# `SearchQuerySet` API

class SearchQuerySet(using=None, query=None)

The `SearchQuerySet` class is designed to make performing a search and iterating over its results easy and consistent. For those familiar with Django’s ORM `QuerySet`, much of the `SearchQuerySet` API should feel familiar.

## Why Follow `QuerySet`?

A couple reasons to follow (at least in part) the `QuerySet` API:

1. Consistency with Django

2. Most Django programmers have experience with the ORM and can use this knowledge with `SearchQuerySet`.

And from a high-level perspective, `QuerySet` and `SearchQuerySet` do very similar things: given certain criteria, provide a set of results. Both are powered by multiple backends, both are abstractions on top of the way a query is performed.

## Quick Start

For the impatient:

    from haystack.query import SearchQuerySet
    all_results = SearchQuerySet().all()
    hello_results = SearchQuerySet().filter(content='hello')
    hello_world_results = SearchQuerySet().filter(content='hello world')
    unfriendly_results = SearchQuerySet().exclude(content='hello').filter(content='world')
    recent_results = SearchQuerySet().order_by('-pub_date')[:5]

    # Using the new input types...
    from haystack.inputs import AutoQuery, Exact, Clean
    sqs = SearchQuerySet().filter(content=AutoQuery(request.GET['q']), product_type=Exact('ancient book'))

    if request.GET['product_url']:
        sqs = sqs.filter(product_url=Clean(request.GET['product_url']))

For more on the `AutoQuery`, `Exact`, `Clean` classes & friends, see the Input Types documentation.

## `SearchQuerySet`

By default, `SearchQuerySet` provide the documented functionality. You can extend with your own behavior by simply subclassing from `SearchQuerySet` and adding what you need, then using your subclass in place of `SearchQuerySet`.

Most methods in `SearchQuerySet` “chain” in a similar fashion to `QuerySet`. Additionally, like `QuerySet`, `SearchQuerySet` is lazy (meaning it evaluates the query as late as possible). So the following is valid:

    from haystack.query import SearchQuerySet
    results = SearchQuerySet().exclude(content='hello').filter(content='world').order_by('-pub_date').boost('title', 0.5)[10:20]

## The `content` Shortcut

Searching your document fields is a very common activity. To help mitigate possible differences in `SearchField` names (and to help the backends deal with search queries that inspect the main corpus), there is a special field called `content`. You may use this in any place that other fields names would work (e.g. `filter`, `exclude`, etc.) to indicate you simply want to search the main documents.

For example:

    from haystack.query import SearchQuerySet

    # This searches whatever fields were marked ``document=True``.
    results = SearchQuerySet().exclude(content='hello')

This special pseudo-field works best with the `exact` lookup and may yield strange or unexpected results with the other lookups.

## `SearchQuerySet` Methods

The primary interface to search in Haystack is through the `SearchQuerySet` object. It provides a clean, programmatic, portable API to the search backend. Many aspects are also “chainable”, meaning you can call methods one after another, each applying their changes to the previous `SearchQuerySet` and further narrowing the search.

All `SearchQuerySet` objects implement a list-like interface, meaning you can perform actions like getting the length of the results, accessing a result at an offset or even slicing the result list.

### Methods That Return A `SearchQuerySet`

#### `all`

SearchQuerySet.all(self):

Returns all results for the query. This is largely a no-op (returns an identical copy) but useful for denoting exactly what behavior is going on.

#### `none`

SearchQuerySet.none(self):

Returns an `EmptySearchQuerySet` that behaves like a `SearchQuerySet` but always yields no results.

#### `filter`

SearchQuerySet.filter(self, **kwargs)

Filters the search by looking for (and including) certain attributes.

The lookup parameters (`**kwargs`) should follow the Field lookups below. If you specify more than one pair, they will be joined in the query according to the `HAYSTACK_DEFAULT_OPERATOR` setting (defaults to `AND`).

You can pass it either strings or a variety of Input Types if you need more advanced query behavior.

Warning

Any data you pass to `filter` gets auto-escaped. If you need to send non-escaped data, use the `Raw` input type (Input Types).

Also, if a string with one or more spaces in it is specified as the value, the string will get passed along AS IS. This will mean that it will NOT be treated as a phrase (like Haystack 1.X’s behavior).

If you want to match a phrase, you should use either the `__exact` filter type or the `Exact` input type (Input Types).

