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In Search Of... integrating site search
- 1. In Search Of... integrating site search Ian Barber @ianbarber http://phpir.com ian@ibuildings.com http://joind.in/talk/view/1462
- 2. what do you want?
- 3. How Search Works Integrating Search Improving Results Using Search Search Performance Questions 3
- 4. 4
- 5. Query Query Query Query Query Parser Result Result Result Result Index Analyser Document Document Document Document 4
- 6. Tokenisation “ With AT&T’s help, the F.B.I Miami-Dade office had recovered $1.1 million from O’Healy’s Ponzi scheme, 10-15% more than ” expected. 6
- 7. PHP Tokenisation function tokenise($string) { $string = strtolower($string); preg_match_all('/w+/', $string, $matches, PREG_OFFSET_CAPTURE); return $matches[0]; } 7
- 8. Document Term Pairs Document ID Term 1 the 1 best 1 of 1 the ... ... 204 and 204 what 204 would 8
- 9. Inverted Index Term Documents best 1 (4, 16), 4 (422), 129 (344) ... what 24 (50, 98), 75 (33, 208) ... would 99 (32, 599), 201 (344) .. ... ... 9
- 10. Boolean Query Merge Query: Best Western Hotel best 1 4 129 298 305 338 western 4 95 194 204 298 305 working 4 298 305 hotel 2 40 200 298 355 402 Result: Document 298 10
- 11. Lorem ipsum dolor sit amet, Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed sit amet ante vitae enim elementum semper sodales quis consectetur adipiscing elit. Sed sit amet ante vitae enim elementum semper sodalesipsum. Aliquam vel condimentum Lorem ipsum dolor sit amet, quis neque. ipsum. Aliquam vel condimentum neque. Curabitur ornare feugiat ornare. Donec consectetur adipiscing elit. Sed sit amet ante consectetur elit metus. Nulla eleifend Curabitur ornare feugiat ornare. Donec vitae enim elementum semper sodales quis consectetur elit metus. Nulla eleifend tincidunt massa et euismod. Vestibulum ipsum. Aliquam vel condimentum neque. vestibulum, justo vel egestas elementum, tincidunt massa et euismod. Vestibulum sit amet, Lorem ipsum dolor Curabitur ornare feugiat ornare. Donec vestibulum, justo consectetur elementum,elit.enim sit ametquam, vel gravida est vel egestas adipiscing purus Sed ornare ante consectetur elit metus. 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- 12. TF-IDF function getWeight($docID, $term, $total) { $tf = count($term[$docID]); $idf = log($total / count($term), 2); return $tf * $idf; } 12
- 13. Document Vector socket what heavy steel ... Doc 1 0.02 0.3 0.001 0 ... Doc 2 0 0 0 0 ... Doc 3 0.001 0.2 0 0 ... Doc 4 0 0 0.002 0.003 ... 13
- 14. Ranked Query Merge best 23 42 179 246 333 703 weight 0.008 0.002 0.023 0.039 0.014 0.001 western 42 88 120 179 246 798 weight 0.003 0.004 0.023 0.001 0.034 0.004 1 - 246: 0.073 2 - 179: 0.024 3 - 120: 0.023 14
- 15. PHP Similarity function score($queryString, $index) { $query = tokenize($queryString); $matches = array(); foreach($query as $qterm) { $postings = $index[$qterm]; foreach($postings as $id => $posting) { $matches[$id] += $posting['score']; } } return arsort($matches); } 15
- 16. Integrating Search 16
- 17. MySQL Full Text Search CREATE TABLE example ( id INT(11) NOT NULL auto_increment, title VARCHAR(255), content TEXT, PRIMARY KEY(id), FULLTEXT(title,content) ) Engine=MyISAM; INSERT INTO example (title, content) VALUES ('Mikko & Bacon','Mikko loves bacon'), ('Marcello & Bacon','Marcello hates bacon'), ('Jo & Sausages','Johanna loves sausages'), ('Hollywood & Garlic','Lorenzo hates garlic'), ('James & Cheddar','James is keen on cheeses'); 17
