def mergeResults(results, has_sort_keys, reverse):
"""Sort/merge sub-results, generating a flat sequence.
results is a list of result set sequences, all with or without sort keys
"""
if not has_sort_keys:
return LazyCat(results)
else:
# Concatenate the catalog results into one list and sort it
# Each result record consists of a list of tuples with three values:
# (sortkey, docid, catalog__getitem__)
if len(results) > 1:
all = []
for r in results:
all.extend(r)
elif len(results) == 1:
all = results[0]
else:
return []
all.sort()
if reverse:
all.reverse()
return LazyMap(lambda rec: rec[2](rec[1]), all, len(all))
def sortResults(self,
rs,
sort_index,
reverse=0,
limit=None,
merge=1,
actual_result_count=None,
b_start=0,
b_size=None):
# Sort a result set using a sort index. Return a lazy
# result set in sorted order if merge is true otherwise
# returns a list of (sortkey, uid, getter_function) tuples
#
# The two 'for' loops in here contribute a significant
# proportion of the time to perform an indexed search.
# Try to avoid all non-local attribute lookup inside
# those loops.
_intersection = intersection
_self__getitem__ = self.__getitem__
index_key_map = sort_index.documentToKeyMap()
_None = None
_keyerror = KeyError
result = []
append = result.append
if hasattr(rs, 'keys'):
rs = rs.keys()
if actual_result_count is None:
rlen = len(rs)
actual_result_count = rlen
else:
rlen = actual_result_count
# don't limit to more than what we have
if limit is not None and limit >= rlen:
limit = rlen
# if we want a batch from the end of the resultset, reverse sorting
# order and limit it, then reverse the resultset again
switched_reverse = False
if b_size and b_start and b_start > rlen / 2:
reverse = not reverse
switched_reverse = True
b_end = b_start + b_size
if b_end >= rlen:
overrun = rlen - b_end
if b_start >= rlen:
# bail out, we are outside the possible range
return LazyCat([], 0, actual_result_count)
else:
b_size += overrun
b_start = 0
else:
b_start = b_end - b_start
limit = b_start + b_size
if merge and limit is None and (rlen >
(len(sort_index) * (rlen / 100 + 1))):
# The result set is much larger than the sorted index,
# so iterate over the sorted index for speed.
# This is rarely exercised in practice...
length = 0
try:
intersection(rs, IISet(()))
except TypeError:
# rs is not an object in the IIBTree family.
# Try to turn rs into an IISet.
rs = IISet(rs)
for k, intset in sort_index.items():
# We have an index that has a set of values for
# each sort key, so we intersect with each set and
# get a sorted sequence of the intersections.
intset = _intersection(rs, intset)
if intset:
keys = getattr(intset, 'keys', _None)
if keys is not _None:
# Is this ever true?
intset = keys()
length += len(intset)
append((k, intset, _self__getitem__))
# Note that sort keys are unique.
if reverse:
result.sort(reverse=True)
else:
result.sort()
sequence, slen = self._limit_sequence(result, length, b_start,
b_size, switched_reverse)
result = LazyCat(LazyValues(sequence), slen, actual_result_count)
elif limit is None or (limit * 4 > rlen):
# Iterate over the result set getting sort keys from the index
for did in rs:
try:
key = index_key_map[did]
except _keyerror:
# This document is not in the sort key index, skip it.
pass
else:
append((key, did, _self__getitem__))
# The reference back to __getitem__ is used in case
# we do not merge now and need to intermingle the
# results with those of other catalogs while avoiding
# the cost of instantiating a LazyMap per result
if merge:
if reverse:
result.sort(reverse=True)
else:
result.sort()
if limit is not None:
result = result[:limit]
sequence, _ = self._limit_sequence(result, 0, b_start, b_size,
switched_reverse)
result = LazyValues(sequence)
result.actual_result_count = actual_result_count
else:
sequence, _ = self._limit_sequence(result, 0, b_start, b_size,
switched_reverse)
return sequence
elif reverse:
# Limit/sort results using N-Best algorithm
# This is faster for large sets then a full sort
# And uses far less memory
keys = []
n = 0
worst = None
for did in rs:
try:
key = index_key_map[did]
except _keyerror:
# This document is not in the sort key index, skip it.
pass
else:
if n >= limit and key <= worst:
continue
i = bisect(keys, key)
keys.insert(i, key)
result.insert(i, (key, did, _self__getitem__))
if n == limit:
del keys[0], result[0]
else:
n += 1
worst = keys[0]
result.reverse()
if merge:
sequence, _ = self._limit_sequence(result, 0, b_start, b_size,
switched_reverse)
result = LazyValues(sequence)
result.actual_result_count = actual_result_count
else:
sequence, _ = self._limit_sequence(result, 0, b_start, b_size,
switched_reverse)
return sequence
elif not reverse:
