Elasticsearch Django app.


License
MIT
Install
pip install elasticsearch-django==7.1

Documentation

https://travis-ci.org/yunojuno/elasticsearch-django.svg?branch=master

This project now requires Python 3.7+ and Django 3.0+. For previous versions please refer to the relevant tag or branch.

Elasticsearch for Django

This is a lightweight Django app for people who are using Elasticsearch with Django, and want to manage their indexes.

NB the master branch is now based on ES7. If you are using ES2/ES5/ES6, please switch to the relevant branch (released on PyPI as 2.x, 5.x, 6.x)


Search Index Lifecycle

The basic lifecycle for a search index is simple:

  1. Create an index
  2. Post documents to the index
  3. Query the index

Relating this to our use of search within a Django project it looks like this:

  1. Create mapping file for a named index
  2. Add index configuration to Django settings
  3. Map models to document types in the index
  4. Post document representation of objects to the index
  5. Update the index when an object is updated
  6. Remove the document when an object is deleted
  7. Query the index
  8. Convert search results into a QuerySet (preserving relevance)

Django Implementation

This section shows how to set up Django to recognise ES indexes, and the models that should appear in an index. From this setup you should be able to run the management commands that will create and populate each index, and keep the indexes in sync with the database.

Create index mapping file

The prerequisite to configuring Django to work with an index is having the mapping for the index available. This is a bit chicken-and-egg, but the underlying assumption is that you are capable of creating the index mappings outside of Django itself, as raw JSON - e.g. using the Chrome extension Sense, or the API tool Paw. (The easiest way to spoof this is to POST a JSON document representing your document type at URL on your ES instance (POST http://ELASTICSEARCH_URL/{{index_name}}) and then retrieving the auto-magic mapping that ES created via GET http://ELASTICSEARCH_URL/{{index_name}}/_mapping.)

Once you have the JSON mapping, you should save it in the root of the Django project as search/mappings/{{index_name}}.json.

Configure Django settings

The Django settings for search are contained in a dictionary called SEARCH_SETTINGS, which should be in the main django.conf.settings file. The dictionary has three root nodes, connections, indexes and settings. Below is an example:

SEARCH_SETTINGS = {
    'connections': {
        'default': getenv('ELASTICSEARCH_URL'),
    },
    'indexes': {
        'blog': {
            'models': [
                'website.BlogPost',
            ]
        }
    },
    'settings': {
        # batch size for ES bulk api operations
        'chunk_size': 500,
        # default page size for search results
        'page_size': 25,
        # set to True to connect post_save/delete signals
        'auto_sync': True,
        # List of models which will never auto_sync even if auto_sync is True
        'never_auto_sync': [],
        # if true, then indexes must have mapping files
        'strict_validation': False
    }
}

The connections node is (hopefully) self-explanatory - we support multiple connections, but in practice you should only need the one - 'default' connection. This is the URL used to connect to your ES instance. The settings node contains site-wide search settings. The indexes nodes is where we configure how Django and ES play together, and is where most of the work happens.

Index settings

Inside the index node we have a collection of named indexes - in this case just the single index called blog. Inside each index we have a models key which contains a list of Django models that should appear in the index, denoted in app.ModelName format. You can have multiple models in an index, and a model can appear in multiple indexes. How models and indexes interact is described in the next section.

Configuration Validation

When the app boots up it validates the settings, which involves the following:

  1. Do each of the indexes specified have a mapping file?
  2. Do each of the models implement the required mixins?

Implement search document mixins

So far we have configured Django to know the names of the indexes we want, and the models that we want to index. What it doesn't yet know is which objects to index, and how to convert an object to its search index document. This is done by implementing two separate mixins - SearchDocumentMixin and SearchDocumentManagerMixin. The configuration validation routine will tell you if these are not implemented.

SearchDocumentMixin

This mixin is responsible for the seaerch index document format. We are indexing JSON representations of each object, and we have two methods on the mixin responsible for outputting the correct format - as_search_document and as_search_document_update.

An aside on the mechanics of the auto_sync process, which is hooked up using Django's post_save and post_delete model signals. ES supports partial updates to documents that already exist, and we make a fundamental assumption about indexing models - that if you pass the ``update_fields`` kwarg to a ``model.save`` method call, then you are performing a partial update, and this will be propagated to ES as a partial update only.