Examples:

    sqs = SearchQuerySet().filter(content='foo')

    sqs = SearchQuerySet().filter(content='foo', pub_date__lte=datetime.date(2008, 1, 1))

    # Identical to the previous example.
    sqs = SearchQuerySet().filter(content='foo').filter(pub_date__lte=datetime.date(2008, 1, 1))

    # To send unescaped data:
    from haystack.inputs import Raw
    sqs = SearchQuerySet().filter(title=Raw(trusted_query))

    # To use auto-query behavior on a non-``document=True`` field.
    from haystack.inputs import AutoQuery
    sqs = SearchQuerySet().filter(title=AutoQuery(user_query))

#### `exclude`

SearchQuerySet.exclude(self, **kwargs)

Narrows the search by ensuring certain attributes are not included.

Warning

Any data you pass to `exclude` gets auto-escaped. If you need to send non-escaped data, use the `Raw` input type (Input Types).

Example:

    sqs = SearchQuerySet().exclude(content='foo')

#### `filter_and`

SearchQuerySet.filter_and(self, **kwargs)

Narrows the search by looking for (and including) certain attributes. Join behavior in the query is forced to be `AND`. Used primarily by the `filter` method.

#### `filter_or`

SearchQuerySet.filter_or(self, **kwargs)

Narrows the search by looking for (and including) certain attributes. Join behavior in the query is forced to be `OR`. Used primarily by the `filter` method.

#### `order_by`

SearchQuerySet.order_by(self, *args)

Alters the order in which the results should appear. Arguments should be strings that map to the attributes/fields within the index. You may specify multiple fields by comma separating them:

    SearchQuerySet().filter(content='foo').order_by('author', 'pub_date')

Default behavior is ascending order. To specify descending order, prepend the string with a `-`:

    SearchQuerySet().filter(content='foo').order_by('-pub_date')

Note

In general, ordering is locale-specific. Haystack makes no effort to try to reconcile differences between characters from different languages. This means that accented characters will sort closely with the same character and NOT necessarily close to the unaccented form of the character.

If you want this kind of behavior, you should override the `prepare_FOO` methods on your `SearchIndex` objects to transliterate the characters as you see fit.

#### `highlight`

SearchQuerySet.highlight(self)

If supported by the backend, the `SearchResult` objects returned will include a highlighted version of the result:

    sqs = SearchQuerySet().filter(content='foo').highlight()
    result = sqs[0]
    result.highlighted['text'][0] # u'Two computer scientists walk into a bar. The bartender says "<em>Foo</em>!".'

The default functionality of the highlighter may not suit your needs. You can pass additional keyword arguments to `highlight` that will ultimately be used to build the query for your backend. Depending on the available arguments for your backend, you may need to pass in a dictionary instead of normal keyword arguments:

    # Solr defines the fields to higlight by the ``hl.fl`` param. If not specified, we
    # would only get `text` back in the ``highlighted`` dict.
    kwargs = {
        'hl.fl': 'other_field',
        'hl.simple.pre': '<span class="highlighted">',
        'hl.simple.post': '</span>'
    }
    sqs = SearchQuerySet().filter(content='foo').highlight(**kwargs)
    result = sqs[0]
    result.highlighted['other_field'][0] # u'Two computer scientists walk into a bar. The bartender says "<span class="highlighted">Foo</span>!".'

Elasticsearch accepts keyword arguments:

    # Use the ``pre_tag`` and ``post_tag`` keywords and pass the desired tags as lists.
    sqs = SearchQuerySet().filter(content='foo').highlight(
        pre_tags=['<strong>'], post_tags=['</strong>'])
    result_example = " ".join(sqs[0].highlighted)
    # u'Two <strong>foo</strong> computer scientists walk into a bar. The bartender says "<strong>Foo</strong>!"'

#### `models`

SearchQuerySet.models(self, *models)

Accepts an arbitrary number of Model classes to include in the search. This will narrow the search results to only include results from the models specified.

Example:

    SearchQuerySet().filter(content='foo').models(BlogEntry, Comment)

#### `result_class`

SearchQuerySet.result_class(self, klass)

Allows specifying a different class to use for results.

Overrides any previous usages. If `None` is provided, Haystack will revert back to the default `SearchResult` object.

Example:

    SearchQuerySet().result_class(CustomResult)

#### `boost`

SearchQuerySet.boost(self, term, boost_value)

Boosts a certain term of the query. You provide the term to be boosted and the value is the amount to boost it by. Boost amounts may be either an integer or a float.

