Backends

Wagtailsearch has support for multiple backends, giving you the choice between using the database for search or an external service such as Elasticsearch. The database backend is enabled by default.

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

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

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 update_index command on a regular basis to keep the index in sync with the database.

ATOMIC_REBUILD

Warning

This option may not work on Elasticsearch version 5.4 and above, due to a bug in the handling of aliases affecting these releases.

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 rollback if an error occurs.

Setting the ATOMIC_REBUILD setting to True makes Wagtail rebuild into a separate index while keep 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.

Database Backend (default)

wagtail.search.backends.db

The database backend is very basic and is intended only to be used in development and on small sites. It cannot order results by relevance, severely hampering its usefulness when searching a large collection of pages.

It also doesn’t support:

If any of these features are important to you, we recommend using Elasticsearch instead.

PostgreSQL Backend

wagtail.contrib.postgres_search.backend

If you use PostgreSQL for your database and your site has less than a million pages, you probably want to use this backend.

See PostgreSQL search engine for more detail.

Elasticsearch Backend

Changed in version 2.1: Support for Elasticsearch 6.x was added

Elasticsearch versions 2, 5 and 6 are supported. Use the appropriate backend for your version:

wagtail.search.backends.elasticsearch2 (Elasticsearch 2.x)

wagtail.search.backends.elasticsearch5 (Elasticsearch 5.x)

wagtail.search.backends.elasticsearch6 (Elasticsearch 6.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>=2.0.0,<3.0.0"  # for Elasticsearch 2.x
pip install "elasticsearch>=5.0.0,<6.0.0"  # for Elasticsearch 5.x
pip install "elasticsearch>=6.0.0,<7.0.0"  # for Elasticsearch 6.x

The backend is configured in settings:

WAGTAILSEARCH_BACKENDS = {
    'default': {
        'BACKEND': 'wagtail.search.backends.elasticsearch2',
        'URLS': ['http://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 case-sensitive keyword argument (e.g. 'max_retries': 1).

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/wagtailsearch/backends/elasticsearch.py. Any new key is added, any existing key, if not a dictionary, is replaced with the new value. Here’s a sample on how to configure the number of shards and setting 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, who offer 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

Amazon AWS Elasticsearch

The Elasticsearch backend is compatible with Amazon Elasticsearch 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.elasticsearch2',
        '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.