To this end, we have two methods for generating the model's JSON representation - as_search_document, which should return a dict that represents the entire object; and as_search_document_update, which takes the update_fields kwarg. This method handler two partial update 'strategies', defined in the SEARCH_SETTINGS, 'full' and 'partial'. The default 'full' strategy simply proxies the as_search_document method - i.e. partial updates are treated as a full document update. The 'partial' strategy is more intelligent - it will map the update_fields specified to the field names defined in the index mapping files. If a field name is passed into the save method but is not in the mapping file, it is ignored. In addition, if the underlying Django model field is a related object, a ValueError will be raised, as we cannot serialize this automatically. In this scenario, you will need to override the method in your subclass - see the code for more details.

To better understand this, let us say that we have a model (MyModel) that is configured to be included in an index called myindex. If we save an object, without passing update_fields, then this is considered a full document update, which triggers the object's index_search_document method:

obj = MyModel.objects.first()
obj.save()
...
# AUTO_SYNC=true will trigger a re-index of the complete object document:
obj.index_search_document(index='myindex')

However, if we only want to update a single field (say the timestamp), and we pass this in to the save method, then this will trigger the update_search_document method, passing in the names of the fields that we want updated.

# save a single field on the object
obj.save(update_fields=['timestamp'])
...
# AUTO_SYNC=true will trigger a partial update of the object document
obj.update_search_document(index, update_fields=['timestamp'])

We pass the name of the index being updated as the first arg, as objects may have different representations in different indexes:

def as_search_document(self, index):
    return {'name': "foo"} if index == 'foo' else {'name': "bar"}

In the case of the second method, the simplest possible implementation would be a dictionary containing the names of the fields being updated and their new values, and this is the default implementation. If the fields passed in are simple fields (numbers, dates, strings, etc.) then a simple {'field_name': getattr(obj, field_name} is returned. However, if the field name relates to a complex object (e.g. a related object) then this method will raise an InvalidUpdateFields exception. In this scenario you should override the default implementationwith one of your own.

def as_search_document_update(self, index, update_fields):
    if 'user' in update_fields:
        # remove so that it won't raise a ValueError
        update_fields.remove('user')
        doc = super().as_search_document_update(index, update_fields)
        doc['user'] = self.user.get_full_name()
        return doc
    return super().as_search_document_update(index, update_fields)

The reason we have split out the update from the full-document index comes from a real problem that we ourselves suffered. The full object representation that we were using was quite DB intensive - we were storing properties of the model that required walking the ORM tree. However, because we were also touching the objects (see below) to record activity timestamps, we ended up flooding the database with queries simply to update a single field in the output document. Partial updates solves this issue:

def touch(self):
    self.timestamp = now()
    self.save(update_fields=['timestamp'])

def as_search_document_update(self, index, update_fields):
    if list(update_fields) == ['timestamp']:
        # only propagate changes if it's +1hr since the last timestamp change
        if now() - self.timestamp < timedelta(hours=1):
            return {}
        else:
            return {'timestamp': self.timestamp}
    ....

Processing updates async

If you are generating a lot of index updates you may want to run them async (via some kind of queueing mechanism). There is no built-in method to do this, given the range of queueing libraries and patterns available, however it is possible using the pre_index, pre_update and pre_delete signals. In this case, you should also turn off AUTO_SYNC (as this will run the updates synchronously), and process the updates yourself. The signals pass in the kwargs required by the relevant model methods, as well as the instance involved:

# ensure that SEARCH_AUTO_SYNC=False

from django.dispatch import receiver
import django_rq
from elasticsearch_django.signals import (
    pre_index,
    pre_update,
    pre_delete
)

queue = django_rq.get_queue("elasticsearch")


@receiver(pre_index, dispatch_uid="async_index_document")
def index_search_document_async(sender, **kwargs):
    """Queue up search index document update via RQ."""
    instance = kwargs.pop("instance")
    queue.enqueue(
        instance.update_search_document,
        index=kwargs.pop("index"),
    )


@receiver(pre_update, dispatch_uid="async_update_document")
def update_search_document_async(sender, **kwargs):
    """Queue up search index document update via RQ."""
    instance = kwargs.pop("instance")
    queue.enqueue(
        instance.index_search_document,
        index=kwargs.pop("index"),
        update_fields=kwargs.pop("update_fields"),
    )


@receiver(pre_delete, dispatch_uid="async_delete_document")
def delete_search_document_async(sender, **kwargs):
    """Queue up search index document deletion via RQ."""
    instance = kwargs.pop("instance")
    queue.enqueue(
        instance.delete_search_document,
        index=kwargs.pop("index"),
    )