Example:

    SearchQuerySet().filter(content='foo').boost('bar', 1.5)

#### `facet`

SearchQuerySet.facet(self, field, **options)

Adds faceting to a query for the provided field. You provide the field (from one of the `SearchIndex` classes) you like to facet on. Any keyword options you provide will be passed along to the backend for that facet.

Example:

    # For SOLR (setting f.author.facet.*; see http://wiki.apache.org/solr/SimpleFacetParameters#Parameters)
    SearchQuerySet().facet('author', mincount=1, limit=10)
    # For Elasticsearch (see http://www.elasticsearch.org/guide/reference/api/search/facets/terms-facet.html)
    SearchQuerySet().facet('author', size=10, order='term')

In the search results you get back, facet counts will be populated in the `SearchResult` object. You can access them via the `facet_counts` method.

Example:

    # Count document hits for each author within the index.
    SearchQuerySet().filter(content='foo').facet('author')

#### `date_facet`

SearchQuerySet.date_facet(self, field, start_date, end_date, gap_by, gap_amount=1)

Adds faceting to a query for the provided field by date. You provide the field (from one of the `SearchIndex` classes) you like to facet on, a `start_date` (either `datetime.datetime` or `datetime.date`), an `end_date` and the amount of time between gaps as `gap_by` (one of `'year'`, `'month'`, `'day'`, `'hour'`, `'minute'` or `'second'`).

You can also optionally provide a `gap_amount` to specify a different increment than `1`. For example, specifying gaps by week (every seven days) would be `gap_by='day', gap_amount=7`).

In the search results you get back, facet counts will be populated in the `SearchResult` object. You can access them via the `facet_counts` method.

Example:

    # Count document hits for each day between 2009-06-07 to 2009-07-07 within the index.
    SearchQuerySet().filter(content='foo').date_facet('pub_date', start_date=datetime.date(2009, 6, 7), end_date=datetime.date(2009, 7, 7), gap_by='day')

#### `query_facet`

SearchQuerySet.query_facet(self, field, query)

Adds faceting to a query for the provided field with a custom query. You provide the field (from one of the `SearchIndex` classes) you like to facet on and the backend-specific query (as a string) you’d like to execute.

Please note that this is NOT portable between backends. The syntax is entirely dependent on the backend. No validation/cleansing is performed and it is up to the developer to ensure the query’s syntax is correct.

In the search results you get back, facet counts will be populated in the `SearchResult` object. You can access them via the `facet_counts` method.

Example:

    # Count document hits for authors that start with 'jo' within the index.
    SearchQuerySet().filter(content='foo').query_facet('author', 'jo*')

#### `within`

SearchQuerySet.within(self, field, point_1, point_2):

Spatial: Adds a bounding box search to the query.

See the Spatial Search docs for more information.

#### `dwithin`

SearchQuerySet.dwithin(self, field, point, distance):

Spatial: Adds a distance-based search to the query.

See the Spatial Search docs for more information.

#### `stats`

SearchQuerySet.stats(self, field):

Adds stats to a query for the provided field. This is supported on Solr only. You provide the field (from one of the `SearchIndex` classes) you would like stats on.

In the search results you get back, stats will be populated in the `SearchResult` object. You can access them via the `` stats_results`` method.

Example:

    # Get stats on the author field.
    SearchQuerySet().filter(content='foo').stats('author')

#### `stats_facet`

SearchQuerySet.stats_facet(self, field,

Adds stats facet for the given field and facet_fields represents the faceted fields. This is supported on Solr only.

Example:

    # Get stats on the author field, and stats on the author field
    faceted by bookstore.
    SearchQuerySet().filter(content='foo').stats_facet('author','bookstore')

#### `distance`

SearchQuerySet.distance(self, field, point):

Spatial: Denotes results must have distance measurements from the provided point.

See the Spatial Search docs for more information.

#### `narrow`

SearchQuerySet.narrow(self, query)

Pulls a subset of documents from the search engine to search within. This is for advanced usage, especially useful when faceting.

Example:

    # Search, from recipes containing 'blend', for recipes containing 'banana'.
    SearchQuerySet().narrow('blend').filter(content='banana')

    # Using a fielded search where the recipe's title contains 'smoothie', find all recipes published before 2009.
    SearchQuerySet().narrow('title:smoothie').filter(pub_date__lte=datetime.datetime(2009, 1, 1))

By using `narrow`, you can create drill-down interfaces for faceting by applying `narrow` calls for each facet that gets selected.

This method is different from `SearchQuerySet.filter()` in that it does not affect the query sent to the engine. It pre-limits the document set being searched. Generally speaking, if you’re in doubt of whether to use `filter` or `narrow`, use `filter`.

