Overviewedit

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.

Compatibilityedit

Language clients are forward compatible; meaning that clients support communicating with greater or equal minor versions of Elasticsearch. 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 elasticsearch2 and elasticsearch5.

Example useedit

Simple use-case:

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

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

# Datetimes will be serialized:
>>> es.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
>>> es.get(index="my-index-000001", id=42)['_source']
{'any': 'data', 'timestamp': '2013-05-12T19:45:31.804229'}

For an elaborate example of how to ingest data into Elastic Cloud, refer to this page.

Featuresedit

The client’s features include:

  • Translating basic Python data types to and from JSON
  • Configurable automatic discovery of cluster nodes
  • Persistent connections
  • Load balancing (with pluggable selection strategy) across all available nodes
  • Node timeouts on transient errors
  • Thread safety
  • Pluggable architecture

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

Elasticsearch DSLedit

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.