Overview

This is the official low-level Python client for Elasticsearch. Its goal is to provide common ground for all Elasticsearch-related code in Python. For this reason, the client is designed to be unopinionated and extendable. An API reference is available on Read the Docs.

Language clients are forward compatible; meaning that the clients support communicating with greater or equal minor versions of Elasticsearch without breaking. It does not mean that the clients automatically support new features of newer Elasticsearch versions; it is only possible after a release of a new client version. For example, a 8.12 client version won’t automatically support the new features of the 8.13 version of Elasticsearch, the 8.13 client version is required for that. Elasticsearch language clients are only backwards compatible with default distributions and without guarantees made.

If you have a need to have multiple versions installed at the same time older versions are also released as elasticsearch7 and elasticsearch8.

Simple use-case:

>>> from datetime import datetime
>>> from elasticsearch import Elasticsearch

# Connect to 'http://localhost:9200'
>>> client = Elasticsearch("http://localhost:9200")

# Datetimes will be serialized:
>>> client.index(index="my-index-000001", id=42, document={"any": "data", "timestamp": datetime.now()})
{'_id': '42', '_index': 'my-index-000001', '_type': 'test-type', '_version': 1, 'ok': True}

# ...but not deserialized
>>> client.get(index="my-index-000001", id=42)['_source']
{'any': 'data', 'timestamp': '2013-05-12T19:45:31.804229'}

The client’s features include:

The client also contains a convenient set of helpers for some of the more engaging tasks like bulk indexing and reindexing.

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.

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 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.