Lazified computing the normalized score on text index search results. This can...

Lazified computing the normalized score on text index search results. This can shave seconds off search times when you get a lot of results

lib/python/Products/ZCatalog/Catalog.py

@@ -487,15 +487,25 @@ class Catalog(Persistent, Acquisition.Implicit, ExtensionClass.Base):
                 # it, compute the normalized score, and Lazify it.
                 rset = rs.byValue(0) # sort it by score
                 max = float(rset[0][0])
-                rs = []
                 
-                # XXX Ugh, this is really not as lazy as it could be
-                # XXX This should be changed to a lazily computed 
-                # XXX attribute since it is not always needed
-                for score, key in rset:
-                    # compute normalized scores
-                    rs.append((int(100. * score / max), score, key))
-                append(LazyMap(self.__getitem__, rs))
+                # 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
+                
+                # Lazify the results
+                append(LazyMap(getScoredResult, rset))
                     
             elif sort_index is None and not hasattr(rs, 'values'):
                 # no scores?  Just Lazify.