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import logging
import operator
import warnings
from haystack import connections, connection_router
from haystack.backends import SQ
from haystack.constants import REPR_OUTPUT_SIZE, ITERATOR_LOAD_PER_QUERY, DEFAULT_OPERATOR
from haystack.exceptions import NotHandled
from haystack.inputs import Raw, Clean, AutoQuery
class SearchQuerySet(object):
"""
Provides a way to specify search parameters and lazily load results.
Supports chaining (a la QuerySet) to narrow the search.
"""
def __init__(self, using=None, query=None):
# ``_using`` should only ever be a value other than ``None`` if it's
# been forced with the ``.using`` method.
self._using = using
self.query = None
self._determine_backend()
# If ``query`` is present, it should override even what the routers
# think.
if query is not None:
self.query = query
self._result_cache = []
self._result_count = None
self._cache_full = False
self._load_all = False
self._ignored_result_count = 0
self.log = logging.getLogger('haystack')
def _determine_backend(self):
# A backend has been manually selected. Use it instead.
if self._using is not None:
return self._using
# No backend, so rely on the routers to figure out what's right.
from haystack import connections
hints = {}
if self.query:
hints['models'] = self.query.models
backend_alias = connection_router.for_read(**hints)
# The ``SearchQuery`` might swap itself out for a different variant
# here.
if self.query:
self.query = self.query.using(backend_alias)
else:
self.query = connections[backend_alias].get_query()
def __getstate__(self):
"""
For pickling.
"""
len(self)
obj_dict = self.__dict__.copy()
obj_dict['_iter'] = None
obj_dict['log'] = None
return obj_dict
def __setstate__(self, data_dict):
"""
For unpickling.
"""
self.__dict__ = data_dict
self.log = logging.getLogger('haystack')
def __repr__(self):
data = list(self[:REPR_OUTPUT_SIZE])
if len(self) > REPR_OUTPUT_SIZE:
data[-1] = "...(remaining elements truncated)..."
return repr(data)
def __len__(self):
if not self._result_count:
self._result_count = self.query.get_count()
# Some backends give weird, false-y values here. Convert to zero.
if not self._result_count:
self._result_count = 0
# This needs to return the actual number of hits, not what's in the cache.
return self._result_count - self._ignored_result_count
def __iter__(self):
if self._cache_is_full():
# We've got a fully populated cache. Let Python do the hard work.
return iter(self._result_cache)
return self._manual_iter()
def __and__(self, other):
if isinstance(other, EmptySearchQuerySet):
return other._clone()
combined = self._clone()
combined.query.combine(other.query, SQ.AND)
return combined
def __or__(self, other):
combined = self._clone()
if isinstance(other, EmptySearchQuerySet):
return combined
combined.query.combine(other.query, SQ.OR)
return combined
def _cache_is_full(self):
if not self.query.has_run():
return False
if len(self) <= 0:
return True
try:
self._result_cache.index(None)
return False
except ValueError:
# No ``None``s found in the results. Check the length of the cache.
return len(self._result_cache) > 0
def _manual_iter(self):
# If we're here, our cache isn't fully populated.
# For efficiency, fill the cache as we go if we run out of results.
# Also, this can't be part of the __iter__ method due to Python's rules
# about generator functions.
current_position = 0
current_cache_max = 0
while True:
if len(self._result_cache) > 0:
try:
current_cache_max = self._result_cache.index(None)
except ValueError:
current_cache_max = len(self._result_cache)
while current_position < current_cache_max:
yield self._result_cache[current_position]
current_position += 1
if self._cache_is_full():
raise StopIteration
# We've run out of results and haven't hit our limit.
# Fill more of the cache.
if not self._fill_cache(current_position, current_position + ITERATOR_LOAD_PER_QUERY):
raise StopIteration
def _fill_cache(self, start, end, **kwargs):
# Tell the query where to start from and how many we'd like.
self.query._reset()
self.query.set_limits(start, end)
results = self.query.get_results(**kwargs)
if results == None or len(results) == 0:
