Issue #1258 - Part 1: Import mailnews, ldap, and mork from comm-esr52.9.1

1 files changed, 346 insertions, 0 deletions

diff --git a/mailnews/db/gloda/modules/msg_search.js b/mailnews/db/gloda/modules/msg_search.js
new file mode 100644
index 000000000..8ba854406
--- /dev/null
+++ b/mailnews/db/gloda/modules/msg_search.js
@@ -0,0 +1,346 @@
+/* This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
+
+this.EXPORTED_SYMBOLS = ["GlodaMsgSearcher"];
+
+var Cc = Components.classes;
+var Ci = Components.interfaces;
+var Cr = Components.results;
+var Cu = Components.utils;
+
+Cu.import("resource://gre/modules/Services.jsm");
+Cu.import("resource:///modules/gloda/public.js");
+
+/**
+ * How much time boost should a 'score point' amount to?  The authoritative,
+ *  incontrivertible answer, across all time and space, is a week.
+ *  Note that gloda stores timestamps as PRTimes for no exceedingly good
+ *  reason.
+ */
+var FUZZSCORE_TIMESTAMP_FACTOR = 1000 * 1000 * 60 * 60 * 24 * 7;
+
+var RANK_USAGE =
+  "glodaRank(matchinfo(messagesText), 1.0, 2.0, 2.0, 1.5, 1.5)";
+
+var DASCORE =
+  "(((" + RANK_USAGE + " + messages.notability) * " +
+    FUZZSCORE_TIMESTAMP_FACTOR +
+   ") + messages.date)";
+
+/**
+ * A new optimization decision we are making is that we do not want to carry
+ *  around any data in our ephemeral tables that is not used for whittling the
+ *  result set.  The idea is that the btree page cache or OS cache is going to
+ *  save us from the disk seeks and carrying around the extra data is just going
+ *  to be CPU/memory churn that slows us down.
+ *
+ * Additionally, we try and avoid row lookups that would have their results
+ *  discarded by the LIMIT.  Because of limitations in FTS3 (which might
+ *  be addressed in FTS4 by a feature request), we can't avoid the 'messages'
+ *  lookup since that has the message's date and static notability but we can
+ *  defer the 'messagesText' lookup.
+ *
+ * This is the access pattern we are after here:
+ * 1) Order the matches with minimized lookup and result storage costs.
+ * - The innermost MATCH does the doclist magic and provides us with
+ *    matchinfo() support which does not require content row retrieval
+ *    from messagesText.  Unfortunately, this is not enough to whittle anything
+ *    because we still need static interestingness, so...
+ * - Based on the match we retrieve the date and notability for that row from
+ *    'messages' using this in conjunction with matchinfo() to provide a score
+ *    that we can then use to LIMIT our results.
+ * 2) We reissue the MATCH query so that we will be able to use offsets(), but
+ *    we intersect the results of this MATCH against our LIMITed results from
+ *    step 1.
+ * - We use 'docid IN (phase 1 query)' to accomplish this because it results in
+ *    efficient lookup.  If we just use a join, we get O(mn) performance because
+ *    a cartesian join ends up being performed where either we end up performing
+ *    the fulltext query M times and table scan intersect with the results from
+ *    phase 1 or we do the fulltext once but traverse the entire result set from
+ *    phase 1 N times.
+ * - We believe that the re-execution of the MATCH query should have no disk
+ *    costs because it should still be cached by SQLite or the OS.  In the case
+ *    where memory is so constrained this is not true our behavior is still
+ *    probably preferable than the old way because that would have caused lots
+ *    of swapping.
+ * - This part of the query otherwise resembles the basic gloda query but with
+ *    the inclusion of the offsets() invocation.  The messages table lookup
+ *    should not involve any disk traffic because the pages should still be
+ *    cached (SQLite or OS) from phase 1.  The messagesText lookup is new, and
+ *    this is the major disk-seek reduction optimization we are making.  (Since
+ *    we avoid this lookup for all of the documents that were excluded by the
+ *    LIMIT.)  Since offsets() also needs to retrieve the row from messagesText
+ *    there is a nice synergy there.
+ */
+var NUEVO_FULLTEXT_SQL =
+  "SELECT messages.*, messagesText.*, offsets(messagesText) AS osets " +
+  "FROM messagesText, messages " +
+  "WHERE" +
+    " messagesText MATCH ?1 " +
+    " AND messagesText.docid IN (" +
+       "SELECT docid " +
+       "FROM messagesText JOIN messages ON messagesText.docid = messages.id " +
+       "WHERE messagesText MATCH ?1 " +
+       "ORDER BY " + DASCORE + " DESC " +
+       "LIMIT ?2" +
+    " )" +
+    " AND messages.id = messagesText.docid " +
+    " AND +messages.deleted = 0" +
+    " AND +messages.folderID IS NOT NULL" +
+    " AND +messages.messageKey IS NOT NULL";
+
+function identityFunc(x) {
+  return x;
+}
+
+function oneLessMaxZero(x) {
+  if (x <= 1)
+    return 0;
+  else
+    return x - 1;
+}
+
+function reduceSum(accum, curValue) {
+  return accum + curValue;
+}
+
+/*
+ * Columns are: body, subject, attachment names, author, recipients
+ */
+
+/**
+ * Scores if all search terms match in a column.  We bias against author
+ *  slightly and recipient a bit more in this case because a search that
+ *  entirely matches just on a person should give a mention of that person
+ *  in the subject or attachment a fighting chance.
