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))
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')
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')
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>!"'
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)
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)
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)
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')
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')
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*')
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')
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')
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.
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()
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')
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"
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')
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()
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]
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]
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.
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': {}
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.
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
# }
# }
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)
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'
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
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')
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
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)
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:
