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A Django elasticsearch wrapper and helper using elasticsearch-dsl-py high level library.

Project Description


Bungiesearch is a Django wrapper for elasticsearch-dsl-py. It inherits from elasticsearch-dsl-py’s Search class, so all the fabulous features developed by the elasticsearch-dsl-py team are also available in Bungiesearch. In addition, just like Search, Bungiesearch is a lazy searching class (and iterable), meaning you can call functions in a row, or do something like the following.

lazy ='match', _all='Description')
print len(lazy) # Prints the number of hits by only fetching the number of items.
for item in lazy[5:10]:
    print item


  • Core Python friendly
    • Iteration ([x for x in lazy_search])
    • Get items (lazy_search[10])
    • Number of hits via len (len(lazy_search))
  • Index management
    • Creating and deleting an index.
    • Creating, updating and deleting doctypes and their mappings.
    • Update index doctypes.
  • Django Model Mapping
    • Very easy mapping (no lies).
    • Automatic model mapping (and supports undefined models by returning a Result instance of elasticsearch-dsl-py).
    • Efficient database fetching:
      • One fetch for all items of a given model.
      • Fetches only desired fields.
  • Django Manager
    • Easy model integration:"match", _all="something to search").
    • Search aliases (search shortcuts with as many parameters as wanted): Tweet.object.bungie_title_search("bungie") or Article.object.bungie_title_search("bungie"), where bungie_title_search is uniquely defined.
  • Django signals
    • Connect to post save and pre delete signals for the elasticsearch index to correctly reflect the database (almost) at all times.
  • Requirements
    • Django >= 1.7
    • Python 2.7 (no Python 3 support yet)

Feature examples

See section “Full example” at the bottom of page to see the code needed to perform these following examples. ### Query a word (or list thereof) on a managed model.'match', _all='Description')

Use a search alias on a model’s manager.


Use a search alias on a bungiesearch instance.'title').bsearch_titlefilter('filter this title')

Iterate over search results

# Will print the Django model instance.
for result in'match', _all='Description'):
    print result

Fetch a single item'match', _all='Description')[0]

Get the number of returned items

print len('match', _all='Description'))

Deferred model instantiation

# Will print the Django model instance's primary key. Will only fetch the `pk` field from the database.
for result in'match', _all='Description').only('pk'):

Elasticsearch limited field fetching

# Will print the Django model instance. However, elasticsearch's response only has the `_id` field.
for result in'match', _all='Description').fields('_id'):
    print result

Get a specific number of items with an offset.

This is actually elasticseach-dsl-py functionality, but it’s demonstrated here because we can iterate over the results via Bungiesearch.

for item in Article.objects.bsearch_title_search('title').only('pk').fields('_id')[5:7]:
    print item

Lazy objects

lazy = Article.objects.bsearch_title_search('title')
print len(lazy)
for item in lazy.filter('range', effective_date={'lte': '2014-09-22'}):
    print item


Unless noted otherwise, each step is required.

Install the package

The easiest way is to install the package from PyPi:

pip install bungiesearch

Note: Check your version of Django after installing bungiesearch. It was reported to me directly that installing bungiesearch may upgrade your version of Django, although I haven’t been able to confirm that myself. Bungiesearch depends on Django 1.7 and above.

In Django

Updating your Django models

Note: this part is only needed if you want to be able to use search aliases, which allow you to define shortcuts to complex queries, available directly from your Django models. I think it’s extremely practical.

  1. Open your file.
  2. Add the bungiesearch manager import: from bungiesearch.managers import BungiesearchManager
  3. Find the model, or models, you wish to index on Elasticsearch and set them to be managed by Bungiesearch by adding the objects field to them, as such: objects = BungiesearchManager(). You should now have a Django model similar to this.

Creating bungiesearch search indexes

The search indexes define how bungiesearch should serialize each of the model’s objects. It effectively defines how your object is serialized and how the ES index should be structured. These are referred to as ModelIndexes.