return False
# Setup the full cache now that we know how many results there are.
# We need the ``None``s as placeholders to know what parts of the
# cache we have/haven't filled.
# Using ``None`` like this takes up very little memory. In testing,
# an array of 100,000 ``None``s consumed less than .5 Mb, which ought
# to be an acceptable loss for consistent and more efficient caching.
if len(self._result_cache) == 0:
self._result_cache = [None for i in xrange(self.query.get_count())]
if start is None:
start = 0
if end is None:
end = self.query.get_count()
to_cache = self.post_process_results(results)
# Assign by slice.
self._result_cache[start:start + len(to_cache)] = to_cache
return True
def post_process_results(self, results):
to_cache = []
# Check if we wish to load all objects.
if self._load_all:
original_results = []
models_pks = {}
loaded_objects = {}
# Remember the search position for each result so we don't have to resort later.
for result in results:
original_results.append(result)
models_pks.setdefault(result.model, []).append(result.pk)
# Load the objects for each model in turn.
for model in models_pks:
try:
ui = connections[self.query._using].get_unified_index()
index = ui.get_index(model)
objects = index.read_queryset()
loaded_objects[model] = objects.in_bulk(models_pks[model])
except NotHandled:
self.log.warning("Model '%s.%s' not handled by the routers.", self.app_label, self.model_name)
# Revert to old behaviour
loaded_objects[model] = model._default_manager.in_bulk(models_pks[model])
for result in results:
if self._load_all:
# We have to deal with integer keys being cast from strings
model_objects = loaded_objects.get(result.model, {})
if not result.pk in model_objects:
try:
result.pk = int(result.pk)
except ValueError:
pass
try:
result._object = model_objects[result.pk]
except KeyError:
# The object was either deleted since we indexed or should
# be ignored; fail silently.
self._ignored_result_count += 1
continue
to_cache.append(result)
return to_cache
def __getitem__(self, k):
"""
Retrieves an item or slice from the set of results.
"""
if not isinstance(k, (slice, int, long)):
raise TypeError
assert ((not isinstance(k, slice) and (k >= 0))
or (isinstance(k, slice) and (k.start is None or k.start >= 0)
and (k.stop is None or k.stop >= 0))), \
"Negative indexing is not supported."
# Remember if it's a slice or not. We're going to treat everything as
# a slice to simply the logic and will `.pop()` at the end as needed.
if isinstance(k, slice):
is_slice = True
start = k.start
if k.stop is not None:
bound = int(k.stop)
else:
bound = None
else:
is_slice = False
start = k
bound = k + 1
# We need check to see if we need to populate more of the cache.
if len(self._result_cache) <= 0 or (None in self._result_cache[start:bound] and not self._cache_is_full()):
try:
self._fill_cache(start, bound)
except StopIteration:
# There's nothing left, even though the bound is higher.
pass
# Cache should be full enough for our needs.
if is_slice:
return self._result_cache[start:bound]
else:
return self._result_cache[start]
# Methods that return a SearchQuerySet.
def all(self):
"""Returns all results for the query."""
return self._clone()
def none(self):
"""Returns an empty result list for the query."""
return self._clone(klass=EmptySearchQuerySet)
def filter(self, *args, **kwargs):
"""Narrows the search based on certain attributes and the default operator."""
if DEFAULT_OPERATOR == 'OR':
return self.filter_or(*args, **kwargs)
else:
return self.filter_and(*args, **kwargs)
def exclude(self, *args, **kwargs):
"""Narrows the search by ensuring certain attributes are not included."""
clone = self._clone()
clone.query.add_filter(~SQ(*args, **kwargs))
return clone
def filter_and(self, *args, **kwargs):
"""Narrows the search by looking for (and including) certain attributes."""
clone = self._clone()
clone.query.add_filter(SQ(*args, **kwargs))
return clone
def filter_or(self, *args, **kwargs):
"""Narrows the search by ensuring certain attributes are not included."""
clone = self._clone()
clone.query.add_filter(SQ(*args, **kwargs), use_or=True)
return clone
def order_by(self, *args):
"""Alters the order in which the results should appear."""
