SearchIndex
API¶
- class SearchIndex¶
The SearchIndex
class allows the application developer a way to provide data to
the backend in a structured format. Developers familiar with Django’s Form
or Model
classes should find the syntax for indexes familiar.
This class is arguably the most important part of integrating Haystack into your application, as it has a large impact on the quality of the search results and how easy it is for users to find what they’re looking for. Care and effort should be put into making your indexes the best they can be.
Quick Start¶
For the impatient:
import datetime
from haystack import indexes
from myapp.models import Note
class NoteIndex(indexes.SearchIndex, indexes.Indexable):
text = indexes.CharField(document=True, use_template=True)
author = indexes.CharField(model_attr='user')
pub_date = indexes.DateTimeField(model_attr='pub_date')
def get_model(self):
return Note
def index_queryset(self, using=None):
"Used when the entire index for model is updated."
return self.get_model().objects.filter(pub_date__lte=datetime.datetime.now())
Background¶
Unlike relational databases, most search engines supported by Haystack are primarily document-based. They focus on a single text blob which they tokenize, analyze and index. When searching, this field is usually the primary one that is searched.
Further, the schema used by most engines is the same for all types of data added, unlike a relational database that has a table schema for each chunk of data.
It may be helpful to think of your search index as something closer to a key-value store instead of imagining it in terms of a RDBMS.
Why Create Fields?¶
Despite being primarily document-driven, most search engines also support the ability to associate other relevant data with the indexed document. These attributes can be mapped through the use of fields within Haystack.
Common uses include storing pertinent data information, categorizations of the document, author information and related data. By adding fields for these pieces of data, you provide a means to further narrow/filter search terms. This can be useful from either a UI perspective (a better advanced search form) or from a developer standpoint (section-dependent search, off-loading certain tasks to search, et cetera).
Warning
Haystack reserves the following field names for internal use: id
,
django_ct
, django_id
& content
. The name
& type
names
used to be reserved but no longer are.
You can override these field names using the HAYSTACK_ID_FIELD
,
HAYSTACK_DJANGO_CT_FIELD
& HAYSTACK_DJANGO_ID_FIELD
if needed.
Significance Of document=True
¶
Most search engines that were candidates for inclusion in Haystack all had a central concept of a document that they indexed. These documents form a corpus within which to primarily search. Because this ideal is so central and most of Haystack is designed to have pluggable backends, it is important to ensure that all engines have at least a bare minimum of the data they need to function.
As a result, when creating a SearchIndex
, one (and only one) field must be
marked with document=True
. This signifies to Haystack that whatever is
placed in this field while indexing is to be the primary text the search engine
indexes. The name of this field can be almost anything, but text
is one of
the more common names used.
Stored/Indexed Fields¶
One shortcoming of the use of search is that you rarely have all or the most up-to-date information about an object in the index. As a result, when retrieving search results, you will likely have to access the object in the database to provide better information.
However, this can also hit the database quite heavily (think
.get(pk=result.id)
per object). If your search is popular, this can lead
to a big performance hit. There are two ways to prevent this. The first way is
SearchQuerySet.load_all
, which tries to group all similar objects and pull
them through one query instead of many. This still hits the DB and incurs a
performance penalty.
The other option is to leverage stored fields. By default, all fields in Haystack are either indexed (searchable by the engine) or stored (retained by the engine and presented in the results). By using a stored field, you can store commonly used data in such a way that you don’t need to hit the database when processing the search result to get more information.
For example, one great way to leverage this is to pre-render an object’s search result template DURING indexing. You define an additional field, render a template with it and it follows the main indexed record into the index. Then, when that record is pulled when it matches a query, you can simply display the contents of that field, which avoids the database hit.:
Within myapp/search_indexes.py
:
class NoteIndex(SearchIndex, indexes.Indexable):
text = CharField(document=True, use_template=True)
author = CharField(model_attr='user')
pub_date = DateTimeField(model_attr='pub_date')
# Define the additional field.
rendered = CharField(use_template=True, indexed=False)
Then, inside a template named search/indexes/myapp/note_rendered.txt
:
<h2>{{ object.title }}</h2>
<p>{{ object.content }}</p>
And finally, in search/search.html
:
...
