Official low-level client for Elasticsearch. Its goal is to provide common ground for all Elasticsearch-related code in Python; because of this it tries to be opinion-free and very extendable.
Install the elasticsearch package with pip:
$ python -m pip install elasticsearch
If your application uses async/await in Python you can install with
the async extra:
$ python -m pip install elasticsearch[async]
Read more about how to use asyncio with this project.
The library is compatible with all Elasticsearch versions since 0.90.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, and so on.
The recommended way to set your requirements in your setup.py or requirements.txt is:
# Elasticsearch 7.x elasticsearch>=7.0.0,<8.0.0 # Elasticsearch 6.x elasticsearch>=6.0.0,<7.0.0 # Elasticsearch 5.x elasticsearch>=5.0.0,<6.0.0 # Elasticsearch 2.x elasticsearch>=2.0.0,<3.0.0
If you have a need to have multiple versions installed at the same time older
versions are also released as elasticsearch2 and elasticsearch5.
>>> from datetime import datetime
>>> from elasticsearch import Elasticsearch
# by default we connect to localhost:9200
>>> es = Elasticsearch()
# create an index in elasticsearch, ignore status code 400 (index already exists)
>>> es.indices.create(index='my-index', ignore=400)
{'acknowledged': True, 'shards_acknowledged': True, 'index': 'my-index'}
# datetimes will be serialized
>>> es.index(index="my-index", id=42, body={"any": "data", "timestamp": datetime.now()})
{'_index': 'my-index',
'_type': '_doc',
'_id': '42',
'_version': 1,
'result': 'created',
'_shards': {'total': 2, 'successful': 1, 'failed': 0},
'_seq_no': 0,
'_primary_term': 1}
# but not deserialized
>>> es.get(index="my-index", id=42)['_source']
{'any': 'data', 'timestamp': '2019-05-17T17:28:10.329598'}Elastic Cloud (and SSL) use-case:
>>> from elasticsearch import Elasticsearch
>>> es = Elasticsearch(cloud_id="<some_long_cloud_id>", http_auth=('elastic','yourpassword'))
>>> es.info()Using SSL Context with a self-signed cert use-case:
>>> from elasticsearch import Elasticsearch
>>> from ssl import create_default_context
>>> context = create_default_context(cafile="path/to/cafile.pem")
>>> es = Elasticsearch("https://elasticsearch.url:port", ssl_context=context, http_auth=('elastic','yourpassword'))
>>> es.info()The client's features include:
- translating basic Python data types to and from json (datetimes are not decoded for performance reasons)
- configurable automatic discovery of cluster nodes
- persistent connections
- load balancing (with pluggable selection strategy) across all available nodes
- failed connection penalization (time based - failed connections won't be retried until a timeout is reached)
- support for ssl and http authentication
- thread safety
- pluggable architecture
For a more high level client library with more limited scope, have a look at
elasticsearch-dsl - a more pythonic library sitting on top of
elasticsearch-py.
elasticsearch-dsl provides a more convenient and idiomatic way to write and manipulate queries by mirroring the terminology and structure of Elasticsearch JSON DSL while exposing the whole range of the DSL from Python either directly using defined classes or a queryset-like expressions.
It also provides an optional persistence layer for working with documents as Python objects in an ORM-like fashion: defining mappings, retrieving and saving documents, wrapping the document data in user-defined classes.
Copyright 2021 Elasticsearch B.V. Licensed under the Apache License, Version 2.0.