This is a package that allows indexing of django models in elasticsearch with elasticsearch-dsl-py.
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safwanrahman Support 6.1.0, but not 6.2.0
This package support 6.1.0, but not 6.2.0
Latest commit fadba88 Sep 21, 2018

README.rst

Django Elasticsearch DSL

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This is a package that allows indexing of django models in elasticsearch. It is built as a thin wrapper around elasticsearch-dsl-py so you can use all the features developed by the elasticsearch-dsl-py team.

Features

  • Based on elasticsearch-dsl-py so you can make queries with the Search class.
  • Django signal receivers on save and delete for keeping Elasticsearch in sync.
  • Management commands for creating, deleting, rebuilding and populating indices.
  • Elasticsearch auto mapping from django models fields.
  • Complex field type support (ObjectField, NestedField).
  • Requirements
    • Django >= 1.8
    • Python 2.7, 3.4, 3.5, 3.6
    • Elasticsearch >= 2.0 < 7.0

Quickstart

Install Django Elasticsearch DSL:

pip install django-elasticsearch-dsl

# Elasticsearch 6.x
pip install 'elasticsearch-dsl>=6.0,<7.0'

# Elasticsearch 5.x
pip install 'elasticsearch-dsl>=5.0,<6.0'

# Elasticsearch 2.x
pip install 'elasticsearch-dsl>=2.1,<3.0'

Then add django_elasticsearch_dsl to the INSTALLED_APPS

You must define ELASTICSEARCH_DSL in your django settings.

For example:

ELASTICSEARCH_DSL={
    'default': {
        'hosts': 'localhost:9200'
    },
}

ELASTICSEARCH_DSL is then passed to elasticsearch-dsl-py.connections.configure (see here).

Then for a model:

# models.py

class Car(models.Model):
    name = models.CharField()
    color = models.CharField()
    description = models.TextField()
    type = models.IntegerField(choices=[
        (1, "Sedan"),
        (2, "Truck"),
        (4, "SUV"),
    ])

To make this model work with Elasticsearch, create a subclass of django_elasticsearch_dsl.DocType and create a django_elasticsearch_dsl.Index to define your Elasticsearch indices, names, and settings. This classes must be defined in a documents.py file.

# documents.py

from django_elasticsearch_dsl import DocType, Index
from .models import Car

# Name of the Elasticsearch index
car = Index('cars')
# See Elasticsearch Indices API reference for available settings
car.settings(
    number_of_shards=1,
    number_of_replicas=0
)


@car.doc_type
class CarDocument(DocType):
    class Meta:
        model = Car # The model associated with this DocType

        # The fields of the model you want to be indexed in Elasticsearch
        fields = [
            'name',
            'color',
            'description',
            'type',
        ]

        # Ignore auto updating of Elasticsearch when a model is saved
        # or deleted:
        # ignore_signals = True
        # Don't perform an index refresh after every update (overrides global setting):
        # auto_refresh = False
        # Paginate the django queryset used to populate the index with the specified size
        # (by default there is no pagination)
        # queryset_pagination = 5000

To create and populate the Elasticsearch index and mapping use the search_index command:

$ ./manage.py search_index --rebuild

Now, when you do something like:

car = Car(
    name="Car one",
    color="red",
    type=1,
    description="A beautiful car"
)
car.save()

The object will be saved in Elasticsearch too (using a signal handler). To get an elasticsearch-dsl-py Search instance, use:

s = CarDocument.search().filter("term", color="red")

# or

s = CarDocument.search().query("match", description="beautiful")

for hit in s:
    print(
        "Car name : {}, description {}".format(hit.name, hit.description)
    )

The previous example returns a result specific to elasticsearch_dsl, but it is also possible to convert the elastisearch result into a real django queryset, just be aware that this costs a sql request to retrieve the model instances with the ids returned by the elastisearch query.

s = CarDocument.search().filter("term", color="blue")[:30]
qs = s.to_queryset()
# qs is just a django queryset and it is called with order_by to keep
# the same order as the elasticsearch result.
for car in qs:
    print(car.name)

Fields

Once again the django_elasticsearch_dsl.fields are subclasses of elasticsearch-dsl-py fields. They just add support for retrieving data from django models.

