wagtail/docs/topics/search/backends.md

(wagtailsearch_backends)=

Backends

Wagtailsearch has support for multiple backends, giving you the choice between using the database for search or an external service such as Elasticsearch.

You can configure which backend to use with the WAGTAILSEARCH_BACKENDS setting:

WAGTAILSEARCH_BACKENDS = {
    'default': {
        'BACKEND': 'wagtail.search.backends.database',
    }
}

(wagtailsearch_backends_auto_update)=

AUTO_UPDATE

By default, Wagtail will automatically keep all indexes up to date. This could impact performance when editing content, especially if your index is hosted on an external service.

The AUTO_UPDATE setting allows you to disable this on a per-index basis:

WAGTAILSEARCH_BACKENDS = {
    'default': {
        'BACKEND': ...,
        'AUTO_UPDATE': False,
    }
}

If you have disabled auto-update, you must run the command on a regular basis to keep the index in sync with the database.

(wagtailsearch_backends_atomic_rebuild)=

ATOMIC_REBUILD

By default (when using the Elasticsearch backend), when the update_index command is run, Wagtail deletes the index and rebuilds it from scratch. This causes the search engine to not return results until the rebuild is complete and is also risky as you can't roll back if an error occurs.

Setting the ATOMIC_REBUILD setting to True makes Wagtail rebuild into a separate index while keeping the old index active until the new one is fully built. When the rebuild is finished, the indexes are swapped atomically and the old index is deleted.

BACKEND

Here's a list of backends that Wagtail supports out of the box.

(wagtailsearch_backends_database)=

Database Backend (default)

wagtail.search.backends.database

The database search backend searches content in the database using the full-text search features of the database backend in use (such as PostgreSQL FTS, SQLite FTS5). This backend is intended to be used for development and also should be good enough to use in production on sites that don't require any Elasticsearch specific features.

(wagtailsearch_backends_elasticsearch)=

Elasticsearch Backend

Elasticsearch versions 7 and 8 are supported. Use the appropriate backend for your version:

• wagtail.search.backends.elasticsearch7 (Elasticsearch 7.x)
• wagtail.search.backends.elasticsearch8 (Elasticsearch 8.x)

Prerequisites are the Elasticsearch service itself and, via pip, the elasticsearch-py package. The major version of the package must match the installed version of Elasticsearch:

pip install "elasticsearch>=7.0.0,<8.0.0"  # for Elasticsearch 7.x

pip install "elasticsearch>=8.0.0,<9.0.0"  # for Elasticsearch 8.x

The backend is configured in settings:

WAGTAILSEARCH_BACKENDS = {
    'default': {
        'BACKEND': 'wagtail.search.backends.elasticsearch8',
        'URLS': ['https://localhost:9200'],
        'INDEX': 'wagtail',
        'TIMEOUT': 5,
        'OPTIONS': {},
        'INDEX_SETTINGS': {},
    }
}

Other than BACKEND, the keys are optional and default to the values shown. Any defined key in OPTIONS is passed directly to the Elasticsearch constructor as a case-sensitive keyword argument (for example 'max_retries': 1).

A username and password may be optionally supplied to the URL field to provide authentication credentials for the Elasticsearch service:

WAGTAILSEARCH_BACKENDS = {
    'default': {
        ...
        'URLS': ['https://username:password@localhost:9200'],
        ...
    }
}

INDEX_SETTINGS is a dictionary used to override the default settings to create the index. The default settings are defined inside the ElasticsearchSearchBackend class in the module wagtail/wagtail/search/backends/elasticsearch7.py. Any new key is added and any existing key, if not a dictionary, is replaced with the new value. Here's a sample of how to configure the number of shards and set the Italian LanguageAnalyzer as the default analyzer:

WAGTAILSEARCH_BACKENDS = {
    'default': {
        ...,
        'INDEX_SETTINGS': {
            'settings': {
                'index': {
                    'number_of_shards': 1,
                },
                'analysis': {
                    'analyzer': {
                        'default': {
                            'type': 'italian'
                        }
                    }
                }
            }
        }
    }

If you prefer not to run an Elasticsearch server in development or production, there are many hosted services available, including Bonsai, which offers a free account suitable for testing and development. To use Bonsai:

• Sign up for an account at Bonsai
• Use your Bonsai dashboard to create a Cluster.
• Configure URLS in the Elasticsearch entry in WAGTAILSEARCH_BACKENDS using the Cluster URL from your Bonsai dashboard
• Run ./manage.py update_index

(opensearch)=

OpenSearch

OpenSearch is a community-driven search engine originally created as a fork of Elasticsearch 7. Wagtail supports OpenSearch through the wagtail.search.backends.elasticsearch7 backend and version 7.13.4 of the Elasticsearch Python library. Later versions of the library only permit connecting to Elastic-branded servers, and are not compatible with OpenSearch.

Amazon AWS OpenSearch

The Elasticsearch backend is compatible with Amazon OpenSearch Service, but requires additional configuration to handle IAM based authentication. This can be done with the requests-aws4auth package along with the following configuration:

from elasticsearch import RequestsHttpConnection
from requests_aws4auth import AWS4Auth

WAGTAILSEARCH_BACKENDS = {
    'default': {
        'BACKEND': 'wagtail.search.backends.elasticsearch7',
        'INDEX': 'wagtail',
        'TIMEOUT': 5,
        'HOSTS': [{
            'host': 'YOURCLUSTER.REGION.es.amazonaws.com',
            'port': 443,
            'use_ssl': True,
            'verify_certs': True,
            'http_auth': AWS4Auth('ACCESS_KEY', 'SECRET_KEY', 'REGION', 'es'),
        }],
        'OPTIONS': {
            'connection_class': RequestsHttpConnection,
        },
    }
}

Rolling Your Own

Wagtail search backends implement the interface defined in wagtail/wagtail/wagtailsearch/backends/base.py. At a minimum, the backend's search() method must return a collection of objects or model.objects.none(). For a fully-featured search backend, examine the Elasticsearch backend code in elasticsearch.py.