- 18. MySQL FTI Query SELECT * FROM example WHERE MATCH(title,content) AGAINST('loves bacon'); +----+------------------+------------------------+ | id | title | content | +----+------------------+------------------------+ | 1 | Mikko & Bacon | Mikko loves bacon | | 2 | Marcello & Bacon | Marcello hates bacon | | 3 | Jo & Sausages | Johanna loves sausages | +----+------------------+------------------------+ 3 rows in set (0.00 sec) 18
- 19. Looking At The Index /var/lib/mysql/fttest# myisam_ftdump example 1 Total rows: 5 Total words: 17 Unique words: 14 Longest word: 9 chars (hollywood) Median length: 5 Average global weight: 1.176117 Most common word: 2 times, weight: 0.405465 (bacon) 19
- 21. Sphinx Configuration source posts { type = mysql sql_host = localhost sql_user = user sql_pass = password sql_db = search sql_query = SELECT id, title, content FROM example; sql_attr_multi = uint tag from query; SELECT example_id, tag_id FROM tags; } 21
- 22. index posts { source = posts path = /var/data/sphinx/example morphology = stem_en min_word_len = 3 min_prefix_len = 3 min_infix_len = 0 enable_star = 1 } 22
- 23. Stemming http://tartarus.org/~martin/PorterStemmer happening - happen happened - happen happens - happen 23
- 24. Command Line Searching indexer --config /etc/sphinx.conf --all search --config /etc/sphinx.conf love bacon displaying matches: 1. document=1, weight=3, tag=(1,2) ! id=1 ! title=Mikko & Bacon ! content=Mikko loves bacon words: 1. 'love': 2 documents, 2 hits 2. 'bacon': 2 documents, 4 hits searchd --config /etc/sphinx.conf 24
- 25. Sphinx From PHP $cl = new SphinxClient(); $cl->SetServer('localhost', 3312); $cl->SetMatchMode(SPH_MATCH_ANY); $result = $cl->Query('bac*'); $docIDs = array_keys($result["matches"]); $cl->SetFilter('tag', array(1)); $result = $cl->Query('bac*'); $docIDs = array_keys($result["matches"]); 25
- 26. Swish-E http://swish-e.org pecl install swish-beta 26
- 27. Filesystem Index With Swish-E /usr/local/bin/swish-e -S fs -c fs-swish-e.conf fs-swish-e.conf IndexDir /var/data/documents IndexFile fs-swish-e.index IndexOnly .doc .docx .pdf FuzzyIndexingMode Stemming_en1 FileFilter .pdf /usr/local/bin/swish_filter.pl FileFilter .doc /usr/local/bin/swish_filter.pl
- 28. Crawling Content /usr/local/bin/swish-e -S prog -c www-swish-e.conf www-swish-e.conf IndexDir /usr/local/lib/swish-e/spider.pl IndexFile www-swish-e.index SwishProgParameters default http://phpir.com/ FuzzyIndexingMode Stemming_en1 DefaultContents HTML
- 29. Swish-E With Multiple Indices $swish = new Swish( 'www-swish-e.index fs-swish-e.index' ); $search = $swish->prepare(); $queryStr = 'search string goes here'; $result = $search->execute($queryStr); $total = $result->hits; while($r = $result->nextResult()) { echo $r->swishdocpath; // url }
- 30. Lucene 30
- 31. $index = Zend_Search_Lucene::create('idx'); foreach($documents as $title => $content) { $doc = new Zend_Search_Lucene_Document(); $doc->addField( Zend_Search_Lucene_Field::Text( 'title', $title)); $doc->addField( Zend_Search_Lucene_Field::UnStored( 'content', $content)); $index->addDocument($doc); } Build Index 31
- 32. $results = $index->find('loves bacon'); foreach($results as $result) { echo $result->score, " "; echo $result->title, "n"; } Output: 0.81656279309067 Mikko and Bacon 0.24800278854758 Marcello & Bacon Query Zend Search Lucene 32
- 33. $file = file_get_contents($url); $doc = Zend_Search_Lucene_Document_Html:: loadHTML($file); $doc->addField( Zend_Search_Lucene_Field::Text( 'url', $url ); $index->addDocument($doc) Index HTML 33
