############################################################################## # # Copyright (c) 2002 Zope Foundation and Contributors. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE # ############################################################################## import types import logging import warnings from bisect import bisect from random import randint import Acquisition from Acquisition import aq_base from Acquisition import aq_parent import ExtensionClass from Missing import MV from Persistence import Persistent from Products.PluginIndexes.interfaces import ILimitedResultIndex import BTrees.Length from BTrees.IIBTree import intersection, IISet from BTrees.IIBTree import weightedIntersection from BTrees.OIBTree import OIBTree from BTrees.IOBTree import IOBTree from Lazy import LazyMap, LazyCat, LazyValues from CatalogBrains import AbstractCatalogBrain, NoBrainer from .plan import CatalogPlan LOG = logging.getLogger('Zope.ZCatalog') def safe_callable(ob): # Works with ExtensionClasses and Acquisition. if hasattr(ob, '__class__'): return hasattr(ob, '__call__') or isinstance(ob, types.ClassType) else: return callable(ob) class CatalogError(Exception): pass class Catalog(Persistent, Acquisition.Implicit, ExtensionClass.Base): """ An Object Catalog An Object Catalog maintains a table of object metadata, and a series of manageable indexes to quickly search for objects (references in the metadata) that satisfy a search query. This class is not Zope specific, and can be used in any python program to build catalogs of objects. Note that it does require the objects to be Persistent, and thus must be used with ZODB3. """ _v_brains = NoBrainer def __init__(self, vocabulary=None, brains=None): # Catalogs no longer care about vocabularies and lexicons # so the vocabulary argument is ignored. (Casey) self.schema = {} # mapping from attribute name to column number self.names = () # sequence of column names self.indexes = {} # maping from index name to index object # The catalog maintains a BTree of object meta_data for # convenient display on result pages. meta_data attributes # are turned into brain objects and returned by # searchResults. The indexing machinery indexes all records # by an integer id (rid). self.data is a mapping from the # integer id to the meta_data, self.uids is a mapping of the # object unique identifier to the rid, and self.paths is a # mapping of the rid to the unique identifier. self.clear() if brains is not None: self._v_brains = brains self.updateBrains() def __len__(self): return self._length() def clear(self): """ clear catalog """ self.data = IOBTree() # mapping of rid to meta_data self.uids = OIBTree() # mapping of uid to rid self.paths = IOBTree() # mapping of rid to uid self._length = BTrees.Length.Length() for index in self.indexes.keys(): self.getIndex(index).clear() def updateBrains(self): self.useBrains(self._v_brains) def __getitem__(self, index, ttype=type(())): """ Returns instances of self._v_brains, or whatever is passed into self.useBrains. """ if type(index) is ttype: # then it contains a score... normalized_score, score, key = index 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_ = normalized_score else: # otherwise no score, set all scores to 1 r=self._v_result_class(self.data[index]).__of__(aq_parent(self)) r.data_record_id_ = index r.data_record_score_ = 1 r.data_record_normalized_score_ = 1 return r def __setstate__(self, state): """ initialize your brains. This method is called when the catalog is first activated (from the persistent storage) """ Persistent.__setstate__(self, state) self.updateBrains() def useBrains(self, brains): """ Sets up the Catalog to return an object (ala ZTables) that is created on the fly from the tuple stored in the self.data Btree. """ class mybrains(AbstractCatalogBrain, brains): pass scopy = self.schema.copy() scopy['data_record_id_']=len(self.schema.keys()) scopy['data_record_score_']=len(self.schema.keys())+1 scopy['data_record_normalized_score_']=len(self.schema.keys())+2 mybrains.__record_schema__ = scopy self._v_brains = brains self._v_result_class = mybrains def addColumn(self, name, default_value=None): """ adds a row to the meta data schema """ schema = self.schema names = list(self.names) if name != name.strip(): # Someone could have mistakenly added a space at the end # of the input field. LOG.warn("stripped space from new column %r -> %r", name, name.strip()) name = name.strip() if name in schema: raise CatalogError('The column %s already exists' % name) if name[0] == '_': raise CatalogError('Cannot cache fields beginning with "_"') values = schema.values() if values: schema[name] = max(values) + 1 else: schema[name] = 0 names.append(name) if default_value in (None, ''): default_value = MV for key, value in self.data.items(): rec = list(value) rec.append(default_value) self.data[key] = tuple(rec) self.names = tuple(names) self.schema = schema # new column? update the brain self.updateBrains() self._p_changed = 1 # why? def delColumn(self, name): """ deletes a row from the meta data schema """ names = list(self.names) _index = names.index(name) if not name in self.schema: LOG.error('delColumn attempted to delete nonexistent ' 'column %s.' % str(name)) return del names[_index] # rebuild the schema i = 0 schema = {} for name in names: schema[name] = i i = i + 1 self.schema = schema self.names = tuple(names) # update the brain self.updateBrains() # remove the column value from each record for key, value in self.data.items(): rec = list(value) del rec[_index] self.data[key] = tuple(rec) def addIndex(self, name, index_type): """Create a new index, given a name and a index_type. Old format: index_type was a string, 'FieldIndex' 