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Search with Solr
- 1. Searching with SolrWhen, Why and How? By Paul Matthews
- 2. 86p @paulmatthews86 86p.paul-matthews.co.uk pmatthews@ibuildings.com techportal.ibuildings.com Projects: Travel companies Media corporations 1
- 3. Searching… What? When? Why? How? 2
- 4. Searching… What? When? Why? How? 3
- 5. What is search? Text navigation Customers describing Sorting Examples Quick search Category listings 4
- 6. The power of search 5
- 8. Database Like Very little effort A very basic search Poor at: > 1 word 7
- 10. Database Full-Text Some power Convenient Feature poor Often very slow 9
- 11. Basic Search Systems 10
- 12. Basic Search Systems Rapid search Simple to setup Feature poor Accuracy Require more application code 11
- 13. Solr Search 12
- 14. Solr Search Very powerful Feature rich Relatively simple Lots of plugins (community) Overkill? Java 13
- 15. Things you need to know 14
- 16. Searching… What? When? Why? How? 15
- 17. Applicable to me? Who is Solr designed for? Traffic Features When is a good time to implement it? Creation Post-live Open Source projects 16
- 18. Business indicators Money / Time / Effort spent Bugs Tuning Features Customers 17
- 19. Development indicators Data MySQL Full Text Degradation 18
- 20. Searching… What? When? Why? How? 19
- 21. Is Solr right for me? Know your enemy With great functionality comes great responsibility 20
- 22. Data sources Database Easy API Features CSV & XML Solr Cell - Rich Documents PDF MS Office 21
- 23. Indexing Parsing Half now, half later 22
- 24. Analyzer Process documents The query gets analyzed too 23
- 25. Tokenizer 24
- 27. Stemming Matching similar words Reduce to Stem 26 Searching Search Searches Searched Searchers Search
- 28. Hit Highlighting “Hit” ==> “This is a <em>Hit</em> test.” 27
- 29. Spell Check Spelchk Did you mean …? “flickr” 28
- 30. 29
- 31. By the power of Queries! Phrase “Search for a phrase” Wildcards Look*familiar? Fuzzy fuzzy~ Proximity “two words”~12 Range name:{Paul TO Jeff} 30
- 32. name:paul AND location:uk A single field Multiple Fields 31
- 33. Faceting (21) Pre-fetching (11) Results (37) 32
- 34. Ranked Search Ordered Any field 33
- 35. Simultaneous update & search Hold on a minute! Actually, I don’t have to… 34
- 36. Searching… What? When? Why? How? 35
- 37. Flow 36
- 38. Container Choose container Make accessible http://<host>:<port>/solr/admin 37
- 39. SolrConfig Cores ~ Database Schema schema.xml ~ Schema definition 38
- 40. Fields Define the data indexed Stored Important to model accurately Tweak to achieve functionality Conscious of space and index 39
- 41. Index Create documents to Schema Spec 40
- 42. Search Quick Search Default Search Advanced Search 41
- 43. Quick Search Partial words Search all fields? Required response data 42
- 44. Default Search Consider useful Analyzers Potentially match on more fields Enrich or refine results with personal data More in depth results 43
- 45. Advanced Search Offer user control Consider search storage Data size vs Additional queries To return more / less results “Search entire document” “Filter by Colour” 44
- 46. Searching… What? When? Why? How? 45
- 47. Questions? 46
- 48. We’re Hiring NL Vlissingen Utrecht UK London Sheffield Liverpool Speak to me at the end… pmatthews@ibuildings.com 47
- 49. Thank you Resources Links: http://www.delicious.com/paulm86/solr This talk: http://joind.in/3221 Contact Me: @paulmatthews86 http://about.me/paul.matthews 48
Notes de l'éditeur
- Twitter: @paulmatthews86Personal Blog: 86pTechnicalNon-techSoftware Engineer at IbuildingsTechportalMongoDBSolr (May 2011)Solr ProjectsTravel CompanyMedia Company
- This talk What Is Solr? When is right timeWhySearch ?How Start journey– investigate Explain to business integrateWho is this talk aimed at? Developers Toying with search DB search Starting with search
- This talk When right time – identifying Why Search benefits Dark horse How Start journey– investigate Explain to business integrateWho is this talk aimed at? Developers Toying with search DB search Starting with search
- What is search? Text based navigation To content / products Customers describing something Capture queries SortingOrganising content Examples Quick search Category listing Advanced search
- The Power of SearchFrom LIKE to SOLR
- First up DB Like
- Pros: Little effort to use, or understand.Cons: Not good User data: Not greater than 1 word
- Full Text Lots of people use
- Pros: Some power Convenient In DBCons: Feature poor Slow