{% for result in page.object_list %}
<div class="search_result">
{{ result.rendered|safe }}
</div>
{% endfor %}
Keeping The Index Fresh¶
There are several approaches to keeping the search index in sync with your database. None are more correct than the others and which one you use depends on the traffic you see, the churn rate of your data, and what concerns are important to you (CPU load, how recent, et cetera).
The conventional method is to use SearchIndex
in combination with cron
jobs. Running a ./manage.py update_index
every couple hours will keep your
data in sync within that timeframe and will handle the updates in a very
efficient batch. Additionally, Whoosh (and to a lesser extent Xapian) behaves
better when using this approach.
Another option is to use RealtimeSignalProcessor
, which uses Django’s
signals to immediately update the index any time a model is saved/deleted. This
yields a much more current search index at the expense of being fairly
inefficient. Solr & Elasticsearch are the only backends that handles this well
under load, and even then, you should make sure you have the server capacity
to spare.
A third option is to develop a custom QueuedSignalProcessor
that, much like
RealtimeSignalProcessor
, uses Django’s signals to enqueue messages for
updates/deletes. Then writing a management command to consume these messages
in batches, yielding a nice compromise between the previous two options.
For more information see Signal Processors.
Note
Haystack doesn’t ship with a QueuedSignalProcessor
largely because there is
such a diversity of lightweight queuing options and that they tend to
polarize developers. Queuing is outside of Haystack’s goals (provide good,
powerful search) and, as such, is left to the developer.
Additionally, the implementation is relatively trivial & there are already good third-party add-ons for Haystack to enable this.
Advanced Data Preparation¶
In most cases, using the model_attr parameter on your fields allows you to easily get data from a Django model to the document in your index, as it handles both direct attribute access as well as callable functions within your model.
Note
The model_attr
keyword argument also can look through relations in
models. So you can do something like model_attr='author__first_name'
to pull just the first name of the author, similar to some lookups used
by Django’s ORM.
However, sometimes, even more control over what gets placed in your index is
needed. To facilitate this, SearchIndex
objects have a ‘preparation’ stage
that populates data just before it is indexed. You can hook into this phase in
several ways.
This should be very familiar to developers who have used Django’s forms
before as it loosely follows similar concepts, though the emphasis here is
less on cleansing data from user input and more on making the data friendly
to the search backend.
1. prepare_FOO(self, object)
¶
The most common way to affect a single field’s data is to create a
prepare_FOO
method (where FOO is the name of the field). As a parameter
to this method, you will receive the instance that is attempting to be indexed.
Note
This method is analogous to Django’s Form.clean_FOO
methods.
To keep with our existing example, one use case might be altering the name
inside the author
field to be “firstname lastname <email>”. In this case,
you might write the following code:
class NoteIndex(SearchIndex, indexes.Indexable):
text = CharField(document=True, use_template=True)
author = CharField(model_attr='user')
pub_date = DateTimeField(model_attr='pub_date')
def get_model(self):
return Note
def prepare_author(self, obj):
return "%s <%s>" % (obj.user.get_full_name(), obj.user.email)
This method should return a single value (or list/tuple/dict) to populate that field’s data upon indexing. Note that this method takes priority over whatever data may come from the field itself.
Just like Form.clean_FOO
, the field’s prepare
runs before the
prepare_FOO
, allowing you to access self.prepared_data
. For example:
class NoteIndex(SearchIndex, indexes.Indexable):
text = CharField(document=True, use_template=True)
author = CharField(model_attr='user')
pub_date = DateTimeField(model_attr='pub_date')
def get_model(self):
return Note
def prepare_author(self, obj):
# Say we want last name first, the hard way.
author = u''
if 'author' in self.prepared_data:
name_bits = self.prepared_data['author'].split()
author = "%s, %s" % (name_bits[-1], ' '.join(name_bits[:-1]))
return author
This method is fully function with model_attr
, so if there’s no convenient
way to access the data you want, this is an excellent way to prepare it:
class NoteIndex(SearchIndex, indexes.Indexable):
text = CharField(document=True, use_template=True)
author = CharField(model_attr='user')
categories = MultiValueField()
pub_date = DateTimeField(model_attr='pub_date')
def get_model(self):
return Note
def prepare_categories(self, obj):
# Since we're using a M2M relationship with a complex lookup,
# we can prepare the list here.
return [category.id for category in obj.category_set.active().order_by('-created')]
2. prepare(self, object)
¶
Each SearchIndex
gets a prepare
method, which handles collecting all
the data. This method should return a dictionary that will be the final data
used by the search backend.