# Limit/sort results using N-Best algorithm in reverse (N-Worst?)
keys = []
n = 0
best = None
for did in rs:
try:
key = index_key_map[did]
except _keyerror:
# This document is not in the sort key index, skip it.
pass
else:
if n >= limit and key >= best:
continue
i = bisect(keys, key)
keys.insert(i, key)
result.insert(i, (key, did, _self__getitem__))
if n == limit:
del keys[-1], result[-1]
else:
n += 1
best = keys[-1]
if merge:
sequence, _ = self._limit_sequence(result, 0, b_start, b_size,
switched_reverse)
result = LazyValues(sequence)
result.actual_result_count = actual_result_count
else:
sequence, _ = self._limit_sequence(result, 0, b_start, b_size,
switched_reverse)
return sequence
return LazyMap(self.__getitem__,
result,
len(result),
actual_result_count=actual_result_count)
def search(self, query, sort_index=None, reverse=0, limit=None, merge=1):
"""Iterate through the indexes, applying the query to each one. If
merge is true then return a lazy result set (sorted if appropriate)
otherwise return the raw (possibly scored) results for later merging.
Limit is used in conjuntion with sorting or scored results to inform
the catalog how many results you are really interested in. The catalog
can then use optimizations to save time and memory. The number of
results is not guaranteed to fall within the limit however, you should
still slice or batch the results as usual."""
rs = None # resultset
# Indexes fulfill a fairly large contract here. We hand each
# index the query mapping we are given (which may be composed
# of some combination of web request, kw mappings or plain old dicts)
# and the index decides what to do with it. If the index finds work
# for itself in the query, it returns the results and a tuple of
# the attributes that were used. If the index finds nothing for it
# to do then it returns None.
# Canonicalize the request into a sensible query before passing it on
query = self.make_query(query)
cr = self.getCatalogPlan(query)
cr.start()
plan = cr.plan()
if not plan:
plan = self._sorted_search_indexes(query)
indexes = self.indexes.keys()
for i in plan:
if i not in indexes:
# We can have bogus keys or the plan can contain index names
# that have been removed in the meantime
continue
index = self.getIndex(i)
_apply_index = getattr(index, "_apply_index", None)
if _apply_index is None:
continue
cr.start_split(i)
limit_result = ILimitedResultIndex.providedBy(index)
if limit_result:
r = _apply_index(query, rs)
else:
r = _apply_index(query)
if r is not None:
r, u = r
# Short circuit if empty result
# BBB: We can remove the "r is not None" check in Zope 2.14
# once we don't need to support the "return everything" case
# anymore
if r is not None and not r:
cr.stop_split(i, result=None, limit=limit_result)
return LazyCat([])
# provide detailed info about the pure intersection time
intersect_id = i + '#intersection'
cr.start_split(intersect_id)
w, rs = weightedIntersection(rs, r)
cr.stop_split(intersect_id)
# consider the time it takes to intersect the index result with
# the total resultset to be part of the index time
cr.stop_split(i, result=r, limit=limit_result)
if not rs:
break
else:
cr.stop_split(i, result=None, limit=limit_result)
# Try to deduce the sort limit from batching arguments
b_start = int(query.get('b_start', 0))
b_size = query.get('b_size', None)
if b_size is not None:
b_size = int(b_size)
if b_size is not None:
limit = b_start + b_size
elif limit and b_size is None:
b_size = limit
if rs is None:
# None of the indexes found anything to do with the query
# We take this to mean that the query was empty (an empty filter)
# and so we return everything in the catalog
warnings.warn('Your query %s produced no query restriction. '
'Currently the entire catalog content is returned. '
'In Zope 2.14 this will result in an empty LazyCat '
'to be returned.' % repr(make_key(self, query)),
DeprecationWarning,
stacklevel=3)
rlen = len(self)
if sort_index is None:
sequence, slen = self._limit_sequence(self.data.items(), rlen,
b_start, b_size)
result = LazyMap(self.instantiate,
sequence,
slen,
actual_result_count=rlen)
else:
cr.start_split('sort_on')
result = self.sortResults(self.data,
sort_index,
reverse,
limit,
merge,
actual_result_count=rlen,
b_start=b_start,
b_size=b_size)
cr.stop_split('sort_on', None)
elif rs:
# We got some results from the indexes.
# Sort and convert to sequences.
# XXX: The check for 'values' is really stupid since we call
# items() and *not* values()
rlen = len(rs)
if sort_index is None and hasattr(rs, 'values'):
# having a 'values' means we have a data structure with
# scores. Build a new result set, sort it by score, reverse
# it, compute the normalized score, and Lazify it.
if not merge:
# Don't bother to sort here, return a list of
# three tuples to be passed later to mergeResults
# note that data_record_normalized_score_ cannot be
# calculated and will always be 1 in this case
getitem = self.__getitem__
result = [(score, (1, score, rid), getitem)
for rid, score in rs.items()]
else:
cr.start_split('sort_on')
rs = rs.byValue(0) # sort it by score
max = float(rs[0][0])
# Here we define our getter function inline so that
# we can conveniently store the max value as a default arg
# and make the normalized score computation lazy
def getScoredResult(item, max=max, self=self):
"""
Returns instances of self._v_brains, or whatever is
passed into self.useBrains.