Note

This method is, generally speaking, not necessarily portable between backends. The syntax is entirely dependent on the backend, though most backends have a similar syntax for basic fielded queries. No validation/cleansing is performed and it is up to the developer to ensure the query’s syntax is correct.

#### `raw_search`

SearchQuerySet.raw_search(self, query_string, **kwargs)

Passes a raw query directly to the backend. This is for advanced usage, where the desired query can not be expressed via `SearchQuerySet`.

This method is still supported, however it now uses the much more flexible `Raw` input type (Input Types).

Warning

Different from Haystack 1.X, this method no longer causes immediate evaluation & now chains appropriately.

Example:

    # In the case of Solr... (this example could be expressed with SearchQuerySet)
    SearchQuerySet().raw_search('django_ct:blog.blogentry "However, it is"')

    # Equivalent.
    from haystack.inputs import Raw
    sqs = SearchQuerySet().filter(content=Raw('django_ct:blog.blogentry "However, it is"'))

Please note that this is NOT portable between backends. The syntax is entirely dependent on the backend. No validation/cleansing is performed and it is up to the developer to ensure the query’s syntax is correct.

Further, the use of `**kwargs` are completely undocumented intentionally. If a third-party backend can implement special features beyond what’s present, it should use those `**kwargs` for passing that information. Developers should be careful to make sure there are no conflicts with the backend’s `search` method, as that is called directly.

#### `load_all`

SearchQuerySet.load_all(self)

Efficiently populates the objects in the search results. Without using this method, DB lookups are done on a per-object basis, resulting in many individual trips to the database. If `load_all` is used, the `SearchQuerySet` will group similar objects into a single query, resulting in only as many queries as there are different object types returned.

Example:

    SearchQuerySet().filter(content='foo').load_all()

#### `auto_query`

SearchQuerySet.auto_query(self, query_string, fieldname=None)

Performs a best guess constructing the search query.

This method is intended for common use directly with a user’s query. This method is still supported, however it now uses the much more flexible `AutoQuery` input type (Input Types).

It handles exact matches (specified with single or double quotes), negation ( using a `-` immediately before the term) and joining remaining terms with the operator specified in `HAYSTACK_DEFAULT_OPERATOR`.

Example:

    sqs = SearchQuerySet().auto_query('goldfish "old one eye" -tank')

    # Equivalent.
    from haystack.inputs import AutoQuery
    sqs = SearchQuerySet().filter(content=AutoQuery('goldfish "old one eye" -tank'))

    # Against a different field.
    sqs = SearchQuerySet().filter(title=AutoQuery('goldfish "old one eye" -tank'))

#### `autocomplete`

A shortcut method to perform an autocomplete search.

Must be run against fields that are either `NgramField` or `EdgeNgramField`.

Example:

    SearchQuerySet().autocomplete(title_autocomplete='gol')

#### `more_like_this`

SearchQuerySet.more_like_this(self, model_instance)

Finds similar results to the object passed in.

You should pass in an instance of a model (for example, one fetched via a `get` in Django’s ORM). This will execute a query on the backend that searches for similar results. The instance you pass in should be an indexed object. Previously called methods will have an effect on the provided results.

It will evaluate its own backend-specific query and populate the `SearchQuerySet` in the same manner as other methods.

Example:

    entry = Entry.objects.get(slug='haystack-one-oh-released')
    mlt = SearchQuerySet().more_like_this(entry)
    mlt.count() # 5
    mlt[0].object.title # "Haystack Beta 1 Released"

    # ...or...
    mlt = SearchQuerySet().filter(public=True).exclude(pub_date__lte=datetime.date(2009, 7, 21)).more_like_this(entry)
    mlt.count() # 2
    mlt[0].object.title # "Haystack Beta 1 Released"

#### `using`

SearchQuerySet.using(self, connection_name)

Allows switching which connection the `SearchQuerySet` uses to search in.

Example:

    # Let the routers decide which connection to use.
    sqs = SearchQuerySet().all()

    # Specify the 'default'.
    sqs = SearchQuerySet().all().using('default')

### Methods That Do Not Return A `SearchQuerySet`

#### `count`

SearchQuerySet.count(self)

Returns the total number of matching results.

This returns an integer count of the total number of results the search backend found that matched. This method causes the query to evaluate and run the search.

Example:

    SearchQuerySet().filter(content='foo').count()

#### `best_match`

SearchQuerySet.best_match(self)

Returns the best/top search result that matches the query.