clone = self._clone()
for field in args:
clone.query.add_order_by(field)
return clone
def order_by_distance(self, **kwargs):
"""Alters the order in which the results should appear."""
clone = self._clone()
clone.query.add_order_by_distance(**kwargs)
return clone
def highlight(self):
"""Adds highlighting to the results."""
clone = self._clone()
clone.query.add_highlight()
return clone
def models(self, *models):
"""Accepts an arbitrary number of Model classes to include in the search."""
clone = self._clone()
for model in models:
if not model in connections[self.query._using].get_unified_index().get_indexed_models():
warnings.warn('The model %r is not registered for search.' % model)
clone.query.add_model(model)
return clone
def 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.
"""
clone = self._clone()
clone.query.set_result_class(klass)
return clone
def boost(self, term, boost):
"""Boosts a certain aspect of the query."""
clone = self._clone()
clone.query.add_boost(term, boost)
return clone
def facet(self, field):
"""Adds faceting to a query for the provided field."""
clone = self._clone()
clone.query.add_field_facet(field)
return clone
def within(self, field, point_1, point_2):
"""Spatial: Adds a bounding box search to the query."""
clone = self._clone()
clone.query.add_within(field, point_1, point_2)
return clone
def dwithin(self, field, point, distance):
"""Spatial: Adds a distance-based search to the query."""
clone = self._clone()
clone.query.add_dwithin(field, point, distance)
return clone
def distance(self, field, point):
"""
Spatial: Denotes results must have distance measurements from the
provided point.
"""
clone = self._clone()
clone.query.add_distance(field, point)
return clone
def date_facet(self, field, start_date, end_date, gap_by, gap_amount=1):
"""Adds faceting to a query for the provided field by date."""
clone = self._clone()
clone.query.add_date_facet(field, start_date, end_date, gap_by, gap_amount=gap_amount)
return clone
def query_facet(self, field, query):
"""Adds faceting to a query for the provided field with a custom query."""
clone = self._clone()
clone.query.add_query_facet(field, query)
return clone
def narrow(self, query):
"""Pushes existing facet choices into the search."""
clone = self._clone()
clone.query.add_narrow_query(query)
return clone
def raw_search(self, query_string, **kwargs):
"""Passes a raw query directly to the backend."""
return self.filter(content=Raw(query_string, **kwargs))
def load_all(self):
"""Efficiently populates the objects in the search results."""
clone = self._clone()
clone._load_all = True
return clone
def auto_query(self, query_string, fieldname='content'):
"""
Performs a best guess constructing the search query.
This method is somewhat naive but works well enough for the simple,
common cases.
"""
kwargs = {
fieldname: AutoQuery(query_string)
}
return self.filter(**kwargs)
def autocomplete(self, **kwargs):
"""
A shortcut method to perform an autocomplete search.
Must be run against fields that are either ``NgramField`` or
``EdgeNgramField``.
"""
clone = self._clone()
query_bits = []
for field_name, query in kwargs.items():
for word in query.split(' '):
bit = clone.query.clean(word.strip())
kwargs = {
field_name: bit,
}
query_bits.append(SQ(**kwargs))
return clone.filter(reduce(operator.__and__, query_bits))
def using(self, connection_name):
"""
Allows switching which connection the ``SearchQuerySet`` uses to
search in.
"""
clone = self._clone()
clone.query = self.query.using(connection_name)
clone._using = connection_name
return clone
# Methods that do not return a SearchQuerySet.
def count(self):
"""Returns the total number of matching results."""
return len(self)
def best_match(self):
"""Returns the best/top search result that matches the query."""
return self[0]
def latest(self, date_field):
"""Returns the most recent search result that matches the query."""
clone = self._clone()
clone.query.clear_order_by()
clone.query.add_order_by("-%s" % date_field)
return clone.best_match()
def more_like_this(self, model_instance):
"""Finds similar results to the object passed in."""
clone = self._clone()
clone.query.more_like_this(model_instance)
return clone
def 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.
