure our users of the origin and continuing existence of the code. You only need to sign the CLA once.

2.  Run the test suite to ensure your changes do not break existing code:

    $ nox -rs lint test

3.  Rebase your changes. Update your local repository with the most recent code from the main elasticsearch-dsl-py repository, and rebase your branch on top of the latest master branch. We prefer your changes to be squashed into a single commit.

4.  Submit a pull request. Push your local changes to your forked copy of the repository and submit a pull request. In the pull request, describe what your changes do and mention the number of the issue where discussion has taken place, eg “Closes #123″. Please consider adding or modifying tests related to your changes.

    Then sit back and wait. There will probably be discussion about the pull request and, if any changes are needed, we would love to work with you to get your pull request merged into elasticsearch-dsl-py.

  Changelog

  7.4.0 (2021-07-15)

•   Added the ConstantKeyword, RankFeatures field types (#1456, #1465)

•   Added the ScriptScore query type (#1464)

•   Added UpdateByQueryResponse.success() method (#1463)

•   Added return_doc_meta parameter to Document.save() and Document.update() for accessing the complete API response (#1466)

•   Added support for calendar_interval and fixed_interval to DateHistogramFacet (#1467)

•   Added Document.exists() method (#1447, contributed by @dem4ply)

•   Added support for the year interval to DateHistogramFacet (#1502, contributed by @nrsimha)

•   Fixed issue where to_dict() should be called recursively on Search.extras and **kwargs (#1458)

•   Fixed inverse of an empty Bool query should be MatchNone (#1459)

•   Fixed issue between retry_on_conflict and optimistic concurrency control within Document.update() (#1461, contributed by @armando1793)

  7.3.0 (2020-09-16)

•   Added Intervals, MatchBoolPrefix, Shape, and Wrapper queries (#1392, #1418)

•   Added Boxplot, RareTerms, VariableWidthHistogram, MedianAbsoluteDeviation, TTest, CumulativeCardinality, Inference, MovingPercentiles, and Normalize aggregations (#1416, #1418)

•   Added __all__ and removed all star imports from elasticsearch_dsl namespace to avoid leaking unintended names (#1390)

•   Fixed an issue where Object and Nested could mutate the inner doc_class mapping (#1255, contributed by @l1nd3r0th)

•   Fixed a typo in query SpanContaining, previously was SpanContainining (#1418)

  7.2.1 (2020-06-02)

•   Fixed issue when slicing a Search that would result in a negative size instead of a size of 0. (#1360, contributed by @bk-equityzen)

  7.2.0 (2020-05-04)

•   Added support for geotile_grid aggregation (#1350, contributed by @owrcasstevens)

•   Added the DenseVector and SparseVector data types (#1278)

•   Added the SearchAsYouType field (#1295, contributed by @dpasqualin)

•   Fixed name of DoubleRange (#1272, contributed by @braunsonm)

  7.1.0 (2019-10-23)

•   Optimistic concurrent control for Document.delete

•   Removing deprecated DocType

•   Proper count caching for ES 7.x

•   Support for multiplexer token filter

•   Don't substitute for __ in FacetedSearch

  7.0.0 (2019-04-26)

•   Compatibility with Elasticsearch 7.x

•   Document.save() now returns "created" or "updated"

•   Dropped support for Python 2.6, 3.2, and 3.3

•   When using fields the values are no longer merged into the body of the document and have to be accessed via .meta.fields only

  6.4.0 (2019-04-26)

•   Index.document now correctly sets the Document's _index only when using default index (#1091)

•   Document inheritance allows overriding Object and Nested field metadata like dynamic

•   adding auto_date_histogram aggregation

•   Do not change data in place when (de)serializing

  6.3.1 (2018-12-05)

•   Analyzer.simulate now supports built-in analyzers

•   proper (de)serialization of the Range wrapper

•   Added search_analyzer to Completion field

  6.3.0 (2018-11-21)

•   Fixed logic around defining a different doc_type name.

•   Added retry_on_conflict parameter to Document.update.

•   fields defined on an index are now used to (de)serialize the data even when not defined on a Document

•   Allow Index.analyzer to construct the analyzer

•   Detect conflict in analyzer definitions when calling Index.analyzer

•   Detect conflicting mappings when creating an index

•   Add simulate method to analyzer object to test the analyzer using the _analyze API.