This method causes the query to evaluate and run the search. This method returns a `SearchResult` object that is the best match the search backend found:

    foo = SearchQuerySet().filter(content='foo').best_match()
    foo.id # Something like 5.

    # Identical to:
    foo = SearchQuerySet().filter(content='foo')[0]

#### `latest`

SearchQuerySet.latest(self, date_field)

Returns the most recent search result that matches the query.

This method causes the query to evaluate and run the search. This method returns a `SearchResult` object that is the most recent match the search backend found:

    foo = SearchQuerySet().filter(content='foo').latest('pub_date')
    foo.id # Something like 3.

    # Identical to:
    foo = SearchQuerySet().filter(content='foo').order_by('-pub_date')[0]

#### `facet_counts`

SearchQuerySet.facet_counts(self)

Returns the facet counts found by the query. This will cause the query to execute and should generally be used when presenting the data (template-level).

You receive back a dictionary with three keys: `fields`, `dates` and `queries`. Each contains the facet counts for whatever facets you specified within your `SearchQuerySet`.

Note

The resulting dictionary may change before 1.0 release. It’s fairly backend-specific at the time of writing. Standardizing is waiting on implementing other backends that support faceting and ensuring that the results presented will meet their needs as well.

Example:

    # Count document hits for each author.
    sqs = SearchQuerySet().filter(content='foo').facet('author')

    sqs.facet_counts()
    # Gives the following response:
    # {
    #     'dates': {},
    #     'fields': {
    #         'author': [
    #             ('john', 4),
    #             ('daniel', 2),
    #             ('sally', 1),
    #             ('terry', 1),
    #         ],
    #     },
    #     'queries': {}
    # }

#### `stats_results`

SearchQuerySet.stats_results(self):

Returns the stats results found by the query.

This will cause the query to execute and should generally be used when presenting the data (template-level).

You receive back a dictionary with three keys: `fields`, `dates` and `queries`. Each contains the facet counts for whatever facets you specified within your `SearchQuerySet`.

Note

The resulting dictionary may change before 1.0 release. It’s fairly backend-specific at the time of writing. Standardizing is waiting on implementing other backends that support faceting and ensuring that the results presented will meet their needs as well.

Example:

    # Count document hits for each author.
    sqs = SearchQuerySet().filter(content='foo').stats('price')

    sqs.stats_results()

    # Gives the following response
    # {
    #    'stats_fields':{
    #       'author:{
    #            'min': 0.0,
    #            'max': 2199.0,
    #            'sum': 5251.2699999999995,
    #            'count': 15,
    #            'missing': 11,
    #            'sumOfSquares': 6038619.160300001,
    #            'mean': 350.08466666666664,
    #            'stddev': 547.737557906113
    #        }
    #    }
    #
    # }

#### `set_spelling_query`

SearchQuerySet.set_spelling_query(self, spelling_query)

This method allows you to set the text which will be passed to the backend search engine for spelling suggestions. This is helpful when the actual query being sent to the backend has complex syntax which should not be seen by the spelling suggestion component.

In this example, a Solr `edismax` query is being used to boost field and document weights and `set_spelling_query` is being used to send only the actual user-entered text to the spellchecker:

    alt_q = AltParser('edismax', self.query,
                      qf='title^4 text provider^0.5',
                      bq='django_ct:core.item^6.0')
    sqs = sqs.filter(content=alt_q)
    sqs = sqs.set_spelling_query(self.query)

#### `spelling_suggestion`

SearchQuerySet.spelling_suggestion(self, preferred_query=None)

Returns the spelling suggestion found by the query.

To work, you must set `INCLUDE_SPELLING` within your connection’s settings dictionary to `True`, and you must rebuild your index afterwards. Otherwise, `None` will be returned.

This method causes the query to evaluate and run the search if it hasn’t already run. Search results will be populated as normal but with an additional spelling suggestion. Note that this does NOT run the revised query, only suggests improvements.

If provided, the optional argument to this method lets you specify an alternate query for the spelling suggestion to be run on. This is useful for passing along a raw user-provided query, especially when there are many methods chained on the `SearchQuerySet`.

Example:

    sqs = SearchQuerySet().auto_query('mor exmples')
    sqs.spelling_suggestion() # u'more examples'

    # ...or...
    suggestion = SearchQuerySet().spelling_suggestion('moar exmples')
    suggestion # u'more examples'

#### `values`

SearchQuerySet.values(self, *fields)

Returns a list of dictionaries, each containing the key/value pairs for the result, exactly like Django’s `ValuesQuerySet`.