"""
if self.query.has_run():
return self.query.get_facet_counts()
else:
clone = self._clone()
return clone.query.get_facet_counts()
def 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``. Otherwise, ``None`` will be returned.
This will cause the query to execute and should generally be used when
presenting the data.
"""
if self.query.has_run():
return self.query.get_spelling_suggestion(preferred_query)
else:
clone = self._clone()
return clone.query.get_spelling_suggestion(preferred_query)
def values(self, *fields):
"""
Returns a list of dictionaries, each containing the key/value pairs for
the result, exactly like Django's ``ValuesQuerySet``.
"""
qs = self._clone(klass=ValuesSearchQuerySet)
qs._fields.extend(fields)
return qs
def values_list(self, *fields, **kwargs):
"""
Returns a list of field values as tuples, exactly like Django's
``QuerySet.values``.
Optionally accepts 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.
"""
flat = kwargs.pop("flat", False)
if flat and len(fields) > 1:
raise TypeError("'flat' is not valid when values_list is called with more than one field.")
qs = self._clone(klass=ValuesListSearchQuerySet)
qs._fields.extend(fields)
qs._flat = flat
return qs
# Utility methods.
def _clone(self, klass=None):
if klass is None:
klass = self.__class__
query = self.query._clone()
clone = klass(query=query)
clone._load_all = self._load_all
return clone
class EmptySearchQuerySet(SearchQuerySet):
"""
A stubbed SearchQuerySet that behaves as normal but always returns no
results.
"""
def __len__(self):
return 0
def _cache_is_full(self):
# Pretend the cache is always full with no results.
return True
def _clone(self, klass=None):
clone = super(EmptySearchQuerySet, self)._clone(klass=klass)
clone._result_cache = []
return clone
def _fill_cache(self, start, end):
return False
def facet_counts(self):
return {}
class ValuesListSearchQuerySet(SearchQuerySet):
"""
A ``SearchQuerySet`` which returns a list of field values as tuples, exactly
like Django's ``ValuesListQuerySet``.
"""
def __init__(self, *args, **kwargs):
super(ValuesListSearchQuerySet, self).__init__(*args, **kwargs)
self._flat = False
self._fields = []
# Removing this dependency would require refactoring much of the backend
# code (_process_results, etc.) and these aren't large enough to make it
# an immediate priority:
self._internal_fields = ['id', 'django_ct', 'django_id', 'score']
def _clone(self, klass=None):
clone = super(ValuesListSearchQuerySet, self)._clone(klass=klass)
clone._fields = self._fields
clone._flat = self._flat
return clone
def _fill_cache(self, start, end):
query_fields = set(self._internal_fields)
query_fields.update(self._fields)
kwargs = {
'fields': query_fields
}
return super(ValuesListSearchQuerySet, self)._fill_cache(start, end, **kwargs)
def post_process_results(self, results):
to_cache = []
if self._flat:
accum = to_cache.extend
else:
accum = to_cache.append
for result in results:
accum([getattr(result, i, None) for i in self._fields])
return to_cache
class ValuesSearchQuerySet(ValuesListSearchQuerySet):
"""
A ``SearchQuerySet`` which returns a list of dictionaries, each containing
the key/value pairs for the result, exactly like Django's
``ValuesQuerySet``.
"""
def _fill_cache(self, start, end):
query_fields = set(self._internal_fields)
query_fields.update(self._fields)
kwargs = {
'fields': query_fields
}
return super(ValuesListSearchQuerySet, self)._fill_cache(start, end, **kwargs)
def post_process_results(self, results):
to_cache = []
for result in results:
to_cache.append(dict((i, getattr(result, i, None)) for i in self._fields))
return to_cache
class RelatedSearchQuerySet(SearchQuerySet):
"""
A variant of the SearchQuerySet that can handle `load_all_queryset`s.
This is predominantly different in the `_fill_cache` method, as it is
far less efficient but needs to fill the cache before it to maintain
consistency.
