class DelegatedCatalog(Catalog) :
'''C'est ici qu'on délègue effectivement à Solr '''
+
def search(self, query, sort_index=None, reverse=0, limit=None, merge=1):
- return Catalog.search(self, query,
- sort_index=sort_index,
- reverse=reverse, limit=limit, merge=merge)
\ No newline at end of file
+ """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
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