This method causes the query to evaluate and run the search if it hasn’t already run.

You must provide a list of one or more fields as arguments. These fields will be the ones included in the individual results.

Example:

    sqs = SearchQuerySet().auto_query('banana').values('title', 'description')

#### `values_list`

SearchQuerySet.values_list(self, *fields, **kwargs)

Returns a list of field values as tuples, exactly like Django’s `ValuesListQuerySet`.

This method causes the query to evaluate and run the search if it hasn’t already run.

You must provide a list of one or more fields as arguments. These fields will be the ones included in the individual results.

You may optionally also provide a `flat=True` kwarg, which in the case of a single field being provided, will return a flat list of that field rather than a list of tuples.

Example:

    sqs = SearchQuerySet().auto_query('banana').values_list('title', 'description')

    # ...or just the titles as a flat list...
    sqs = SearchQuerySet().auto_query('banana').values_list('title', flat=True)

### Field Lookups

The following lookup types are supported:

- content

- contains

- exact

- gt

- gte

- lt

- lte

- in

- startswith

- endswith

- range

- fuzzy

Except for `fuzzy` these options are similar in function to the way Django’s lookup types work. The actual behavior of these lookups is backend-specific.

Warning

The `startswith` filter is strongly affected by the other ways the engine parses data, especially in regards to stemming (see Glossary). This can mean that if the query ends in a vowel or a plural form, it may get stemmed before being evaluated.

This is both backend-specific and yet fairly consistent between engines, and may be the cause of sometimes unexpected results.

Warning

The `content` filter became the new default filter as of Haystack v2.X (the default in Haystack v1.X was `exact`). This changed because `exact` caused problems and was unintuitive for new people trying to use Haystack. `content` is a much more natural usage.

If you had an app built on Haystack v1.X & are upgrading, you’ll need to sanity-check & possibly change any code that was relying on the default. The solution is just to add `__exact` to any “bare” field in a `.filter(...)` clause.

Example:

    SearchQuerySet().filter(content='foo')

    # Identical to:
    SearchQuerySet().filter(content__content='foo')

    # Phrase matching.
    SearchQuerySet().filter(content__exact='hello world')

    # Other usages look like:
    SearchQuerySet().filter(pub_date__gte=datetime.date(2008, 1, 1), pub_date__lt=datetime.date(2009, 1, 1))
    SearchQuerySet().filter(author__in=['daniel', 'john', 'jane'])
    SearchQuerySet().filter(view_count__range=[3, 5])

## `EmptySearchQuerySet`

Also included in Haystack is an `EmptySearchQuerySet` class. It behaves just like `SearchQuerySet` but will always return zero results. This is useful for places where you want no query to occur or results to be returned.

## `RelatedSearchQuerySet`

Sometimes you need to filter results based on relations in the database that are not present in the search index or are difficult to express that way. To this end, `RelatedSearchQuerySet` allows you to post-process the search results by calling `load_all_queryset`.

Warning

`RelatedSearchQuerySet` can have negative performance implications. Because results are excluded based on the database after the search query has been run, you can’t guarantee offsets within the cache. Therefore, the entire cache that appears before the offset you request must be filled in order to produce consistent results. On large result sets and at higher slices, this can take time.

This is the old behavior of `SearchQuerySet`, so performance is no worse than the early days of Haystack.

It supports all other methods that the standard `SearchQuerySet` does, with the addition of the `load_all_queryset` method and paying attention to the `load_all_queryset` method of `SearchIndex` objects when populating the cache.

### `load_all_queryset`

RelatedSearchQuerySet.load_all_queryset(self, model_class, queryset)

Allows for specifying a custom `QuerySet` that changes how `load_all` will fetch records for the provided model. This is useful for post-processing the results from the query, enabling things like adding `select_related` or filtering certain data.

Example:

    sqs = RelatedSearchQuerySet().filter(content='foo').load_all()
    # For the Entry model, we want to include related models directly associated
    # with the Entry to save on DB queries.
    sqs = sqs.load_all_queryset(Entry, Entry.objects.all().select_related(depth=1))

This method chains indefinitely, so you can specify `QuerySets` for as many models as you wish, one per model. The `SearchQuerySet` 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 to do this frequently and have one `QuerySet` you’d like to apply everywhere, you can specify this at the `SearchIndex` level using the `load_all_queryset` method. See SearchIndex API for usage.