A good practice here is to have all the bungiesearch stuff in its own package. For example, for the section of the Sparrho platform that uses Django, we have a package called search where we define the search indexes, and a subpackage called aliases which has the many aliases we use (more on that latter).

  1. Create a subclass of ModelIndex, which you can import from from bungiesearch.indices import ModelIndex, in a new module preferably.
  2. In this class, define a class called Meta: it will hold meta information of this search index for bungiesearch’s internal working.
  3. Import the Django model you want to index (from your models file) and, in the Meta class, define a field called model, which must be set to the model you want indexed.
  4. By default, bungiesearch will index every field of your model. This may not always be desired, so you can define which fields must be excluded in this Meta class, via the exclude field.
  5. There are plenty of options, so definitely have a read through the documentation for ModelIndex.

Here’s an example of a search index. There can be many such definitions in a file.

Django settings

This is the final required step. Here’s the full documentation of this step.

  1. Open your settings file and add a BUNGIESEARCH variable, which must be a dictionary.
  2. Define URLS as a list of URLs (which can contain only one) of your ES servers.
  3. Define the INDICES key as a dictionary where the key is the name of the index on ES that you want, and the value is the full Python path to the module which has all the ModelIndex classes for to be indexed on that index name.
  4. Set ALIASES to an empty dictionary (until you define any search aliases).
  5. You can keep other values as their defaults.

In your shell

Create the ES indexes

From your shell, in the Django environment, run the following:

python search_index --create

Start populating the index

Run the following which will take each of the objects in your model, serialize them, and add them to the elasticsearch index.

python search_index --update

Note: With additional parameters, you can limit the number of documents to be indexed, as well as set conditions on whether they should be indexed based on updated time for example.

In Elasticsearch

You can now open your elasticsearch dashboard, such as Elastic HQ, and see that your index is created with the appropriate mapping and has items that are indexed.

Quick start example

This example is from the test folder. It may be partially out-dated, so please refer to the test folder for the latest version.


  1. In your file (or your, import bungiesearch and use it as a model manager.
  2. Define one or more ModelIndex subclasses which define the mapping between your Django model and elasticsearch.
  3. (Optional) Define SearchAlias subclasses which make it trivial to call complex elasticsearch-dsl-py functions.
  4. Add a BUNGIESEARCH variable in your Django settings, which must contain the elasticsearch URL(s), the modules for the indices, the modules for the search aliases and the signal definitions.


Here’s the code which is applicable to the previous examples. ### Django Model

from django.db import models
from bungiesearch.managers import BungiesearchManager

class Article(models.Model):
    title = models.TextField(db_index=True)
    authors = models.TextField(blank=True)
    description = models.TextField(blank=True)
    link = models.URLField(max_length=510, unique=True, db_index=True)
    published = models.DateTimeField(null=True)
    created = models.DateTimeField(auto_now_add=True)
    updated = models.DateTimeField(null=True)
    tweet_count = models.IntegerField()
    raw = models.BinaryField(null=True)
    source_hash = models.BigIntegerField(null=True)
    missing_data = models.CharField(blank=True, max_length=255)
    positive_feedback = models.PositiveIntegerField(null=True, blank=True, default=0)
    negative_feedback = models.PositiveIntegerField(null=True, blank=True, default=0)
    popularity_index = models.IntegerField(default=0)

    objects = BungiesearchManager()

    class Meta:
        app_label = 'core'


The following ModelIndex will generate a mapping containing all fields from Article, minus those defined in ArticleIndex.Meta.exclude. When the mapping is generated, each field will the most appropriate elasticsearch core type, with default attributes (as defined in bungiesearch.fields).