- 35. Solr Search Index $options = array( 'hostname' => 'localhost', 'port' => 8983 ); $client = new SolrClient($options); $doc = new SolrInputDocument(); $doc->addField('id', $id); $doc->addField('cat', $category); $doc->addField('title', $title); $doc->addField('text', $text); $response = $client->addDocument($doc); $client->commit(); 35
- 36. Solr Search Client $client = new SolrClient($options); $query = new SolrQuery('bacon'); $response = $client->query($query); $r = $response->getResponse(); foreach($r['response']['docs'] as $d) { echo $d->title[0] . "n"; } 36
- 37. Xapian http://xapian.org 37
- 38. Xapian In PHP $db = new XapianWritableDatabase( 'idx', Xapian::DB_CREATE_OR_OPEN); $i = new XapianTermGenerator(); $i->set_stemmer(new XapianStem("english")); $doc = new XapianDocument(); $doc->set_data($content); $doc->add_value(1, $title); $i->set_document($doc); $i->index_text($content); $db->add_document($doc); 38
- 39. Xapian Search In PHP $database = new XapianDatabase('idx'); $enquire = new XapianEnquire($database); $qp = new XapianQueryParser(); $qp->set_stemmer(new XapianStem("english")); $qp->set_database($database); $qp->set_stemming_strategy( XapianQueryParser::STEM_SOME); $query = $qp->parse_query($queryString); $enquire->set_query($query); 39
- 40. $matches = $enquire->get_mset(0, 10); $i = $matches->begin(); while(!$i->equals($matches->end())) { $n = $i->get_rank() + 1; $data = $i->get_document()->get_data(); $title = $i->get_document()->get_value(1); $score = $i->get_percent(); $i->next(); } 40
- 41. Improving Results 41
- 42. Anchor Text 42
- 43. Parse Anchor Text $p = file_get_contents('http://phpir.com'); libxml_use_internal_errors(true); $dom = DomDocument::loadHTML($p); $links = $dom->getElementsByTagName('a'); foreach($links as $link) { $href = $link->getAttribute('href'); $text = $link->nodeValue; } 43
- 44. 1 2 3 Zone Weighting 44
- 45. $doc = new Zend_Search_Lucene_Document(); $tfield = Zend_Search_Lucene_Field::Text ('title', $title); $tfield->boost = 1.3; $doc->addField($tfield); $doc->addField( Zend_Search_Lucene_Field::UnStored ('content', $content)); $index->addDocument($doc); ZSL Zone Weighting 45
- 46. Document Authority 46
- 47. Document Weights in ZSL $doc = new Zend_Search_Lucene_Document(); $doc->addField( Zend_Search_Lucene_Field::Text ('title', $title)); $doc->addField( Zend_Search_Lucene_Field::UnStored ('content', $content)); $doc->boost = 1 + ($numComments / 100); $index->addDocument($doc); 47
- 48. Using Search 48
- 49. Summaries & Highlighting 49
- 50. Sphinx Extract & Highlight $cl = new SphinxClient(); $cl->SetServer( "localhost", 3312 ); $q = 'bacon'; $r = $cl->Query($q); foreach ($r["matches"] as $doc => $info) { $text[$doc] = getTextFromDB($doc); } $e = $cl->BuildExcerpts($text, 'posts', $q); foreach($extracts as $extract) { echo $extract; } 50
- 52. Xapian Spelling Correction Indexer $indexer = new XapianTermGenerator(); $indexer->set_database($database); $indexer->set_flags( XapianTermGenerator::FLAG_SPELLING); Searcher $queryString = "strreplace or str_cmp"; $q = new XapianQueryParser(); $q->set_database($database); $query = $q->parse_query($queryString, XapianQueryParser::FLAG_SPELLING_CORRECTION); echo "Did you mean: " . $q->get_corrected_query_string() . "n"; 52
- 53. Spelling Correction Output php xapsearch.php Did you mean: str_replace or strcmp 4644 results found for “strreplace or str_cmp”: 1: 2% docid=572 [phpdocs/html/cc.license.html] 2: 2% docid=7169 [phpdocs/html/imagick.constants.html] 3: 2% docid=10086 [phpdocs/html/sqlite3result.fetcharray.html] 4: 2% docid=6132 [phpdocs/html/function.swf-posround.html] 53