SearchDocumentManagerMixin

This mixin must be implemented by the model's default manager (objects). It also requires a single method implementation - get_search_queryset() - which returns a queryset of objects that are to be indexed. This can also use the index kwarg to provide different sets of objects to different indexes.

def get_search_queryset(self, index='_all'):
    return self.get_queryset().filter(foo='bar')

We now have the bare bones of our search implementation. We can now use the included management commands to create and populate our search index:

# create the index 'foo' from the 'foo.json' mapping file
$ ./manage.py create_search_index foo

# populate foo with all the relevant objects
$ ./manage.py update_search_index foo

The next step is to ensure that our models stay in sync with the index.

Add model signal handlers to update index

If the setting auto_sync is True, then on AppConfig.ready each model configured for use in an index has its post_save and post_delete signals connected. This means that they will be kept in sync across all indexes that they appear in whenever the relevant model method is called. (There is some very basic caching to prevent too many updates - the object document is cached for one minute, and if there is no change in the document the index update is ignored.)

There is a VERY IMPORTANT caveat to the signal handling. It will only pick up on changes to the model itself, and not on related (ForeignKey, ManyToManyField) model changes. If the search document is affected by such a change then you will need to implement additional signal handling yourself.

In addition to object.save(), SeachDocumentMixin also provides the update_search_index(self, action, index='_all', update_fields=None, force=False) method. Action should be 'index', 'update' or 'delete'. The difference between 'index' and 'update' is that 'update' is a partial update that only changes the fields specified, rather than re-updating the entire document. If action is 'update' whilst update_fields is None, action will be changed to index.

We now have documents in our search index, kept up to date with their Django counterparts. We are ready to start querying ES.


Search Queries (How to Search)

Running search queries

The search itself is done using elasticsearch_dsl, which provides a pythonic abstraction over the QueryDSL, but also allows you to use raw JSON if required:

from elasticsearch_django.settings import get_client
from elasticsearch_dsl import Search

# run a default match_all query
search = Search(using=get_client())
response = search.execute()

# change the query using the python interface
search = search.query("match", title="python")

# change the query from the raw JSON
search.update_from_dict({"query": {"match": {"title": "python"}}})

The response from execute is a Response object which wraps up the ES JSON response, but is still basically JSON.

SearchQuery

The elasticsearch_django.models.SearchQuery model wraps this functionality up and provides helper properties, as well as logging the query:

from elasticsearch_django.settings import get_client
from elasticsearch_django.models import execute_search
from elasticsearch_dsl import Search

# run a default match_all query
search = Search(using=get_client(), index='blog')
sq = execute_search(search)
# the raw response is stored on the return object,
# but is not stored on the object in the database.
print(sq.response)

Calling the execute_search function will execute the underlying search, log the query JSON, the number of hits, and the list of hit meta information for future analysis. The execute method also includes these additional kwargs:

  • user - the user who is making the query, useful for logging
  • search_terms - the search query supplied by the user (as opposed to the DSL) - not used by ES, but stored in the logs
  • reference - a free text reference field - used for grouping searches together - could be session id.
  • save - by default the SearchQuery created will be saved, but passing in False will prevent this.

In conclusion - running a search against an index means getting to grips with the elasticsearch_dsl library, and when playing with search in the shell there is no need to use anything else. However, in production, searches should always be executed using the SearchQuery.execute method.

Converting search hits into Django objects

Running a search against an index will return a page of results, each containing the _source attribute which is the search document itself (as created by the SearchDocumentMixin.as_search_document method), together with meta info about the result - most significantly the relevance score, which is the magic value used for ranking (ordering) results. However, the search document probably doesn't contain all the of the information that you need to display the result, so what you really need is a standard Django QuerySet, containing the objects in the search results, but maintaining the order. This means injecting the ES score into the queryset, and then using it for ordering. There is a method on the SearchDocumentManagerMixin called from_search_query which will do this for you. It uses raw SQL to add the score as an annotation to each object in the queryset. (It also adds the 'rank' - so that even if the score is identical for all hits, the ordering is preserved.)

from models import BlogPost

# run a default match_all query
search = Search(using=get_client(), index='blog')
sq = execute_search(search)
for obj in BlogPost.objects.from_search_query(sq):
    print obj.search_score, obj.search_rank