Overriding this method is useful if you need to collect more than one piece
of data or need to incorporate additional data that is not well represented
by a single SearchField
. An example might look like:
class NoteIndex(SearchIndex, indexes.Indexable):
text = CharField(document=True, use_template=True)
author = CharField(model_attr='user')
pub_date = DateTimeField(model_attr='pub_date')
def get_model(self):
return Note
def prepare(self, object):
self.prepared_data = super().prepare(object)
# Add in tags (assuming there's a M2M relationship to Tag on the model).
# Note that this would NOT get picked up by the automatic
# schema tools provided by Haystack.
self.prepared_data['tags'] = [tag.name for tag in object.tags.all()]
return self.prepared_data
If you choose to use this method, you should make a point to be careful to call
the super()
method before altering the data. Without doing so, you may have
an incomplete set of data populating your indexes.
This method has the final say in all data, overriding both what the fields
provide as well as any prepare_FOO
methods on the class.
Note
This method is roughly analogous to Django’s Form.full_clean
and
Form.clean
methods. However, unlike these methods, it is not fired
as the result of trying to access self.prepared_data
. It requires
an explicit call.
3. Overriding prepare(self, object)
On Individual SearchField
Objects¶
The final way to manipulate your data is to implement a custom SearchField
object and write its prepare
method to populate/alter the data any way you
choose. For instance, a (naive) user-created GeoPointField
might look
something like:
from haystack import indexes
class GeoPointField(indexes.CharField):
def __init__(self, **kwargs):
kwargs['default'] = '0.00-0.00'
super().__init__(**kwargs)
def prepare(self, obj):
return "%s-%s" % (obj.latitude, obj.longitude)
The prepare
method simply returns the value to be used for that field. It’s
entirely possible to include data that’s not directly referenced to the object
here, depending on your needs.
Note that this is NOT a recommended approach to storing geographic data in a search engine (there is no formal suggestion on this as support is usually non-existent), merely an example of how to extend existing fields.
Note
This method is analogous to Django’s Field.clean
methods.
Adding New Fields¶
If you have an existing SearchIndex
and you add a new field to it, Haystack
will add this new data on any updates it sees after that point. However, this
will not populate the existing data you already have.
In order for the data to be picked up, you will need to run ./manage.py
rebuild_index
. This will cause all backends to rebuild the existing data
already present in the quickest and most efficient way.
Note
With the Solr backend, you’ll also have to add to the appropriate
schema.xml
for your configuration before running the rebuild_index
.
Search Index
¶
get_model
¶
- SearchIndex.get_model(self)¶
Should return the Model
class (not an instance) that the rest of the
SearchIndex
should use.
This method is required & you must override it to return the correct class.
index_queryset
¶
- SearchIndex.index_queryset(self, using=None)¶
Get the default QuerySet to index when doing a full update.
Subclasses can override this method to avoid indexing certain objects.
read_queryset
¶
- SearchIndex.read_queryset(self, using=None)¶
Get the default QuerySet for read actions.
Subclasses can override this method to work with other managers. Useful when working with default managers that filter some objects.
build_queryset
¶
- SearchIndex.build_queryset(self, start_date=None, end_date=None)¶
Get the default QuerySet to index when doing an index update.
Subclasses can override this method to take into account related model modification times.
The default is to use SearchIndex.index_queryset
and filter
based on SearchIndex.get_updated_field
prepare
¶
- SearchIndex.prepare(self, obj)¶
Fetches and adds/alters data before indexing.
get_content_field
¶
- SearchIndex.get_content_field(self)¶
Returns the field that supplies the primary document to be indexed.
update
¶
- SearchIndex.update(self, using=None)¶
Updates the entire index.
If using
is provided, it specifies which connection should be
used. Default relies on the routers to decide which backend should
be used.
update_object
¶
- SearchIndex.update_object(self, instance, using=None, **kwargs)¶
Update the index for a single object. Attached to the class’s post-save hook.