- 54. Results Sorting 54
- 55. Sorting in ZSL $q = Zend_Search_Lucene_Search_QueryParser:: parse('search string'); $results = $index->find($q, 'title'); foreach($results as $result) { echo '<h3>', $result->title, "</h3>n"; $doc = getDocumentFromDB($result->did); echo $q->htmlFragmentHighlightMatches($doc); } 55
- 56. Faceted Search 56
- 57. Faceted Search In Solr $client = new SolrClient($options); $query = new SolrQuery('bacon'); $response = $client->query($query); $query->setFacet(true); $query->addFacetField('cat'); $r = $response->getResponse(); $f = $r['facet_counts']['facet_fields']; foreach($f['cat'] as $facet => $count) { echo $facet . " " . $count . "n"; } 57
- 58. More Like This 58
- 59. More Like This $rset = new XapianRset(); $rset->add_document(5959); // str_replace $e = $enquire->get_eset(40, $rset); $t = $e->begin(); for($t; !$t->equals($e->end()); $t->next()){ $qs[] = new XapianQuery($t->get_term(), intval($t->get_weight())); } $query = new XapianQuery( XapianQuery::OP_OR, $qs); 59
- 60. More Like This Example php xapsim.php 1656 results found: 1: 100% docid=5959 [phpdocs/html/function.str-replace.html] 2: 47% docid=5956 [phpdocs/html/function.str-ireplace.html] 3: 24% docid=5328 [phpdocs/html/function.preg-replace.html] 4: 18% docid=5958 [phpdocs/html/function.str-repeat.html] 60
- 61. Search Performance 61
- 62. Index Updates New Docs Docs Delta Docs Docs Delta Main Main Query Main Delta Main 62
- 63. Search Speed Zend Search Lucene $index = Zend_Search_Lucene::open('index'); $index->optimize(); Sphinx indexer --merge main delta --rotate Solr $client = new SolrClient($options); $client->optimize(); Xapian xapian-compact xapindex xapindex2 63
- 64. Distributing Search Document Document Document Document Index Index Index Application 64
- 65. Large Scale Search http://www.nutch.org http://hadoop.apache.org 65
- 66. Image Credits Title http://www.flickr.com/photos/generated/2084287794/ What Do You Want http://www.flickr.com/photos/the_justified_sinner/ You Are Here 2498066986/ http://www.flickr.com/photos/alecvuijlsteke/2692475420/ Integrating Search http://www.flickr.com/photos/squeaks2569/3700355684/ Sphinx http://www.flickr.com/photos/generated/2084287794/ Lucene http://www.flickr.com/photos/mypanda/7731447/ Swish-e http://www.flickr.com/photos/ryan_fung/2239687100/ Solr http://www.flickr.com/photos/m-j-s/2724756177/ Xapian http://www.flickr.com/photos/olibac/3522056495/ Using Search http://www.flickr.com/photos/eneas/175027945/ Improving Search http://www.flickr.com/photos/x-ray_delta_one/3928200642/ Search Performance http://www.flickr.com/photos/maisonbisson/1634408/ Large Scale Search http://www.flickr.com/photos/zedzap/3663508847/ 66
- 67. Questions? 67
- 68. Thank you! Ian Barber @ianbarber http://phpir.com ian@ibuildings.com http://joind.in/talk/view/1462
Notas del editor
- Contact Details
- This is a question we&#x2019;d often like to ask our users But with search, they tell us Search is about getting content to users that want it Searching users are Active and engaged Give them what they want and they are more likely to Buy, Read, Comment, Share etc.
- This talk covers how full text search works, looks at some different options for integration looks at making it better Time for questions at the end, but one does spring to mind now:
- Why search, why not let google do it? Private, intranet, FB inbox, offline Bad at, twitter for a long time, blogs for a long time Product focus, like amazon Speed of update, like a forum Now, lets look at how a full text search operates.
