Small fixes for url_index_private_data.cc

components/omnibox/browser/url_index_private_data.cc

 // Copyright (c) 2012 The Chromium Authors. All rights reserved.
 // Use of this source code is governed by a BSD-style license that can be
 // found in the LICENSE file.
 
 #include "components/omnibox/browser/url_index_private_data.h"
 
 #include <stdint.h>
 
 #include <functional>
 #include <iterator>
@@ skipping 235 matching lines @@
   if (pre_filter_item_count_ > post_filter_item_count_) {
     // If we trim the results set we do not want to cache the results for next
     // time as the user's ultimately desired result could easily be eliminated
     // in the early rough filter.
     search_term_cache_.clear();
   } else {
     // Remove any stale SearchTermCacheItems.
     for (SearchTermCacheMap::iterator cache_iter = search_term_cache_.begin();
          cache_iter != search_term_cache_.end(); ) {
       if (!cache_iter->second.used_)
-        search_term_cache_.erase(cache_iter++);
+        cache_iter = search_term_cache_.erase(cache_iter);
       else
         ++cache_iter;
     }
   }
 
   return scored_items;
 }
 
 bool URLIndexPrivateData::UpdateURL(
     history::HistoryService* history_service,
@@ skipping 223 matching lines @@
   word_id_history_map_.clear();
   history_id_word_map_.clear();
   history_info_map_.clear();
   word_starts_map_.clear();
 }
 
 URLIndexPrivateData::~URLIndexPrivateData() {}
 
 HistoryIDSet URLIndexPrivateData::HistoryIDSetFromWords(
     const String16Vector& unsorted_words) {
+  SCOPED_UMA_HISTOGRAM_TIMER("Omnibox.HistoryIDSetFromWords");

[Peter Kasting, 2016/11/30 21:39:50]

Double-checking: the existing omnibox provider time histograms are
insufficiently granular to measure HQP performance wins in the field?  (I want
to make sure you know about these histograms before adding this, since my own
knowledge of them is a little too sketchy to know whether this is good or not.)

   // Break the terms down into individual terms (words), get the candidate
   // set for each term, and intersect each to get a final candidate list.
   // Note that a single 'term' from the user's perspective might be
   // a string like "http://www.somewebsite.com" which, from our perspective,
   // is four words: 'http', 'www', 'somewebsite', and 'com'.
   HistoryIDSet history_id_set;
   String16Vector words(unsorted_words);
   // Sort the words into the longest first as such are likely to narrow down
   // the results quicker. Also, single character words are the most expensive
   // to process so save them for last.
@@ skipping 88 matching lines @@
             word_id_set, leftover_set);
         word_id_set.swap(new_word_id_set);
       }
     }
 
     // We must filter the word list because the resulting word set surely
     // contains words which do not have the search term as a proper subset.
     for (WordIDSet::iterator word_set_iter = word_id_set.begin();
          word_set_iter != word_id_set.end(); ) {
       if (word_list_[*word_set_iter].find(term) == base::string16::npos)
-        word_id_set.erase(word_set_iter++);
+        word_set_iter = word_id_set.erase(word_set_iter);
       else
         ++word_set_iter;
     }
   } else {
     word_id_set = WordIDSetForTermChars(Char16SetFromString16(term));
   }
 
   // If any words resulted then we can compose a set of history IDs by unioning
   // the sets from each word.
   HistoryIDSet history_id_set;
@@ skipping 763 matching lines @@
   // First cut: typed count, visit count, recency.
   // TODO(mrossetti): This is too simplistic. Consider an approach which ranks
   // recently visited (within the last 12/24 hours) as highly important. Get
   // input from mpearson.
   if (r1.typed_count() != r2.typed_count())
     return (r1.typed_count() > r2.typed_count());
   if (r1.visit_count() != r2.visit_count())
     return (r1.visit_count() > r2.visit_count());
   return (r1.last_visit() > r2.last_visit());
 }