Using Different Attributes for Model Fields

Let's say you don't want to store the type of the car as an integer, but as the corresponding string instead. You need some way to convert the type field on the model to a string, so we'll just add a method for it:

# models.py

class Car(models.Model):
    # ... #
    def type_to_string(self):
        """Convert the type field to its string representation
        (the boneheaded way).
        """
        if self.type == 1:
            return "Sedan"
        elif self.type == 2:
            return "Truck"
        else:
            return "SUV"

Now we need to tell our DocType subclass to use that method instead of just accessing the type field on the model directly. Change the CarDocument to look like this:

# documents.py

from django_elasticsearch_dsl import DocType, fields

# ... #

@car.doc_type
class CarDocument(DocType):
    # add a string field to the Elasticsearch mapping called type, the
    # value of which is derived from the model's type_to_string attribute
    type = fields.TextField(attr="type_to_string")

    class Meta:
        model = Car
        # we removed the type field from here
        fields = [
            'name',
            'color',
            'description',
        ]

After a change like this we need to rebuild the index with:

$ ./manage.py search_index --rebuild

Using prepare_field

Sometimes, you need to do some extra prepping before a field should be saved to Elasticsearch. You can add a prepare_foo(self, instance) method to a DocType (where foo is the name of the field), and that will be called when the field needs to be saved.

# documents.py

# ... #

class CarDocument(DocType):
    # ... #

    foo = TextField()

    def prepare_foo(self, instance):
        return " ".join(instance.foos)

Handle relationship with NestedField/ObjectField

For example for a model with ForeignKey relationships.

# models.py

class Car(models.Model):
    name = models.CharField()
    color = models.CharField()
    manufacturer = models.ForeignKey('Manufacturer')

class Manufacturer(models.Model):
    name = models.CharField()
    country_code = models.CharField(max_length=2)
    created = models.DateField()

class Ad(models.Model):
    title = models.CharField()
    description = models.TextField()
    created = models.DateField(auto_now_add=True)
    modified = models.DateField(auto_now=True)
    url = models.URLField()
    car = models.ForeignKey('Car', related_name='ads')

You can use an ObjectField or a NestedField.

# documents.py

from django_elasticsearch_dsl import DocType, Index, fields
from .models import Car, Manufacturer, Ad

car = Index('cars')
car.settings(
    number_of_shards=1,
    number_of_replicas=0
)


@car.doc_type
class CarDocument(DocType):
    manufacturer = fields.ObjectField(properties={
        'name': fields.TextField(),
        'country_code': fields.TextField(),
    })
    ads = fields.NestedField(properties={
        'description': fields.TextField(analyzer=html_strip),
        'title': fields.TextField(),
        'pk': fields.IntegerField(),
    })

    class Meta:
        model = Car
        fields = [
            'name',
            'color',
        ]
        related_models = [Manufacturer, Ad]  # Optional: to ensure the Car will be re-saved when Manufacturer or Ad is updated

    def get_queryset(self):
        """Not mandatory but to improve performance we can select related in one sql request"""
        return super(CarDocument, self).get_queryset().select_related(
            'manufacturer'
        )

    def get_instances_from_related(self, related_instance):
        """If related_models is set, define how to retrieve the Car instance(s) from the related model.
        The related_models option should be used with caution because it can lead in the index
        to the updating of a lot of items.
        """
        if isinstance(related_instance, Manufacturer):
            return related_instance.car_set.all()
        elif isinstance(related_instance, Ad):
            return related_instance.car

Field Classes

Most Elasticsearch field types are supported. The attr argument is a dotted "attribute path" which will be looked up on the model using Django template semantics (dict lookup, attribute lookup, list index lookup). By default the attr argument is set to the field name.

For the rest, the field properties are the same as elasticsearch-dsl fields.