- Search Engine Structure Raw Text Documents (add url, title, split up etc.) Text Analysis Index Query Parser Query Results Search UI
- Simplified structure of a search engine. Start with pool of raw data, chunked into documents Analyser processes text in docs , Index stores Other side: Search UI Query parsed by query parser, like anlyser, Searched on index and Results sorted and returned
- Tokenising is taking a document and splitting it into tokens to index. Can be difficult, even with space char. Commas - remove punctuation - then send 1.1 mil to 11 mil! Hyphens Apostrophes
- That said, starting with something simple isn&#x2019;t a bad idea. Here we look for continuous sequences of word chars Capture with offset, which is for phrase matching. More advanced SEs have better tokenisation: & in AT&T Some instead have buzzwords file, specific terms: C++
- Pair extracted tokens with assigned doc ID Filter stop words - an, the, of - don&#x2019;t distinguish Position info included
- Invert and merge pairs, so terms -> doc Positions still stored, represented by () e.g best @ 4 and 16 in doc 1. Often stored separate, or just a straight count List of docs == posting list Enough to start a search
- Take search query and tokenise the same way. Important! For each term we array_intersect. Can do boolean searches by doing array union for OR etc. BUT no RANK - any result with all words as good as other Must store importance of terms to documents - weight
- The weighting scheme includes two measures TF - term frequency, the count of terms in the document IDF, inverse document frequency, the rareness of the term in the collection
- Simple but usable weight algo, basis of most. TF - Count of times term appears IDF - total docs / docs with term, 10 total / 3 with term. Log to smooth Store this score with the document in the posting list for the term Normalise scores over a doc to acct for length - but still boosts short text
- TF-IDF PHP code
- TF-IDF PHP code
- Document is position in N dimension space One dimension for each term ever seen Mostly 0 Normalised to length 1 (sqrt of sum of sqrs of vals)
- Just look at 2 terms here to keep it simple Here, rather than just looking for matches We accumulate a score for each matching document 246 is our highest scoring document, picking up two good scores But 120 makes it in at number three, despite not having &#x2018;best&#x2019; in it.
- For a 2 term, 2 dimension case, that looks like this. Calculate cosine of angle between with dot prod Similarity - 1 = same, 0 = orthogonal (no shared terms) We can treat a query as a new document The documents it is most similar to are the best results Only need to compare to documents that share terms -rest will be score 0
- Look at query terms, retrieve posting list from index Treat query term weights as 1 - incorrect, but ok for relative results Index merge, and calc dot product by summing weights. So, don&#x2019;t need a full match Could add phrase search, or positional bias.
- Two main question Where does the data come from? How is the index accessed? Look at 6 PHP friendly engines Each different integration method Each with new bits of functionality
- Data from a database columns in one table Simplest of all to implement - integrate through query Note fulltext index. Straight vector space search impl. as described before. Only can be used for MyISAM, not InnoDB If you&#x2019;re using postgres, tsearch built in since 8.3
- MATCH AGAINST syntax Boolean too - all engines have this, we focus on natural Only one document has both words Ranked in score order - MATCH AGAINST returns a float Note there&#x2019;s some tricky default config: min word length 4, and 50% fill exclusion
- One interesting option is Query Expansion - Blindly expand the search based on words returned. Usually not a very good idea, because we want more precision that recall Precision is quality of results, recall is completeness In this case it&#x2019;s expanded to lorenzo, because of marcello&#x2019;s hatred for bacon
- Can actually interrogate the index myisam_ftdump Run from the database directory However, lets say you want to search on a normalised schema directly - multiple tables
- Using sphinx you can index a more complex query Used on craigslist, and apparently on The Pirate Bay There is a PHP API for access, or extension pecl/sphinx Same interface but faster
- Once installed, setup sphinx.conf file Top: Connection Stuff - also works with postgresql Indexing on sql_query - could use view, complex etc. Adding attributes - non indexed elements of a doc - Numeric or timestamp only in sphinx. Using multi valued attributes, support tags many to many Other options, such sql_query_pre or post
- Next tell sphinx about the index Minimum length of indexed word Prefix for wild card search - infix anywhere, prefix end We also enable a stemmer
- Stemming consistently collapses different forms of the same word to a stem Here each version is reduced to happen, but not always an english word is generated, just a consistent one This allows us to match more words, and is often, but not always, helpful The most common algorithm is the Porter stemmer, there is a PHP implementation on the site
- Indexer command to build index Might lock DB table, there is a ranged table work around Command line search, defaults to require all Stemmer - love vs loves Last line - start indexing daemon
- Match any word Wildcard search - prefix search, Returns both &#x2018;bacon&#x2019; docs Add filters - limiting to certain values of attributes Now we just get 1 result Sphinx can be built into mysql as a table type, and queried via a where arg
- From the other end - Swish is easy to plug in to existing system at short notice Swish-e is an engine with a long pedigree, and a PHP extension. Used by quite a few universities. Doesn&#x2019;t support multibyte charsets, which is a bit of a downside. Great for combinations where you have a bunch of word docs or similar documents, and a website, and you want to search both.
