API Documentation

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

Document

class elasticsearch_dsl.DocType(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 DocType 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 from_es(hit)

Helper method to construct an instance from a dictionary returned by elasticsearch.

classmethod get(id, using=None, index=None, **kwargs)

Retrieve a single document from elasticsearch using it’s id.

Parameters:
  • idid of the document to be retireved
  • index – elasticsearch index to use, if the DocType 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 retireved 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 DocType 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, **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 DocType 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

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

classmethod search(using=None, index=None)

Create an Search instance that will search over this DocType.

to_dict(include_meta=False)

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, _type, _id etc). Otherwise just the document’s data is serialized. This is useful when passing multiple instances into elasticsearch.helpers.bulk.
update(using=None, index=None, **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 DocType 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.update unchanged.

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(**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(analyzer)

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(**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, 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(**kwargs)

Closes the index in elasticsearch.

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

create(**kwargs)

Creates the index in elasticsearch.

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

delete(**kwargs)

Deletes the index in elasticsearch.

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

delete_alias(**kwargs)

Delete specific alias.

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

doc_type(doc_type)

Associate a DocType subclass with an index. This means that, when this index is created, it will contain the mappings for the DocType. If the DocType class doesn’t have a default index yet, name of the Index instance will be used. Can be used as a decorator:

i = Index('blog')

@i.doc_type
class Post(DocType):
    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(**kwargs)

Returns True if the index already exists in elasticsearch.

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

exists_alias(**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(**kwargs)

Check if a type/types exists in the index.

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

flush(**kwargs)

Preforms a flush operation on the index.

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

flush_synced(**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(**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(**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(**kwargs)

Retrieve a specified alias.

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

get_field_mapping(**kwargs)

Retrieve mapping definition of a specific field.

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

get_mapping(**kwargs)

Retrieve specific mapping definition for a specific type.

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

get_settings(**kwargs)

Retrieve settings for the index.

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

get_upgrade(**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(**kwargs)

Opens the index in elasticsearch.

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

put_alias(**kwargs)

Create an alias for the index.

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

put_mapping(**kwargs)

Register specific mapping definition for a specific type.

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

put_settings(**kwargs)

Change specific index level settings in real time.

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

recovery(**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(**kwargs)

Preforms a refresh operation on the index.

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

save()

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()

Rteurn a Search object searching over this index and its DocTypes.

segments(**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(**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(**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(**kwargs)

Retrieve statistics on different operations happening on the index.

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

upgrade(**kwargs)

Upgrade the index to the latest format.

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

validate_query(**kwargs)

Validate a potentially expensive query without executing it.

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