'TextIndex' or 'KeywordIndex' is no longer valid; the actual index must be instantiated and passed in to addIndex. New format: index_type is the actual index object to be stored. """ if name in self.indexes: raise CatalogError('The index %s already exists' % name) if name.startswith('_'): raise CatalogError('Cannot index fields beginning with "_"') if not name: raise CatalogError('Name of index is empty') if name != name.strip(): # Someone could have mistakenly added a space at the end # of the input field. LOG.warn("stripped space from new index %r -> %r", name, name.strip()) name = name.strip() indexes = self.indexes if isinstance(index_type, str): raise TypeError("Catalog addIndex now requires the index type to" "be resolved prior to adding; create the proper " "index in the caller.") indexes[name] = index_type self.indexes = indexes def delIndex(self, name): """ deletes an index """ if not name in self.indexes: raise CatalogError('The index %s does not exist' % name) indexes = self.indexes del indexes[name] self.indexes = indexes def getIndex(self, name): """ get an index wrapped in the catalog """ return self.indexes[name].__of__(self) def updateMetadata(self, object, uid, index): """ Given an object and a uid, update the column data for the uid with the object data iff the object has changed """ data = self.data newDataRecord = self.recordify(object) if index is None: index = getattr(self, '_v_nextid', 0) if index % 4000 == 0: index = randint(-2000000000, 2000000000) while not data.insert(index, newDataRecord): index = randint(-2000000000, 2000000000) # We want ids to be somewhat random, but there are # advantages for having some ids generated # sequentially when many catalog updates are done at # once, such as when reindexing or bulk indexing. # We allocate ids sequentially using a volatile base, # so different threads get different bases. This # further reduces conflict and reduces churn in # here and it result sets when bulk indexing. self._v_nextid = index + 1 else: if data.get(index, 0) != newDataRecord: data[index] = newDataRecord return index # the cataloging API def catalogObject(self, object, uid, threshold=None, idxs=None, update_metadata=1): """ Adds an object to the Catalog by iteratively applying it to all indexes. 'object' is the object to be cataloged 'uid' is the unique Catalog identifier for this object If 'idxs' is specified (as a sequence), apply the object only to the named indexes. If 'update_metadata' is true (the default), also update metadata for the object. If the object is new to the catalog, this flag has no effect (metadata is always created for new objects). """ if idxs is None: idxs = [] index = self.uids.get(uid, None) if index is None: # we are inserting new data index = self.updateMetadata(object, uid, None) self._length.change(1) self.uids[uid] = index self.paths[index] = uid elif update_metadata: # we are updating and we need to update metadata self.updateMetadata(object, uid, index) # do indexing total = 0 if idxs == []: use_indexes = self.indexes.keys() else: use_indexes = idxs for name in use_indexes: x = self.getIndex(name) if hasattr(x, 'index_object'): blah = x.index_object(index, object, threshold) total = total + blah else: LOG.error('catalogObject was passed bad index ' 'object %s.' % str(x)) return total def uncatalogObject(self, uid): """ Uncatalog and object from the Catalog. and 'uid' is a unique Catalog identifier Note, the uid must be the same as when the object was catalogued, otherwise it will not get removed from the catalog This method should not raise an exception if the uid cannot be found in the catalog. """ data = self.data uids = self.uids paths = self.paths indexes = self.indexes.keys() rid = uids.get(uid, None) if rid is not None: for name in indexes: x = self.getIndex(name) if hasattr(x, 'unindex_object'): x.unindex_object(rid) del data[rid] del paths[rid] del uids[uid] self._length.change(-1) else: LOG.error('uncatalogObject unsuccessfully ' 'attempted to uncatalog an object ' 'with a uid of %s. ' % str(uid)) def uniqueValuesFor(self, name): """ return unique values for FieldIndex name """ return self.getIndex(name).uniqueValues() def hasuid(self, uid): """ return the rid if catalog contains an object with uid """ return self.uids.get(uid) def recordify(self, object): """ turns an object into a record tuple """ record = [] # the unique id is always the first element for x in self.names: attr = getattr(object, x, MV) if (attr is not MV and safe_callable(attr)): attr = attr() record.append(attr) return tuple(record) def instantiate(self, record): r = self._v_result_class(record[1]) r.data_record_id_ = record[0] return r.__of__(self) def getMetadataForRID(self, rid): record = self.data[rid] result = {} for (key, pos) in self.schema.items(): result[key] = record[pos] return result def getIndexDataForRID(self, rid): result = {} for name in self.indexes.keys(): result[name] = self.getIndex(name).getEntryForObject(rid, "") return result # This is the Catalog search engine. Most of the heavy lifting happens # below def make_query(self, request): # This is a bit of a mess, but the ZCatalog API has traditionally # supported passing in query restrictions in almost arbitary ways real_req = None if isinstance(request, dict): query = request.copy() elif isinstance(request, CatalogSearchArgumentsMap): query = {} query.update(request.keywords) real_req = request.request if isinstance(real_req, dict): query.update(real_req) real_req = None else: real_req = request if real_req: warnings.warn('You have specified a query using either a request ' 'object or a mixture of a query dict and keyword ' 'arguments. Please use only a simple query dict. ' 'Your query contained "%s". This