- First &#x2018;fs&#x2019; for file system index - we create a conf file for indexer In the conf we tell it where to look for files FileFilters extract text from non-text formats doc/pdf Can specify IndexDir multiple times different doc stores Requires wv ware and xpdf Apache Tika
- Includes an effective web crawler, another way to get data Getting it through the web loses some of the advantages Can plug into website no real control over Mode is prog to call out to the spider script Index file is different name
- Being able to query across the two indexes is very handy Here we search fs and www indexes and give combined results Can use various filters to limit search to parts of HTML documents Or filter on file system paths
- Now we&#x2019;ll look at engines where we index from within PHP Lucene, apache foundation search engine Very succesful, but has ports instead of bindings Native PHP port in Zend Framework, Zend Search Lucene
- Hook right into the application, easy addition/plugin Lots of control, easy to add metadata/attributes Lucene calls them fields: string keys, multiple value types Text indexed and original stored - unstored not Index compatible w/ Java lucene 2.3 - can index java, search PHP
- Querying is straightforward, and quick. The scores are only really interesting as a relative value
- Includes some handy utilities such as HTML doc parser Spits out various fields such as title and body auto Allows you to add other fields as required Advantage of PHP - easy to hack at, add new doc types HOWEVER - doesn&#x2019;t scale to large collections so you may prefer to use one the Java based versions... and the easiest way is with Solr
- Solr uses java lucene - wraps in REST+XML/JSON web service Convenient for all the usual SOA reasons. Solr is in use by CNET, digg, netflix and other high profile sites. There is a PHP extension, or a PHP client API
- Not massively different from ZSL. Solr needs you to create a schema first, to define the fields of docs Note the client commit down the bottom. Until a document is committed Hardly know you&#x2019;re using a webservice
- Searching is similar XML based response format means a more complex return struct Solr is great for larger scale collections Provides good admin functionality - enterprise friendly
- Our last engine is Xapian High performance C based search engine. There is a Solr like service called Flax, but we&#x2019;ll look at the engine directly. PHP SWIG based extension and low level API Gives some cool features, and a lot of control Creates database on FS, or can be accessed remote
- Separation between the document and the indexer Integration of stemmer - english here We have an numeric indexed attribute, referred to as a &#x2018;value&#x2019; here, for the title
- Xapian index (local etc.)