•   Add script and script_id options to Document.update

•   Facet can now use other metric than doc_count

•   Range objects to help with storing and working with _range fields

•   Improved behavior of Index.save where it does a better job when index already exists

•   Composite aggregations now correctly support multiple sources aggs

•   UpdateByQuery implemented by @emarcey

  6.2.1 (2018-07-03)

•   allow users to redefine doc_type in Index (#929)

•   include DocType in elasticsearch_dsl module directly (#930)

  6.2.0 (2018-07-03)

Backwards incompatible change - DocType refactoring.

In 6.2.0 we refactored the DocType class and renamed it to Document. The primary motivation for this was the support for types being dropped from elasticsearch itself in 7.x - we needed to somehow link the Index and Document classes. To do this we split the options that were previously defined in the class Meta between it and newly introduced class Index. The split is that all options that were tied to mappings (like setting dynamic = MetaField('strict')) remain in class Meta and all options for index definition (like settings, name, or aliases) got moved to the new class Index.

You can see some examples of the new functionality in the examples directory. Documentation has been updated to reflect the new API.

DocType is now just an alias for Document which will be removed in 7.x. It does, however, work in the new way which is not fully backwards compatible.

•   Percolator field now expects Query objects as values

•   you can no longer access meta fields on a Document instance by specifying ._id or similar. Instead all access needs to happen via the .meta attribute.

•   Implemented NestedFacet for FacetedSearch. This brought a need to slightly change the semantics of Facet.get_values which now expects the whole data dict for the aggregation, not just the buckets. This is a backwards incompatible change for custom aggregations that redefine that method.

•   Document.update now supports refresh kwarg

•   DslBase._clone now produces a shallow copy, this means that modifying an existing query can have effects on existing Search objects.

•   Empty Search no longer defaults to match_all query and instead leaves the query key empty. This is backwards incompatible when using suggest.

  6.1.0 (2018-01-09)

•   Removed String field.

•   Fixed issue with Object/Nested deserialization

  6.0.1 (2018-01-02)

Fixing wheel package for Python 2.7 (#803)

  6.0.0 (2018-01-01)

Backwards incompatible release compatible with elasticsearch 6.0, changes include:

•   use doc as default DocType name, this change includes: * DocType._doc_type.matches method is now used to determine which DocType should be used for a hit instead of just checking _type

•   Nested and Object field refactoring using newly introduced InnerDoc class. To define a Nested/Object field just define the InnerDoc subclass and then use it when defining the field:

    class Comment(InnerDoc):
        body = Text()
        created_at = Date()
    class Blog(DocType):
        comments = Nested(Comment)

•   methods on connections singleton are now exposed on the connections module directly.

•   field values are now only deserialized when coming from elasticsearch (via from_es method) and not when assigning values in python (either by direct assignment or in __init__).

  5.4.0 (2017-12-06)

•   fix ip_range aggregation and rename the class to IPRange. Iprange is kept for bw compatibility

•   fix bug in loading an aggregation with meta data from dict

•   add support for normalizer parameter of Keyword fields

•   IndexTemplate can now be specified using the same API as Index

•   Boolean field now accepts "false" as False

  5.3.0 (2017-05-18)

•   fix constant score query definition

•   DateHistogramFacet now works with datetime objects

•   respect __ in field names when creating queries from dict

  5.2.0 (2017-03-26)

•   make sure all response structers are pickleable (for caching)

•   adding exclude to Search

•   fix metric aggregation deserialization

•   expose all index-level APIs on Index class

•   adding delete to Search which calls delete_by_query API

  5.1.0 (2017-01-08)

•   Renamed Result and ResultMeta to Hit and HitMeta respectively

•   Response now stores Search which it gets as first arg to __init__

•   aggregation results are now wrapped in classes and properly deserialized

•   Date fields now allow for numerical timestamps in the java format (in millis)

•   Added API documentation

•   replaced generated classes with manually created

  5.0.0 (2016-11-04)

Version compatible with elasticsearch 5.0.

Breaking changes:

•   String field type has been deprecated in favor of Text and Keyword

•   fields method has been removed in favor of source filtering

  2.2.0 (2016-11-04)

•   accessing missing string fields no longer returned '' 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.

AUTHOR
Elasticsearch B.V

COPYRIGHT
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