support is ' 'deprecated and will be removed in Zope 2.14.' % repr(real_req), DeprecationWarning, stacklevel=4) known_keys = query.keys() # The request has too many places where an index restriction # might be specified. Putting all of request.form, # request.other, ... into the query isn't what we want. # So we iterate over all known indexes instead and see if they # are in the request. for iid in self.indexes.keys(): if iid in known_keys: continue value = real_req.get(iid) if value: query[iid] = value return query def _get_index_query_names(self, index): if hasattr(index, 'getIndexQueryNames'): return index.getIndexQueryNames() return (index.getId(),) def _sorted_search_indexes(self, query): # Simple implementation doing no ordering. query_keys = query.keys() order = [] for name, index in self.indexes.items(): for attr in self._get_index_query_names(index): if attr in query_keys: order.append((ILimitedResultIndex.providedBy(index), name)) break order.sort() return [i[1] for i in order] def _limit_sequence(self, sequence, slen, b_start=0, b_size=None, switched_reverse=False): if b_size is not None: sequence = sequence[b_start:b_start + b_size] if slen: slen = len(sequence) if switched_reverse: sequence.reverse() return (sequence, slen) 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) # weightedIntersection preserves the values from any mappings # we get, as some indexes don't return simple sets if hasattr(rs, 'items') or hasattr(r, 'items'): _, rs = weightedIntersection(rs, r) else: rs = intersection(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(cr.make_key(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, 'items'): # having a 'items' 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 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() 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 = rlen - b_end 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. actual_result_count -= 1 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. actual_result_count -= 1 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. actual_result_count -= 1 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 _get_sort_attr(self, attr, kw): """Helper function to find sort-on or sort-order.""" # There are three different ways to find the attribute: # 1. kw[sort-attr] # 2. self.sort-attr # 3. kw[sort_attr] # kw may be a dict or an ExtensionClass MultiMapping, which # differ in what get() returns with no default value. name = "sort-%s" % attr val = kw.get(name, None) if val is not None: return val val = getattr(self, name, None) if val is not None: return val return kw.get("sort_%s" % attr, None) def _getSortIndex(self, args): """Returns a search index object or None.""" sort_index_name = self._get_sort_attr("on", args) if sort_index_name is not None: # self.indexes is always a dict, so get() w/ 1 arg works sort_index = self.indexes.get(sort_index_name) if sort_index is None: raise CatalogError('Unknown sort_on index (%s)' % sort_index_name) else: if not hasattr(sort_index, 'documentToKeyMap'): raise CatalogError( 'The index chosen for sort_on (%s) is not capable of ' 'being used as a sort index.' % sort_index_name) return sort_index else: return None def searchResults(self, REQUEST=None, used=None, _merge=1, **kw): # You should pass in a simple dictionary as the request argument, # which only contains the relevant query. # The used argument is deprecated and is ignored if REQUEST is None and not kw: # Try to acquire request if we get no args for bw compat warnings.warn('Calling searchResults without a query argument nor ' 'keyword arguments is deprecated. In Zope 2.14 the ' 'query will no longer be automatically taken from ' 'the acquired request.', DeprecationWarning, stacklevel=3) REQUEST = getattr(self, 'REQUEST', None) if isinstance(REQUEST, dict) and not kw: # short cut for the best practice args = REQUEST else: args = CatalogSearchArgumentsMap(REQUEST, kw) sort_index = self._getSortIndex(args) sort_limit = self._get_sort_attr('limit', args) reverse = 0 if sort_index is not None: order = self._get_sort_attr("order", args) if (isinstance(order, str) and order.lower() in ('reverse', 'descending')): reverse = 1 # Perform searches with indexes and sort_index return self.search(args, sort_index, reverse, sort_limit, _merge) __call__ = searchResults def getCatalogPlan(self, query=None): """Query time reporting and planning. """ parent = aq_base(aq_parent(self)) threshold = getattr(parent, 'long_query_time', 0.1) return CatalogPlan(self, query, threshold) class CatalogSearchArgumentsMap(object): """Multimap catalog arguments coming simultaneously from keywords and request. BBB: Values that are empty strings are treated as non-existent. This is to ignore empty values, thereby ignoring empty form fields to be consistent with hysterical behavior. This is deprecated and can be changed in Zope 2.14. """ def __init__(self, request, keywords): self.request = request or {} self.keywords = keywords or {} def __getitem__(self, key): marker = [] v = self.keywords.get(key, marker) if v is marker or v == '': v = self.request[key] if v == '': raise KeyError(key) return v def get(self, key, default=None): try: v = self[key] except KeyError: return default else: return v def has_key(self, key): try: self[key] except KeyError: return False else: return True def __contains__(self, name): return self.has_key(name) 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__) combined = [] if len(results) > 1: for r in results: combined.extend(r) elif len(results) == 1: combined = results[0] else: return [] if reverse: combined.sort(reverse=True) else: combined.sort() return LazyMap(lambda rec: rec[2](rec[1]), combined, len(combined))