These default attributes can be overwritten with ArticleIndex.Meta.hotfixes: each dictionary key must be field defined either in the model or in the ModelIndex subclass (ArticleIndex in this case).

from core.models import Article
from bungiesearch.fields import DateField, StringField
from bungiesearch.indices import ModelIndex

class ArticleIndex(ModelIndex):
    effectived_date = DateField(eval_as='obj.created if obj.created and obj.published > obj.created else obj.published')
    meta_data = StringField(eval_as='" ".join([fld for fld in [, str(obj.tweet_count), obj.raw] if fld])')

    class Meta:
        model = Article
        exclude = ('raw', 'missing_data', 'negative_feedback', 'positive_feedback', 'popularity_index', 'source_hash')
        hotfixes = {'updated': {'null_value': '2013-07-01'},
                    'title': {'boost': 1.75},
                    'description': {'boost': 1.35},
                    'full_text': {'boost': 1.125}}


Defines a search alias for one or more models (in this case only for core.models.Article).

from core.models import Article
from bungiesearch.aliases import SearchAlias

class SearchTitle(SearchAlias):
    def alias_for(self, title):
        return self.search_instance.query('match', title=title)

    class Meta:
        models = (Article,)
        alias_name = 'title_search' # This is optional. If none is provided, the name will be the class name in lower case.

class InvalidAlias(SearchAlias):
    def alias_for_does_not_exist(self, title):
        return title

    class Meta:
        models = (Article,)

Django settings

                'URLS': [os.getenv('ELASTIC_SEARCH_URL')],
                'INDICES': {'bungiesearch_demo': 'core.search_indices'},
                'ALIASES': {'bsearch': 'myproject.search_aliases'},
                'SIGNALS': {'BUFFER_SIZE': 1}  # uses BungieSignalProcessor



A ModelIndex defines mapping and object extraction for indexing of a given Django model.

Any Django model to be managed by bungiesearch must have a defined ModelIndex subclass. This subclass must contain a subclass called Meta which must have a model attribute (sets the model which it represents).

Class attributes

As detailed below, the doc type mapping will contain fields from the model it related to. However, one may often need to index fields which correspond to either a concatenation of fields of the model or some logical operation.

Bungiesearch makes this very easy: simply define a class attribute as whichever core type, and set to the eval_as constructor parameter to a one line Python statement. The object is referenced as obj (not self nor object, just obj).


This is a partial example as the Meta subclass is not defined, yet mandatory (cf. below).

from bungiesearch.fields import DateField, StringField
from bungiesearch.indices import ModelIndex

class ArticleIndex(ModelIndex):
    effective_date = DateField(eval_as='obj.created if obj.created and obj.published > obj.created else obj.published')
    meta_data = StringField(eval_as='" ".join([fld for fld in [, str(obj.tweet_count), obj.raw] if fld])')

Here, both effective_date and meta_data will be part of the doc type mapping, but won’t be reversed mapped since those fields do not exist in the model.

This can also be used to index foreign keys:

some_field_name = StringField(eval_as='",".join([item for item in obj.some_foreign_relation.values_list("some_field", flat=True)]) if obj.some_foreign_relation else ""')

Class methods


Override this function to specify whether an item should be indexed or not. This is useful when defining multiple indices (and ModelIndex classes) for a given model. This method’s signature and super class code is as follows, and allows indexing of all items.

def matches_indexing_condition(self, item):
    return True

For example, if a given elasticsearch index should contain only item whose title starts with "Awesome", then this method can be overridden as follows.

def matches_indexing_condition(self, item):
    return item.title.startswith("Awesome")

Meta subclass attributes

Note: in the following, any variable defined a being a list could also be a tuple. ##### model Required: defines the Django model for which this ModelIndex is applicable.


Optional: list of fields (or columns) which must be fetched when serializing the object for elasticsearch, or when reverse mapping the object from elasticsearch back to a Django Model instance. By default, all fields will be fetched. Setting this will restrict which fields can be fetched and may lead to errors when serializing the object. It is recommended to use the exclude attribute instead (cf. below).


