wagtail-pg-search-backend

PostgreSQL full text search backend for Wagtail CMS


Keywords
wagtail, postgres, fulltext, search
License
MIT
Install
pip install wagtail-pg-search-backend==1.3.2

Documentation

Wagtail PostgreSQL full text search backend

A PostgreSQL full text search backend for Wagtail CMS.

Installation

PostgreSQL full text search in Wagtail requires PostgreSQL >= 9.2 (noticable speed improvements are in place for PostgreSQL >= 9.5), Django >= 1.10 and Wagtail >= 1.8.

First, install the module using:

pip install wagtail-pg-search-backend

Then you'll need to do a little bit of configuration.

Add the following to the project settings:

INSTALLED_APPS = [
    ...
    'wagtail_pgsearchbackend'
    ...
]

WAGTAILSEARCH_BACKENDS = {
    'default': {
        'BACKEND': 'wagtail_pgsearchbackend.backend',
        'SEARCH_CONFIG': 'english'
    }
}

Then run migrations to add the required database table:

./manage.py migrate wagtail_pgsearchbackend

Configuration

The SEARCH_CONFIG key takes a text search configuration name. This controls the stemming, stopwords etc. used when searching and indexing the database. To get a list of the available config names use this query:

SELECT cfgname FROM pg_catalog.pg_ts_config

Usage

This backend implements the required methods to be compatible with most features mentioned in the the Wagtail search docs.

Known limitations

  • SearchField.partial_match behaviour is not implemented.
  • Due to a PostgreSQL limitation, SearchField.boost is only partially respected. It is changed so that there can only be 4 different boosts. If you define 4 or less different boosts, everything will be perfectly accurate. However, your search will be a little less accurate if you define more than 4 different boosts. That being said, it will work and be roughly the same.
  • SearchField.es_extra is not handled because it is specific to ElasticSearch.
  • Using SearchQuerySet.search while limiting to specific field(s) is only supported for database fields, not methods.

Performance

The PostgreSQL search backend has been tried and tested on a few small to medium sized website and its performance compares favorably to that of ElasticSearch.

Some noticeable speed improvements are in place when using PostgreSQL >= 9.5.

Features to add

These features would awesome to have once this project is merged with Wagtail:

  • Per-object boosting
  • Faceting
  • Autocomplete (maybe it should replace partial search?)
  • Spelling suggestions

Development

Install the package and dev requirements:

pip install -e . -r requirements-dev.txt

Creating migrations

First create a database:

createdb -Upostgres wagtail_pgsearchbackend

Then call makemigrations using the test settings:

django-admin makemigrations --settings=tests.settings

Testing

To run the unittests for the current environment's Python version and Wagtail run:

make unittests

To check the code for style errors run:

make flaketest

To combine these tasks run:

make

To run the unittest against all supported versions of Python and Wagtail run:

tox

The tox run will also create a coverage report combining the results of all runs. This report is located in htmlcov/index.html.

To run individual tests by name use the runtests.py script and give the dotted path the the test module(s), class(es) or method(s) that you want to test e.g.:

./runtests.py tests.test_module.TestClass.test_method