- The searching is more complicated We have more control - STEM_SOME, don&#x2019;t stem words that start with a capital letter (proper nouns)
- Xapian query
- Retrieving the result relies on these functions wrapping around C iterators. Note the percentage score value - overtly relative, but can be thresholded if needed
- Xapian query result
- We have search engine We know where data coming from How can we improve results
- Link text can be a great source of keywords To use a classic example, from one of the early papers about google, if someone types &#x2018;big blue&#x2019; into google, one of the top results is IBM.com. But the page it points to doesn&#x2019;t contain the phrase Things link to it that do contain that phrase, and Google index against it. Big win for things like images and videos, where there may be no text
- Need to parse document Easy in PHP with the DOM parser We could then add these to the index, as a new field on a document ZSL has a built in html document type, but the getLinks function doesn&#x2019;t include anchor text
- Anchor text extract
- The next idea is zone weighting. This is a page from my blog I know what&#x2019;s important on this page - 1 to 3 Google has to guess, based on appearance Green = boilerplate - don&#x2019;t index Index these zones as fields, and weight differently
- If we break our content down into fields, We can set different &#x2018;boost&#x2019; values on them Boosts > 1 more important, < 1 less important E.G. de-emphasise comments
- Document Weight - Importance, Authority In general - not tied to specific query Page rank - but that wont work on small collection Comments - &#x201C;great post&#x201D;, comment count Inbound visitors Retweets - Google uses a UserRank PR type calculation on follower counts
- Similar to zones, boost at document level The default is 1 Adding one 100th for each comment This of course could be tuned for individual circumstances
- Got engine, got data, got good results Now, look at ways to improve search user experience
- With UI - do what other websites do With search - do what google et al do Summaries or snippits show a selection of the page
- Sphinx build highlights
- Most search engines have some support for this. With Sphinx here, we can pass the query and index name to the BuildExcerpts function to get highlighted contextual snippits getTextFromDB is just a pretend function that would wrap retrieving the raw full text.
- We can do by storing some of the original text in SE We&#x2019;ve added a StoreDescription based on the body, for 1000 characters This will appear in the result object as swishdescription. We may want to index more, then choose the bit we display based on the presence of query words.
- Google highlighted search terms on summaries Can do on whole document as well Easy to do in many engines ZSL highlight matches - could use stored field or external HighlightMatches without fragment will add HTML headers
- Spelling correction is a really handy function Important to correct to known words from the index Rather than default dictionary
- Xapian example - set flag on indexer & queryparser. We had an index based on PHP documentation Have mistyped str_replace and strcmp
- Function names were corrected, despite not being &#x2018;words&#x2019; They featured in index, and had low edit distance from query Some low quality results returned - where we might use threshold Solr/Lucene has a similar plugin
- Another useful idea is sorting result sets on other than rank This is an example from google news E.G. file search, email, private messages may want others (sender, date, subject)
- Here we&#x2019;ve added a sort on title Can be expensive as SEs can&#x2019;t do normal shortcuts But normally straightforward
- We&#x2019;ve got a search here on epicurious, the food and cooking site. Shows categories and result counts This is called faceted search, categories = facets Document has many categories Good for product based search Solr was built with faceted search in mind for CNET reviews
- Enable faceted mode, set one facet, &#x2018;cat&#x2019; If we&#x2019;d been duplicating epicurious, each of the options on the left would have been a facet. Get results plus enumeration of options in each facet + count
- User can offer feedback by selecting more like this Find documents like this one Good for search with many meanings - &#x2018;creed&#x2019; (game, band, belief) Example from a dissertation search engine
- Generate search from document user selected Xapian has built in, can do in Solr as well. Top 40 most important terms extracted (can do more than one doc) Using str_replace from index of phpdoc Combine terms with ORs
- Finds itself, and other good matches MySQL FTI has blind query expansion, which gets more results based on the results retrieved - not as good, and hella slow!
- Search can be expensive Lots of data to process Most engines have some sort of query cache built in We&#x2019;ll take a quick look at some different aspects of performance.
- Indexes designed for more read than write Adding data can be expensive to a large index. Have two indexes Merge Lucene uses segments automatically
- Smaller index: less IO, better O/S cache, faster results But slower update speed Recombine segments, Merge deltas Optimise and compress index This can be an expensive operation though. Try to keep index on local disk, not network
- When demands too big for a single server, need to look at distributing Replication tends not to give such a boost here, as you generally have too large an index which is too slow for single queries, rather than scale Need to shard contents based on hash - something not searched for Most systems have a way of working with remote backends, to give single search and sort point
- The systems we&#x2019;ve talked about will all index tens of thousands of documents Xap and Solr should handle into the millions on one server 100s of mil/billions = webscale - Challenges: Data size, rate of update Nutch is a FOSS webscale SE/crawler created by Doug Cutting, of Lucene. Also did hadoop: mapreduce, distributes files etc. (not being sued by google) Used on thousands of nodes at yahoo, among others
- Thanks!