Optional: list of fields (or columns) which must not be fetched when serializing or deserializing the object.


Optional: a dictionary whose keys are index fields and whose values are dictionaries which define core type attributes. By default, there aren’t any special settings, apart for String fields, where the analyzer is set to `snowball <>`__ ({'analyzer': 'snowball'}).


Optional: additional fields to fetch for mapping, may it be for eval_as fields or when returning the object from the database.


Optional: the model field to use as a unique ID for elasticsearch’s metadata _id. Defaults to id (also called `pk <>`__).


Optional: set the model’s field which can be filtered on dates in order to find when objects have been updated. Note, this is mandatory to use --start and/or --end when updating index (with search_index --update).


Optional: set to True to make efficient queries when automatically mapping to database objects. This will always restrict fetching to the fields set in fields and in additional_fields. Note: You can also perform an optimal database query with .only('__model'), which will use the same fields as optimize_queries, or .only('__fields'), which will use the fields provided in the .fields() call.


Optional: set to a QuerySet instance to specify the query used when the search_index command is ran to index. This does not affect how each piece of content is indexed.


Enables support for a given model to be indexed on several elasticsearch indices. Set to False on all but the default index. Note: if all managed models are set with default=False then Bungiesearch will fail to find and index that model.


Indexes all objects of Article, as long as their updated datetime is less than 21 October 2015 04:29.

from core.models import Article
from bungiesearch.indices import ModelIndex
from datetime import datetime

class ArticleIndex(ModelIndex):

    def matches_indexing_condition(self, item):
        return item.updated < datetime.datetime(2015, 10, 21, 4, 29)

    class Meta:
        model = Article
        id_field = 'id' # That's actually the default value, so it's not really needed.
        exclude = ('raw', 'missing_data', 'negative_feedback', 'positive_feedback', 'popularity_index', 'source_hash')
        hotfixes = {'updated': {'null_value': '2013-07-01'},
                    'title': {'boost': 1.75},
                    'description': {'boost': 1.35},
                    'full_text': {'boost': 1.125}}
        optimized_queries = True
        indexing_query = Article.objects.defer(*exclude).select_related().all().prefetch_related('tags')


A SearchAlias define search shortcuts (somewhat similar to Django managers). Often times, a given search will be used in multiple parts of the code. SearchAliases allow you define those queries, filters, or any bungiesearch/elasticsearch-dsl-py calls as an alias.

A search alias is either applicable to a list (or tuple) of managed models, or to any bungiesearch instance. It’s very simple, so here’s an example which is detailed right below.


The most simple implementation of a SearchAlias is as follows. This search alias can be called via Article.objects.bungie_title (or, supposing that the namespace is set to None in the settings (cf. below).

from bungiesearch.aliases import SearchAlias

class Title(SearchAlias):
    def alias_for(self, title):
        return self.search_instance.query('match', title=title)

Method overwrite

Any implementation needs to inherit from bungiesearch.aliases.SearchAlias and overwrite alias_for. You can set as many or as little parameters as you want for that function (since bungiesearch only return the pointer to that function without actually calling it).

Since each managed model has its own doc type, self.search_instance is a bungiesearch instance set to search the specific doctype.

Meta subclass attributes

Although not mandatory, the Meta subclass enabled custom naming and model restrictions for a search alias.


Optional: list (or tuple) of Django models which are allowed to use this search alias. If a model which is not allowed to use this SearchAlias tries it, a ValueError will be raised.


Optional: A string corresponding the suffix name of this search alias. Defaults to the lower case class name.

WARNING: As explained in the “Settings” section below, all search aliases in a given module share the prefix (or namespace). This is to prevent aliases from accidently overwriting Django manager function (e.g. update or get). In other words, if you define the alias_name to test, then it must be called as model_obj.objects.$prefix$_test where $prefix$ is the prefix defined in the settings. This prefix is also applicable to search aliases which are available via bungiesearch instances directly. Hence, one can define in one module search utilities (e.g. regex and range) and define model specific aliases (e.g. title) in another module, and use both in conjunction as such:'search title').utils_range(field='created', gte='2014-05-20', as_query=True). These aliases can be concatenated ad vitam aeternam.

