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Apache Solr for begginers

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The presentation describes what is Apache Solr, how it could be used. There is apache solr overview, performance tuning tips and advanced features description

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Apache Solr for begginers

  1. 1. Full text search for lazy guys STARRING APACHE SOLR
  2. 2. Agenda • Introduction • FTS solutions • FTS patterns • Apache Solr • Architecture • Client libraries • data treatment pipeline • Index modeling • ingestion • Searching • Demo 1 • Solr in clustered environment • Architecture • Idexing • quering • Demo 2 • Advanced Solr • Cool features overview • Performance tuning • Q&A sessions
  3. 3. FTS solutions attributes 1. Search by content of documents rather than by attributes 2. Read-oriented 3. Flexible data structure 4. 1 dedicated tailored index used further for search 5. index contains unique terms and their position in all documents 6. Indexer takes into account language-specific nuances like stop words, stemming, shingling (word-grams, common-grams)
  4. 4. FTS architectures Id Price Weight Description RDBMS FTS FTS server Index
  5. 5. FTS usage patterns 1. Spell checking 2. Full text search 3. Highlighting
  6. 6. FTS usage patterns 1. Suggestions 2. Faceted search 3. Paging
  7. 7. Market leaders
  8. 8. FTS scope
  9. 9. Q&A
  10. 10. Solr • True open source (under Apache) full text search engine • Built over Lucene • Multi-language support • Rich document parsing (rtf, pdf, …) • Various client APIs • Versatile query language • Scalable • Full of additional features 
  11. 11. Well-known Solr users and many others in https://wiki.apache.org/solr/PublicServers
  12. 12. Architecture
  13. 13. Client access 1. Main REST API • Common operations • Schema API • Rebalance/collection API • Search API • Faceted API 2. Native JAVA client SolrJ 3. Client bindings like Ruby, .Net, Python, PHP, Scala – see https://wiki.apache.org/solr/IntegratingSolr + https://wiki.apache.org/solr/SolPython 4. Parallel SQL (via REST and JDBC)
  14. 14. Inverted index
  15. 15. Index modeling Choose Solr mode: 1. Schema 2. Schema-less Define field attributes: 1. Indexed (query, sort, facet, group by, provide query suggestions for, execute function) 2. Stored – all fields which are intended to be shown in a response 3. Mandatory 4. Data type 5. Multivalued 6. Copy field (calculated) Choose a field for UniqueIdentifier
  16. 16. Field data types 1. Dates 2. Strings 3. Numeric 4. Guid 5. Spatial 6. Boolean 7. Currency and etc
  17. 17. Real life-schema
  18. 18. Text processing Intended to mitigate differences between terms to provide perfect search
  19. 19. Text processing Set of filters to get desired results
  20. 20. Text processing Set of filters to get desired results
  21. 21. Ingestion
  22. 22. Transaction management 1. Solr doesn’t expose immediately new data as well as not remove deleted 2. Commit/rollback should be issued Commit types: 1. Soft Data indexed in memory 1. Hard It moves data to hard-drive Risks: 1. Commits are slow 2. Many simultaneous commits could lead to Solr exceptions (too many commits) <h2>HTTP ERROR: 503</h2> <pre>Error opening new searcher. exceeded limit of maxWarmingSearchers=2, try again later.</pre> 3. Commit command works on instance level – not on user one
  23. 23. Transaction log Intention: 1. recovery/durability 2. Nearly-Real-Time (NRT) update 3. Replication for Solr cloud 4. Atomic document update, in-place update (syntax is different) 5. Optimistic concurrency Transaction log could be enabled in solrconfig.xml <updateLog> <str name="dir">${solr.ulog.dir:}</str> </updateLog> Atomic update example: {"id":"mydoc", "price":{"set":99}, "popularity":{"inc":20}, "categories":{"add":["toys","games"]}, "promo_ids":{"remove":"a123x"}, "tags":{"remove":["free_to_try"," on_sale"]} }
  24. 24. Data modification Rest API Rest API accepts: 1. Json objects 2. Xml-update 3. CSV Solr UPDATE = UPSERT if schema.xml has <UniqueIdentifier>
  25. 25. Data modification Rest API curl http://192.168.77.65:8983/solr/single-core/update?commit=true -H 'Content-type:application/json' -d ' [ {"id" : "3", "internal_name":"post 2", }, {"id" : “1", "internal_name":"post 1", } ]‘ Data.xml <add> <doc> <field name='id'>8</field> <field name='internal_name'>test1</field> <doc> <doc> <field name='id'>9</field> <field name='internal_name'>test6</field> <doc> </add> curl -X POST 'http://192.168.77.65:8983/solr/single-core/update?commit=true&wt=json' -H 'Content-Type:text/xml' -d @data.xml Delete.xml <delete> <id>11604</id> <id>:11603</id> </delete> Delete_with_query.xml <delete> <query>id:[1 TO 85]</query> </delete>