- Basic / Easy to use proper Search
- Pros: Can be very fast Often simple to setupCons: Feature poor Less accurate More application code?Google Custom Search Engine Crawls siteXapian Simple search solution
- Pros:Poweful Feature rich Relatively Simple Lots of pluginsCons: Could be overkill Different language
- On Java stand alone Requires servlet container Tomcat Jetty stand alone Lucene Search library Offers Full Text High performance Java - other implementations available
- This talk When right time – identifying Why Search benefits Dark horse How Start journey– investigate Explain to business integrateWho is this talk aimed at? Developers Toying with search DB search Starting with search
- Who? Traffic Not for Facebook Works for average Features It has many No need to use themWhen? Designed from beginning Easily used to enrich site navigation Implementation as post-live project Implementation into existing open source softwareDrupalMagento
- Spending time / effort / money on the search box Fixing bugs Endless tuning Adding functionalityCustomers complaining Not finding content High Bounce rates Site is slow Not finding the *right* content
- Large data sets 10000 records Speed Like queriesMySQL full-text Site performanceSlowlog? Results Inaccurate MissingGraceful degradation Important for quality Low cost
- This talk When right time – identifying Why Search benefits Dark horse How Start journey– investigate Explain to business integrateWho is this talk aimed at? Developers Toying with search DB search Starting with search
- Is Solr right for me?Before Answering:Terms:Find materialsCommunicate to peopleFunctionality:Most Use – Know FunctionalityRe-invent – Wheel
- Main 2Database tables Data Import Handler Easy – just configAPI Anything publish API Hooked into contentCSV & XMLSolr Cell - Rich Docs PDF MS Office
- Parse: text generate index Removes junk Improve matchesHalf now, half later: Reduce time searching
- Analyzer Groups actions of Parsing Important to do same / similar in searching
- TokenizerStrings to tokensExample ones:Whitespace – splits on whitespaceKeyword – strips special charsStandard – General purpose, adds context
- Transforms tokensLower case.Stop – filters out stop words: a, if, to, andStandard – Remove dots, ‘s (Context only)Synonym.
- Hit Highlighting* Remember to set the delimiter, not everything is a web page.
- Spell checkingConfigureSpellingsNames - FlickrKeywords
- Autocomplete Common queries
- Phrase queries "search for a phrase"Wildcard queries Match with wildcards ? single * multipleFuzzy queriesLevenshtein Distance Similar to word ~Proximity queries Words close together "two words"~12Range queries Between two values started:[20110101 TO 20120101] Inclusivename:{Paul TO Jeff} exclusive
- Fields Single field Target search Multiple field Build Queries
- Faceted Set Counts Filter data Multiple classifications
- Ordered results based on best matchOr order by any field
- Simultaneous update and search
- This talk When right time – identifying Why Search benefits Dark horse How Start journey– investigate Explain to business integrateWho is this talk aimed at? Developers Toying with search DB search Starting with search
- Blog post – to explainsConfigure ContainerSolrIndex Documents Any sourceSearch Default search Advanced search
- Container setup Choose Configure Accessible
- Define the data Define what is indexed Define what is storedIntegral to returning relevant search responsesRequire tweaking to get rightConscious of space size of the index - speed
- Docs to Schema SpecIndexing by Database or API
- Partial Words Analyzing?Search all fields Possibly the main onesResponse Less data Stay clear of additional queries consider caching
- Consider using stemming analyzers to return more resultsIncrease matching columnsUse session data affect results Consider caching effectsMore response data required
- Users modify their search Specify fields For enriching the results Consider bloated storage Tradeoff with Additional queries Tweak later?Advanced for returning More / Less results Search more of the document Filter on property
- This talk When right time – identifying Why Search benefits Dark horse How Start journey– investigate Explain to business integrateWho is this talk aimed at? Developers Toying with search DB search Starting with search
- Twitter: @paulmatthews86Personal Blog: 86pTechnicalNon-techSoftware Engineer at IbuildingsTechportalMongoDBSolr (May 2011)Solr ProjectsTravel CompanyMedia Company