So for example you can use a custom analyzer:

# documents.py

# ... #

html_strip = analyzer(
    'html_strip',
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)

@car.doc_type
class CarDocument(DocType):
    description = fields.TextField(
        analyzer=html_strip,
        fields={'raw': fields.KeywordField()}
    )

    class Meta:
        model = Car
        fields = [
            'name',
            'color',
        ]

Available Fields

  • Simple Fields

    • BooleanField(attr=None, **elasticsearch_properties)
    • ByteField(attr=None, **elasticsearch_properties)
    • CompletionField(attr=None, **elasticsearch_properties)
    • DateField(attr=None, **elasticsearch_properties)
    • DoubleField(attr=None, **elasticsearch_properties)
    • FileField(attr=None, **elasticsearch_properties)
    • FloatField(attr=None, **elasticsearch_properties)
    • IntegerField(attr=None, **elasticsearch_properties)
    • IpField(attr=None, **elasticsearch_properties)
    • GeoPointField(attr=None, **elasticsearch_properties)
    • GeoShapeField(attr=None, **elasticsearch_properties)
    • ShortField(attr=None, **elasticsearch_properties)
    • StringField(attr=None, **elasticsearch_properties)
  • Complex Fields

    • ObjectField(properties, attr=None, **elasticsearch_properties)
    • NestedField(properties, attr=None, **elasticsearch_properties)
  • Elasticsearch >=5 Fields

    • TextField(attr=None, **elasticsearch_properties)
    • KeywordField(attr=None, **elasticsearch_properties)

properties is a dict where the key is a field name, and the value is a field instance.

Index

To define an Elasticsearch index you must instantiate a django_elasticsearch_dsl.Index class and set the name and settings of the index. This class inherits from elasticsearch-dsl-py Index. After you instantiate your class, you need to associate it with the DocType you want to put in this Elasticsearch index.

# documents.py

from django_elasticsearch_dsl import DocType, Index
from .models import Car, Manufacturer

# The name of your index
car = Index('cars')
# See Elasticsearch Indices API reference for available settings
car.settings(
    number_of_shards=1,
    number_of_replicas=0
)


@car.doc_type
class CarDocument(DocType):
    class Meta:
        model = Car
        fields = [
            'name',
            'color',
        ]

@car.doc_type
class ManufacturerDocument(DocType):
    class Meta:
        model = Car
        fields = [
            'name', # If a field as the same name in multiple DocType of
                    # the same Index, the field type must be identical
                    # (here fields.TextField)
            'country_code',
        ]

When you execute the command:

$ ./manage.py search_index --rebuild

This will create an index named cars in Elasticsearch with two mappings: manufacturer_document and car_document.

Management Commands

Delete all indices in Elasticsearch or only the indices associate with a model (--models):

$ search_index --delete [-f] [--models [app[.model] app[.model] ...]]

Create the indices and their mapping in Elasticsearch:

$ search_index --create [--models [app[.model] app[.model] ...]]

Populate the Elasticsearch mappings with the django models data (index need to be existing):

$ search_index --populate [--models [app[.model] app[.model] ...]]

Recreate and repopulate the indices:

$ search_index --rebuild [-f] [--models [app[.model] app[.model] ...]]

Settings

ELASTICSEARCH_DSL_AUTOSYNC

Default: True

Set to False to globally disable auto-syncing.

ELASTICSEARCH_DSL_INDEX_SETTINGS

Default: {}

Additional options passed to the elasticsearch-dsl Index settings (like number_of_replicas or number_of_shards).

ELASTICSEARCH_DSL_AUTO_REFRESH

Default: True

Set to False not force an [index refresh](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-refresh.html) with every save.

ELASTICSEARCH_DSL_SIGNAL_PROCESSOR

This (optional) setting controls what SignalProcessor class is used to handle Django's signals and keep the search index up-to-date.

An example:

ELASTICSEARCH_DSL_SIGNAL_PROCESSOR = 'django_elasticsearch_dsl.signals.RealTimeSignalProcessor'

Defaults to django_elasticsearch_dsl.signals.RealTimeSignalProcessor.

You could, for instance, make a CelerySignalProcessor which would add update jobs to the queue to for delayed processing.

Testing

You can run the tests by creating a Python virtual environment, installing the requirements from requirements_test.txt (pip install -r requirements_test):

$ python runtests.py

Or:

$ make test

$ make test-all # for tox testing

For integration testing with a running Elasticsearch server:

$ python runtests.py --elasticsearch [localhost:9200]

TODO

  • Add support for --using (use another Elasticsearch cluster) in management commands.
  • Add management commands for mapping level operations (like update_mapping....).
  • Dedicated documentation.
  • Generate ObjectField/NestField properties from a DocType class.
  • More examples.
  • Better ESTestCase and documentation for testing