  26. 26. Post utility 1. Java-written utility 2. Intended to load files 3. Works extremely fast 4. Loads csv, json 5. Loads files by mask of file-by-file bin/post -c http://localhost:8983/cloud tags*.json ISSUE: doesn’t work with Solr Cloud
  27. 27. Data import handler 1. Solr loads data itself 2. DIH could access to JDBC, ATOM/RSS, HTTP, XML, SMTP datasource 3. Delta approach could be implemented (statements for new, updated and deleted data) 4. Loading progress could be tracked 5. Various transformation could be done inside (regexp, conversion, javascript) 6. Own datasource loaders could be implemented via Java 7. Web console to run/monitor/modify
  28. 28. Data import handler How to implement: 1. Create data config <dataConfig> <dataSource name="jdbc" driver="org.postgresql.Driver" url="jdbc:postgresql://localhost/db" user="admin" readOnly="true" autoCommit="false" /> <document> <entity name="artist" dataSource="jdbc" pk="id" query="select *from artist a" transformer="DateFormatTransformer" > <field column="id" name="id"/> <field column="department_code" name="department_code"/> <field column="department_name" name="department_name"/> <field column = "begin_date" dateTimeFormat="yyyy-MM-dd" /> </entity> </document> </dataConfig> 2. Publish in solrconfig.xml <requestHandler name="/jdbc" class="org.apache.solr.handler.dataimport.DataImportHandler "> <lst name=“default"> <str name="jdbc.xml</str> </lst> </requestHandler> DIH could be started via REST call curl http://localhost:8983/cloud/jdbc -F command=full-import
  29. 29. Data import handler In process: <?xml version="1.0" encoding="UTF-8"?> <response> <lst name="responseHeader"> <int name="status">0</int> <int name="QTime">0</int> </lst> <lst name="initArgs"> <lst name="defaults"> <str name="config">jdbc.xml</str> </lst> </lst> <str name="status">busy</str> <str name="importResponse">A command is still running...</str> <lst name="statusMessages"> <str name="Time Elapsed">0:1:15.460</str> <str name="Total Requests made to DataSource">39547</str> <str name="Total Rows Fetched">59319</str> <str name="Total Documents Processed">19772</ str> <str name="Total Documents Skipped">0</str> <str name="Full Dump Started">2010-10-03 14:28:00</str> </lst> <str name="WARNING">This response format is experimental. It is likely to change in the future.</ str> </response>
  30. 30. Data import handler After import: <?xml version="1.0" encoding="UTF-8"?> <response> <lst name="responseHeader"> <int name="status">0</int> <int name="QTime">0</int> </lst> <lst name="initArgs"> <lst name="defaults"> <str name="config">jdbc.xml</str> </lst> </lst> <str name="status">idle</str> <str name="importResponse"/> <lst name="statusMessages"> <str name="Total Requests made to DataSource">2118645</str> <str name="Total Rows Fetched">3177966</str> <str name="Total Documents Skipped">0</str> <str name="Full Dump Started">2010-10-03 14:28:00</str> <str name="">Indexing completed. Added/Updated: 1059322 documents. Deleted 0 documents.</str> <str name="Committed">2010-10-03 14:55:20</str> <str name="Optimized">2010-10-03 14:55:20</str> <str name="Total Documents Processed">1059322</str> <str name="Time taken ">0:27:20.325</str> </lst> <str name="WARNING">This response format is experimental. It is likely to change in the future.</str> </response>
  31. 31. Search
  32. 32. Search
  33. 33. Search typesFuzzy Developer~ Developer~1 Developer~4 It matches developer, developers, development and etc. Proximity “solr search developer”~ “solr search developer”~1 It matches: solr search developer, solr senior developer Wildcard Deal* Com*n C??t Need *xed? Add ReversedWildcardFilterFactory. Range [1 TO25] {23 TO50} {23 TO90]
  34. 34. Search characteristics 1. Similarity 2. Term frequency Similarity could be changed via boosting: q=title:(solr for developers)^2.5 AND description:(professional) q=title:(java)^0.5 AND description:(professional)^3
  35. 35. Search result customization Field list /query?=&fl=id, genre /query?=&fl=*,score Sort /query?=&fl=id, name&sort=date, score desc Paging select?q=*:*&sort=id&fl=id&rows=5&start=5 Transformers [docid] [shard] Debuging /query?=&fl=id&debug=true Format /query?=&fl=id&wt=json /query?=&fl=id&wt=xml
  36. 36. Search queries examples Parameter style curl "http://localhost:8983/cloud/ query?q=heroy&fq=inStock:true" JSON API $ curl http://localhost:8983/cloud/query -d ' { query:"hero" "filter" : "inStock:true" }' Response { "responseHeader":{ "status":0, "QTime":2, "params":{ "json":"n{n query:"hero" "filter" : "inStock:true" n}"}}, "response":{"numFound":1,"start":0,"docs":[ { "id":"book3", "author":"Brandon Sanderson", "author_s":"Brandon Sanderson", "title":["The Hero of Aages"], "series_s":"Mistborn", "sequence_i":3, "genre_s":"fantasy", "_version_":1486581355536973824 }] } }
  37. 37. Q&A
  38. 38. SolrCloud ALEX 2 turn!!!!