+ * Keep in mind that because of our indexing in the face of address book
+ *  contacts (namely, we index the name used in the e-mail as well as the
+ *  display name on the address book card associated with the e-mail adress)
+ *  a contact is going to bias towards matching multiple times.
+ */
+var COLUMN_ALL_MATCH_SCORES = [4, 20, 20, 16, 12];
+/**
+ * Score for each distinct term that matches in the column.  This is capped
+ *  by COLUMN_ALL_SCORES.
+ */
+var COLUMN_PARTIAL_PER_MATCH_SCORES = [1, 4, 4, 4, 3];
+/**
+ * If a term matches multiple times, what is the marginal score for each
+ *  additional match.  We count the total number of matches beyond the
+ *  first match for each term.  In other words, if we have 3 terms which
+ *  matched 5, 3, and 0 times, then the total from our perspective is
+ *  (5 - 1) + (3 - 1) + 0 = 4 + 2 + 0 = 6.  We take the minimum of that value
+ *  and the value in COLUMN_MULTIPLE_MATCH_LIMIT and multiply by the value in
+ *  COLUMN_MULTIPLE_MATCH_SCORES.
+ */
+var COLUMN_MULTIPLE_MATCH_SCORES = [1, 0, 0, 0, 0];
+var COLUMN_MULTIPLE_MATCH_LIMIT = [10, 0, 0, 0, 0];
+
+/**
+ * Score the message on its offsets (from stashedColumns).
+ */
+function scoreOffsets(aMessage, aContext) {
+  let score = 0;
+
+  let termTemplate = aContext.terms.map(_ => 0);
+  // for each column, a list of the incidence of each term
+  let columnTermIncidence = [termTemplate.concat(),
+                             termTemplate.concat(),
+                             termTemplate.concat(),
+                             termTemplate.concat(),
+                             termTemplate.concat()];
+
+  // we need a friendlyParseInt because otherwise the radix stuff happens
+  //  because of the extra arguments map parses.  curse you, map!
+  let offsetNums =
+    aContext.stashedColumns[aMessage.id][0].split(" ").map(x => parseInt(x));
+  for (let i=0; i < offsetNums.length; i += 4) {
+    let columnIndex = offsetNums[i];
+    let termIndex = offsetNums[i+1];
+    columnTermIncidence[columnIndex][termIndex]++;
+  }
+
+  for (let iColumn = 0; iColumn < COLUMN_ALL_MATCH_SCORES.length; iColumn++) {
+    let termIncidence = columnTermIncidence[iColumn];
+    // bestow all match credit
+    if (termIncidence.every(identityFunc))
+      score += COLUMN_ALL_MATCH_SCORES[iColumn];
+    // bestow partial match credit
+    else if (termIncidence.some(identityFunc))
+      score += Math.min(COLUMN_ALL_MATCH_SCORES[iColumn],
+                        COLUMN_PARTIAL_PER_MATCH_SCORES[iColumn] *
+                          termIncidence.filter(identityFunc).length);
+    // bestow multiple match credit
+    score += Math.min(termIncidence.map(oneLessMaxZero).reduce(reduceSum, 0),
+                      COLUMN_MULTIPLE_MATCH_LIMIT[iColumn]) *
+             COLUMN_MULTIPLE_MATCH_SCORES[iColumn];
+  }
+
+  return score;
+}
+
+/**
+ * The searcher basically looks like a query, but is specialized for fulltext
+ *  search against messages.  Most of the explicit specialization involves
+ *  crafting a SQL query that attempts to order the matches by likelihood that
+ *  the user was looking for it.  This is based on full-text matches combined
+ *  with an explicit (generic) interest score value placed on the message at
+ *  indexing time.  This is followed by using the more generic gloda scoring
+ *  mechanism to explicitly score the messages given the search context in
+ *  addition to the more generic score adjusting rules.
+ */
+function GlodaMsgSearcher(aListener, aSearchString, aAndTerms) {
+  this.listener = aListener;
+
+  this.searchString = aSearchString;
+  this.fulltextTerms = this.parseSearchString(aSearchString);
+  this.andTerms = (aAndTerms != null) ? aAndTerms : true;
+
+  this.query = null;
+  this.collection = null;
+
+  this.scores = null;
+}
+GlodaMsgSearcher.prototype = {
+  /**
+   * Number of messages to retrieve initially.