If using
is provided, it specifies which connection should be
used. Default relies on the routers to decide which backend should
be used.
remove_object
¶
- SearchIndex.remove_object(self, instance, using=None, **kwargs)¶
Remove an object from the index. Attached to the class’s post-delete hook.
If using
is provided, it specifies which connection should be
used. Default relies on the routers to decide which backend should
be used.
clear
¶
- SearchIndex.clear(self, using=None)¶
Clears the entire index.
If using
is provided, it specifies which connection should be
used. Default relies on the routers to decide which backend should
be used.
reindex
¶
- SearchIndex.reindex(self, using=None)¶
Completely clears the index for this model and rebuilds it.
If using
is provided, it specifies which connection should be
used. Default relies on the routers to decide which backend should
be used.
get_updated_field
¶
- SearchIndex.get_updated_field(self)¶
Get the field name that represents the updated date for the model.
If specified, this is used by the reindex command to filter out results
from the QuerySet
, enabling you to reindex only recent records. This
method should either return None (reindex everything always) or a
string of the Model
’s DateField
/DateTimeField
name.
should_update
¶
- SearchIndex.should_update(self, instance, **kwargs)¶
Determine if an object should be updated in the index.
It’s useful to override this when an object may save frequently and cause excessive reindexing. You should check conditions on the instance and return False if it is not to be indexed.
The kwargs
passed along to this method can be the same as the ones passed
by Django when a Model is saved/delete, so it’s possible to check if the object
has been created or not. See django.db.models.signals.post_save
for details
on what is passed.
By default, returns True (always reindex).
load_all_queryset
¶
- SearchIndex.load_all_queryset(self)¶
Provides the ability to override how objects get loaded in conjunction
with RelatedSearchQuerySet.load_all
. This is useful for post-processing the
results from the query, enabling things like adding select_related
or
filtering certain data.
Warning
Utilizing this functionality can have negative performance implications.
Please see the section on RelatedSearchQuerySet
within
SearchQuerySet API for further information.
By default, returns all()
on the model’s default manager.
Example:
class NoteIndex(SearchIndex, indexes.Indexable):
text = CharField(document=True, use_template=True)
author = CharField(model_attr='user')
pub_date = DateTimeField(model_attr='pub_date')
def get_model(self):
return Note
def load_all_queryset(self):
# Pull all objects related to the Note in search results.
return Note.objects.all().select_related()
When searching, the RelatedSearchQuerySet
appends on a call to in_bulk
, so be
sure that the QuerySet
you provide can accommodate this and that the ids
passed to in_bulk
will map to the model in question.
If you need a specific QuerySet
in one place, you can specify this at the
RelatedSearchQuerySet
level using the load_all_queryset
method. See
SearchQuerySet API for usage.
ModelSearchIndex
¶
The ModelSearchIndex
class allows for automatic generation of a
SearchIndex
based on the fields of the model assigned to it.
With the exception of the automated introspection, it is a SearchIndex
class, so all notes above pertaining to SearchIndexes
apply. As with the
ModelForm
class in Django, it employs an inner class called Meta
, which
should contain a model
attribute. By default all non-relational model
fields are included as search fields on the index, but fields can be restricted
by way of a fields
whitelist, or excluded with an excludes
list, to
prevent certain fields from appearing in the class.
In addition, it adds a text field that is the document=True
field and
has use_template=True option set, just like the BasicSearchIndex
.
Warning
Usage of this class might result in inferior SearchIndex
objects, which
can directly affect your search results. Use this to establish basic
functionality and move to custom SearchIndex objects for better control.
At this time, it does not handle related fields.
Quick Start¶
For the impatient:
import datetime
from haystack import indexes
from myapp.models import Note
# All Fields
class AllNoteIndex(indexes.ModelSearchIndex, indexes.Indexable):
class Meta:
model = Note
# Blacklisted Fields
class LimitedNoteIndex(indexes.ModelSearchIndex, indexes.Indexable):
class Meta:
model = Note
excludes = ['user']
# Whitelisted Fields
class NoteIndex(indexes.ModelSearchIndex, indexes.Indexable):
class Meta:
model = Note
fields = ['user', 'pub_date']
# Note that regular ``SearchIndex`` methods apply.
def index_queryset(self, using=None):
"Used when the entire index for model is updated."
return Note.objects.filter(pub_date__lte=datetime.datetime.now())