"""
_load_all_querysets = {}
_result_cache = []
def _cache_is_full(self):
return len(self._result_cache) >= len(self)
def _manual_iter(self):
# If we're here, our cache isn't fully populated.
# For efficiency, fill the cache as we go if we run out of results.
# Also, this can't be part of the __iter__ method due to Python's rules
# about generator functions.
current_position = 0
current_cache_max = 0
while True:
current_cache_max = len(self._result_cache)
while current_position < current_cache_max:
yield self._result_cache[current_position]
current_position += 1
if self._cache_is_full():
raise StopIteration
# We've run out of results and haven't hit our limit.
# Fill more of the cache.
start = current_position + self._ignored_result_count
if not self._fill_cache(start, start + ITERATOR_LOAD_PER_QUERY):
raise StopIteration
def _fill_cache(self, start, end):
# Tell the query where to start from and how many we'd like.
self.query._reset()
self.query.set_limits(start, end)
results = self.query.get_results()
if len(results) == 0:
return False
if start is None:
start = 0
if end is None:
end = self.query.get_count()
# Check if we wish to load all objects.
if self._load_all:
original_results = []
models_pks = {}
loaded_objects = {}
# Remember the search position for each result so we don't have to resort later.
for result in results:
original_results.append(result)
models_pks.setdefault(result.model, []).append(result.pk)
# Load the objects for each model in turn.
for model in models_pks:
if model in self._load_all_querysets:
# Use the overriding queryset.
loaded_objects[model] = self._load_all_querysets[model].in_bulk(models_pks[model])
else:
# Check the SearchIndex for the model for an override.
try:
index = connections[self.query._using].get_unified_index().get_index(model)
qs = index.load_all_queryset()
loaded_objects[model] = qs.in_bulk(models_pks[model])
except NotHandled:
# The model returned doesn't seem to be handled by the
# routers. We should silently fail and populate
# nothing for those objects.
loaded_objects[model] = []
if len(results) + len(self._result_cache) < len(self) and len(results) < ITERATOR_LOAD_PER_QUERY:
self._ignored_result_count += ITERATOR_LOAD_PER_QUERY - len(results)
for result in results:
if self._load_all:
# We have to deal with integer keys being cast from strings; if this
# fails we've got a character pk.
try:
result.pk = int(result.pk)
except ValueError:
pass
try:
result._object = loaded_objects[result.model][result.pk]
except (KeyError, IndexError):
# The object was either deleted since we indexed or should
# be ignored; fail silently.
self._ignored_result_count += 1
continue
self._result_cache.append(result)
return True
def __getitem__(self, k):
"""
Retrieves an item or slice from the set of results.
"""
if not isinstance(k, (slice, int, long)):
raise TypeError
assert ((not isinstance(k, slice) and (k >= 0))
or (isinstance(k, slice) and (k.start is None or k.start >= 0)
and (k.stop is None or k.stop >= 0))), \
"Negative indexing is not supported."
# Remember if it's a slice or not. We're going to treat everything as
# a slice to simply the logic and will `.pop()` at the end as needed.
if isinstance(k, slice):
is_slice = True
start = k.start
if k.stop is not None:
bound = int(k.stop)
else:
bound = None
else:
is_slice = False
start = k
bound = k + 1
# We need check to see if we need to populate more of the cache.
if len(self._result_cache) <= 0 or not self._cache_is_full():
try:
while len(self._result_cache) < bound and not self._cache_is_full():
current_max = len(self._result_cache) + self._ignored_result_count
self._fill_cache(current_max, current_max + ITERATOR_LOAD_PER_QUERY)
except StopIteration:
# There's nothing left, even though the bound is higher.
pass
# Cache should be full enough for our needs.
if is_slice:
return self._result_cache[start:bound]
else:
return self._result_cache[start]
def load_all_queryset(self, model, 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.
"""
clone = self._clone()
clone._load_all_querysets[model] = queryset
return clone
def _clone(self, klass=None):
if klass is None:
klass = self.__class__
query = self.query._clone()
clone = klass(query=query)
clone._load_all = self._load_all
clone._load_all_querysets = self._load_all_querysets
return clone
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