+   */
+  get retrievalLimit() {
+    return Services.prefs.getIntPref(
+      "mailnews.database.global.search.msg.limit"
+    );
+  },
+
+  /**
+   * Parse the string into terms/phrases by finding matching double-quotes.
+   */
+  parseSearchString: function GlodaMsgSearcher_parseSearchString(aSearchString) {
+    aSearchString = aSearchString.trim();
+    let terms = [];
+
+    /*
+     * Add the term as long as the trim on the way in didn't obliterate it.
+     *
+     * In the future this might have other helper logic; it did once before.
+     */
+    function addTerm(aTerm) {
+      if (aTerm)
+        terms.push(aTerm);
+    }
+
+    while (aSearchString) {
+      if (aSearchString.startsWith('"')) {
+        let endIndex = aSearchString.indexOf(aSearchString[0], 1);
+        // eat the quote if it has no friend
+        if (endIndex == -1) {
+          aSearchString = aSearchString.substring(1);
+          continue;
+        }
+
+        addTerm(aSearchString.substring(1, endIndex).trim());
+        aSearchString = aSearchString.substring(endIndex + 1);
+        continue;
+      }
+
+      let spaceIndex = aSearchString.indexOf(" ");
+      if (spaceIndex == -1) {
+        addTerm(aSearchString);
+        break;
+      }
+
+      addTerm(aSearchString.substring(0, spaceIndex));
+      aSearchString = aSearchString.substring(spaceIndex+1);
+    }
+
+    return terms;
+  },
+
+  buildFulltextQuery: function GlodaMsgSearcher_buildFulltextQuery() {
+    let query = Gloda.newQuery(Gloda.NOUN_MESSAGE, {
+      noMagic: true,
+      explicitSQL: NUEVO_FULLTEXT_SQL,
+      limitClauseAlreadyIncluded: true,
+      // osets is 0-based column number 14 (volatile to column changes)
+      // save the offset column for extra analysis
+      stashColumns: [14]
+    });
+
+    let fulltextQueryString = "";
+
+    for (let [iTerm, term] of this.fulltextTerms.entries()) {
+      if (iTerm)
+        fulltextQueryString += this.andTerms ? " " : " OR ";
+
+      // Put our term in quotes.  This is needed for the tokenizer to be able
+      //  to do useful things.  The exception is people clever enough to use
+      //  NEAR.
+      if (/^NEAR(\/\d+)?$/.test(term))
+        fulltextQueryString += term;
+      // Check if this is a single-character CJK search query.  If so, we want
+      //  to add a wildcard.
+      // Our tokenizer treats anything at/above 0x2000 as CJK for now.
+      else if (term.length == 1 && term.charCodeAt(0) >= 0x2000)
+        fulltextQueryString += term + "*";
+      else if (
+          term.length == 2 &&
+            term.charCodeAt(0) >= 0x2000 &&
+            term.charCodeAt(1) >= 0x2000
+          || term.length >= 3
+      )
+        fulltextQueryString += '"' + term + '"';
+
+    }
+
+    query.fulltextMatches(fulltextQueryString);
+    query.limit(this.retrievalLimit);
+
+    return query;
+  },
+
+  getCollection: function GlodaMsgSearcher_getCollection(
+      aListenerOverride, aData) {
+    if (aListenerOverride)
+      this.listener = aListenerOverride;
+
+    this.query = this.buildFulltextQuery();
+    this.collection = this.query.getCollection(this, aData);
+    this.completed = false;
+
+    return this.collection;
+  },
+
+  sortBy: '-dascore',
+
+  onItemsAdded: function GlodaMsgSearcher_onItemsAdded(aItems, aCollection) {
+    let newScores = Gloda.scoreNounItems(
+      aItems,
+      {
+        terms: this.fulltextTerms,
+        stashedColumns: aCollection.stashedColumns
+      },
+      [scoreOffsets]);
+    if (this.scores)
+      this.scores = this.scores.concat(newScores);
+    else
+      this.scores = newScores;
+
+    if (this.listener)
+      this.listener.onItemsAdded(aItems, aCollection);
+  },
+  onItemsModified: function GlodaMsgSearcher_onItemsModified(aItems,
+                                                             aCollection) {
+    if (this.listener)
+      this.listener.onItemsModified(aItems, aCollection);
+  },
+  onItemsRemoved: function GlodaMsgSearcher_onItemsRemoved(aItems,
+                                                           aCollection) {
+    if (this.listener)
+      this.listener.onItemsRemoved(aItems, aCollection);
+  },
+  onQueryCompleted: function GlodaMsgSearcher_onQueryCompleted(aCollection) {
+    this.completed = true;
+    if (this.listener)
+      this.listener.onQueryCompleted(aCollection);
+  },
+};