Sophisticated example

This example shows that we can have some fun with search aliases. In this case, we define a Range alias which is applicable to any field on any model.

class Range(SearchAlias):
    def alias_for(self, field, gte=None, lte=None, boost=None, as_query=False):
        body = {field: {}}
        if gte:
            body[field]['gte'] = gte
        if lte:
            body[field]['lte'] = lte
        if boost:
            if not as_query:
                logging.warning('Boost is not applicable to search alias Range when not used as a query.')
                body[field]['boost'] = boost
        if as_query:
            return self.search_instance.query({'range': body})
        return self.search_instance.filter({'range': body})

We can use it as such Article.objects.bungie_range(field='created', gte='2014-05-20', as_query=True).


Add ‘bungiesearch’ to INSTALLED_APPS.

You must define BUNGIESEARCH in your Django settings in order for bungiesearch to know elasticsearch URL(s) and which index name contains mappings for each ModelIndex.

                'URLS': ['localhost'], # No leading http:// or the elasticsearch client will complain.
                'INDICES': {'main_index': 'myproject.myapp.myindices'} # Must be a module path.
                'ALIASES': {'bsearch': 'myproject.search_aliases'},
                'SIGNALS': {'BUFFER_SIZE': 1},
                'TIMEOUT': 5


Required: must be a list of URLs which host elasticsearch instance(s). This is directly sent to elasticsearch-dsl-py, so any issue with multiple URLs should be refered to them.


Required: must be a dictionary where each key is the name of an elasticsearch index and each value is a path to a Python module containing classes which inherit from bungiesearch.indices.ModelIndex (cf. below).


Optional: a dictionary whose key is the alias namespace and whose value is the Python module containing classes which inherit from bungiesearch.aliases.SearchAlias. If the namespace is None, then the alias will be named bungie. If the namespace is an empty string, there will be no alias namespace. The provided namespace will be appended by an underscore. In the example above, each search alias defined in myproject.search_aliases will be referenced as $ModelObj$.objects.bsearch_$alias$, where $ModelObj$ is a Django model and $alias$ is the name of the search alias.

The purpose is to not accidently overwrite Django’s default manager functions with search aliases.


Optional: if it exists, it must be a dictionary (even empty), and will connect to the post save and pre delete model functions of all models using bungiesearch.managers.BungiesearchManager as a manager. One may also define a signal processor class for more custom functionality by placing the string value of the module path under a key called SIGNAL_CLASS in the dictionary value of SIGNALS and defining setup and teardown methods, which take model as the only parameter. These methods connect and disconnect the signal processing class to django signals (signals are connected to each model which uses a BungiesearchManager).

If SIGNALS is not defined in the settings, none of the models managed by BungiesearchManager will automatically update the index when a new item is created or deleted.


Optional: an integer representing the number of items to buffer before making a bulk index update, defaults to 100.

WARNING: if your application is shut down before the buffer is emptied, then any buffered instance will not be indexed on elasticsearch. Hence, a possibly better implementation is wrapping post_save_connector and pre_delete_connector from bungiesearch.signals in a celery task. It is not implemented as such here in order to not require celery.


Optional: Elasticsearch connection timeout in seconds. Defaults to 5.


The easiest way to run the tests is to install all dev dependencies using ./ then run ./

All Bungiesearch tests are in tests/core/ You can run the tests by creating a Python virtual environment, installing the requirements from requirements.txt, installing the package (pip install .) and running python tests/ test. Make sure to update tests/ to use your own elasticsearch URLs, or update the ELASTIC_SEARCH_URL environment variable.

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