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 use native python types like str and datetime. In case of Object or Nested fields an instance of the InnerDoc subclass is used, as 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 some 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 

Python Type Hints

Document fields can be defined using standard Python type hints if desired. Here are some simple examples:

from typing import Optional class Post(Document): title: str # same as title = Text(required=True) created_at: Optional[datetime] # same as created_at = Date(required=False) published: bool # same as published = Boolean(required=True) 

It is important to note that when using Field subclasses such as Text, Date and Boolean, they must be given in the right-side of an assignment, as shown in examples above. Using these classes as type hints will result in errors.

Python types are mapped to their corresponding field type according to the following table:

Python type to DSL field mappings
Python type	DSL field
`str`	`Text(required=True)`
`bool`	`Boolean(required=True)`
`int`	`Integer(required=True)`
`float`	`Float(required=True)`
`bytes`	`Binary(required=True)`
`datetime`	`Date(required=True)`
`date`	`Date(format="yyyy-MM-dd", required=True)`

To type a field as optional, the standard Optional modifier from the Python typing package can be used. When using Python 3.10 or newer, “pipe” syntax can also be used, by adding | None to a type. The List modifier can be added to a field to convert it to an array, similar to using the multi=True argument on the field object.

from typing import Optional, List class MyDoc(Document): pub_date: Optional[datetime] # same as pub_date = Date() middle_name: str | None # same as middle_name = Text() authors: List[str] # same as authors = Text(multi=True, required=True) comments: Optional[List[str]] # same as comments = Text(multi=True) 

A field can also be given a type hint of an InnerDoc subclass, in which case it becomes an Object field of that class. When the InnerDoc subclass is wrapped with List, a Nested field is created instead.

from typing import List class Address(InnerDoc): ... class Comment(InnerDoc): ... class Post(Document): address: Address # same as address = Object(Address, required=True) comments: List[Comment] # same as comments = Nested(Comment, required=True) 

Unfortunately it is impossible to have Python type hints that uniquely identify every possible Elasticsearch field type. To choose a field type that is different than the ones in the table above, the field instance can be added explicitly as a right-side assignment in the field declaration. The next example creates a field that is typed as Optional[str], but is mapped to Keyword instead of Text:

class MyDocument(Document): category: Optional[str] = Keyword() 

This form can also be used when additional options need to be given to initialize the field, such as when using custom analyzer settings or changing the required default:

class Comment(InnerDoc): content: str = Text(analyzer='snowball', required=True) 

When using type hints as above, subclasses of Document and InnerDoc inherit some of the behaviors associated with Python dataclasses, as defined by PEP 681 and the dataclass_transform decorator. To add per-field dataclass options such as default or default_factory, the mapped_field() wrapper can be used on the right side of a typed field declaration:

class MyDocument(Document): title: str = mapped_field(default="no title") created_at: datetime = mapped_field(default_factory=datetime.now) published: bool = mapped_field(default=False) category: str = mapped_field(Keyword(required=True), default="general") 

When using the mapped_field() wrapper function, an explicit field type instance can be passed as a first positional argument, as the category field does in the example above.

Static type checkers such as mypy and pyright can use the type hints and the dataclass-specific options added to the mapped_field() function to improve type inference and provide better real-time suggestions in IDEs.

One situation in which type checkers can’t infer the correct type is when using fields as class attributes. Consider the following example:

class MyDocument(Document): title: str doc = MyDocument() # doc.title is typed as "str" (correct) # MyDocument.title is also typed as "str" (incorrect) 

To help type checkers correctly identify class attributes as such, the M generic must be used as a wrapper to the type hint, as shown in the next examples:

from elasticsearch_dsl import M class MyDocument(Document): title: M[str] created_at: M[datetime] = mapped_field(default_factory=datetime.now) doc = MyDocument() # doc.title is typed as "str" # doc.created_at is typed as "datetime" # MyDocument.title is typed as "InstrumentedField" # MyDocument.created_at is typed as "InstrumentedField" 

Note that the M type hint does not provide any runtime behavior and its use is not required, but it can be useful to eliminate spurious type errors in IDEs or type checking builds.

The InstrumentedField objects returned when fields are accessed as class attributes are proxies for the field instances that can be used anywhere a field needs to be referenced, such as when specifying sort options in a Search object:

# sort by creation date descending, and title ascending s = MyDocument.search().sort(-MyDocument.created_at, MyDocument.title) 

When specifying sorting order, the + and - unary operators can be used on the class field attributes to indicate ascending and descending order.

Finally, the ClassVar annotation can be used to define a regular class attribute that should not be mapped to the Elasticsearch index:

.. code:: python 

from typing import ClassVar

class MyDoc(Document):
title: M[str] created_at: M[datetime] = mapped_field(default_factory=datetime.now) my_var: ClassVar[str] # regular class variable, ignored by Elasticsearch

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() 