"""
score, key = item
r=self._v_result_class(self.data[key])\
.__of__(aq_parent(self))
r.data_record_id_ = key
r.data_record_score_ = score
r.data_record_normalized_score_ = int(100. * score /
max)
return r
sequence, slen = self._limit_sequence(
rs, rlen, b_start, b_size)
result = LazyMap(getScoredResult,
sequence,
slen,
actual_result_count=rlen)
cr.stop_split('sort_on', None)
elif sort_index is None and not hasattr(rs, 'values'):
# no scores
if hasattr(rs, 'keys'):
rs = rs.keys()
sequence, slen = self._limit_sequence(rs, rlen, b_start,
b_size)
result = LazyMap(self.__getitem__,
sequence,
slen,
actual_result_count=rlen)
else:
# sort. If there are scores, then this block is not
# reached, therefore 'sort-on' does not happen in the
# context of a text index query. This should probably
# sort by relevance first, then the 'sort-on' attribute.
cr.start_split('sort_on')
result = self.sortResults(rs,
sort_index,
reverse,
limit,
merge,
actual_result_count=rlen,
b_start=b_start,
b_size=b_size)
cr.stop_split('sort_on', None)
else:
# Empty result set
result = LazyCat([])
cr.stop()
return result
def search(self, request, sort_index=None, reverse=0, limit=None, merge=1):
"""Iterate through the indexes, applying the query to each one. If
merge is true then return a lazy result set (sorted if appropriate)
otherwise return the raw (possibly scored) results for later merging.
Limit is used in conjuntion with sorting or scored results to inform
the catalog how many results you are really interested in. The catalog
can then use optimizations to save time and memory. The number of
results is not guaranteed to fall within the limit however, you should
still slice or batch the results as usual."""
rs = None # resultset
# Indexes fulfill a fairly large contract here. We hand each
# index the request mapping we are given (which may be composed
# of some combination of web request, kw mappings or plain old dicts)
# and the index decides what to do with it. If the index finds work
# for itself in the request, it returns the results and a tuple of
# the attributes that were used. If the index finds nothing for it
# to do then it returns None.
# For hysterical reasons, if all indexes return None for a given
# request (and no attributes were used) then we append all results
# in the Catalog. This generally happens when the search values
# in request are all empty strings or do not coorespond to any of
# the indexes.
# Note that if the indexes find query arguments, but the end result
# is an empty sequence, we do nothing
for i in self.indexes.keys():
index = self.getIndex(i)
_apply_index = getattr(index, "_apply_index", None)
if _apply_index is None:
continue
r = _apply_index(request)
if r is not None:
r, u = r
w, rs = weightedIntersection(rs, r)
if rs is None:
# None of the indexes found anything to do with the request
# We take this to mean that the query was empty (an empty filter)
# and so we return everything in the catalog
if sort_index is None:
return LazyMap(self.instantiate, self.data.items(), len(self))
else:
return self.sortResults(self.data, sort_index, reverse, limit,
merge)
elif rs:
# We got some results from the indexes.
# Sort and convert to sequences.
# XXX: The check for 'values' is really stupid since we call
# items() and *not* values()
if sort_index is None and hasattr(rs, 'values'):
# having a 'values' means we have a data structure with
# scores. Build a new result set, sort it by score, reverse
# it, compute the normalized score, and Lazify it.
if not merge:
# Don't bother to sort here, return a list of
# three tuples to be passed later to mergeResults
# note that data_record_normalized_score_ cannot be
# calculated and will always be 1 in this case
getitem = self.__getitem__
return [(score, (1, score, rid), getitem)
for rid, score in rs.items()]
rs = rs.byValue(0) # sort it by score
max = float(rs[0][0])
# Here we define our getter function inline so that
# we can conveniently store the max value as a default arg
# and make the normalized score computation lazy
def getScoredResult(item, max=max, self=self):
"""
Returns instances of self._v_brains, or whatever is passed
into self.useBrains.
"""
score, key = item
r=self._v_result_class(self.data[key])\
.__of__(self.aq_parent)
r.data_record_id_ = key
r.data_record_score_ = score
r.data_record_normalized_score_ = int(100. * score / max)
return r
return LazyMap(getScoredResult, rs, len(rs))
elif sort_index is None and not hasattr(rs, 'values'):
# no scores
if hasattr(rs, 'keys'):
rs = rs.keys()
return LazyMap(self.__getitem__, rs, len(rs))
else:
# sort. If there are scores, then this block is not
# reached, therefore 'sort-on' does not happen in the
# context of a text index query. This should probably
# sort by relevance first, then the 'sort-on' attribute.
return self.sortResults(rs, sort_index, reverse, limit, merge)
else:
# Empty result set
return LazyCat([])