  39. 39. Advanced Solr 1. Streaming language Special language tailored mostly for Solr Cloud, parallel processing, map-reduce style approach. The idea is to process and return big datasets. Commands like: search, jdbc, intersect, parallel, or, and 2. Parallel query JDBC/REST to process data in SQL style. Works on many Solr nodes in MPP style. curl --data-urlencode 'stmt=SELECT to, count(*) FROM collection4 GROUP BY to ORDER BY count(*) desc LIMIT 10' http://localhost:8983/solr/cloud/sql 3. Graph functions Graph traversal, aggregations, cycle detection, export to GraphML format 4. Spatial queries There is field datatype Location. It permits to deal with spatial conditions like filtering by distance (circle, square, sphere) and etc. &q=*:*&fq=(state:"FL" AND city:"Jacksonville")&sort=geodist()+asc 5. Spellchecking It could be based on a current index, another index, file or using word breaks. Many options what to return: most similar, more popular etc http://localhost:8983/solr/cloud/spell?df=text&spellcheck.q=delll+ultra+sharp&spellcheck=true 6. Suggestions http://localhost:8983/solr/cloud/a_term_suggest?q=sma&wt=json 7. Highlighter Marks fragments in found document http://localhost:8983/solr/cloud/select?hl=on&q=apple 8. Facets Arrangement of search results into categories based on indexed terms with statistics. Could be done by values, range, dates, interval, heatmap
  40. 40. Performance tuning Cache Be aware of Solr cache types: 1. Filter cache Holds unordered document identifiers associated with filter queries that have been executed (only if fq query parameter is used) 2. Query result cache Holds ordered document identifiers resulting from queries that have been executed 3. Document cache Holds Lucene document instances for access to fields marked as stored Identify most suitable cache class 1. LRUCache – last recently used are evicted first, track time 2. FastLRUCache – the same but works in separate thread 3. LFUCache – least frequently used are evicted first, track usage count Play with auto-warm <filterCache class="solr.FastLRUCache" size="512“ initialSize=“100" autowarmCount=“10"/> Be aware how auto-warm works internally – doesn’t delete data, repopulated completely
  41. 41. Performance tuning Memory Care about OS memory for disk caching Estimate properly Java heap size for Solr – use https://svn.apache.org/repos/asf/lucene/dev/tags/lucene_solr_4_2_0/dev -tools/size-estimator-lucene-solr.xls
  42. 42. Performance tuning Schema design 1. Try to decrease number of stored fields mark as indexed only 2. If fields are used only to be returned in search results – use stored only
  43. 43. Performance tuning Ingestion 1. Use bulk sending data rather than per-document 2. If you use SolrJ use ConcurentUpdateSolrServer class 3. Disable ID uniqueness checking 4. Identify proper mergeFactor + maxSegments for Lucene segment merge 5. Issue OPTIMIZE after huge bulk loadings 6. If you use DIH try to not use transformers – pass them to DB level in SQL 7. Configure AUTOCOMMIT properly
  44. 44. Performance tuning Search 1. Choose appropriate query parser based on use case 2. Use Solr pagination to return data without waiting for a long time 3. If you return huge data set use Solr cursors rather than pagination 4. Use fq clause to speed up queries with one equal condition – time for scoring isn’t used + results are put in cache 5. If you have a lot of stored fields but queries don’t show all of them use field lazy loading <enableLazyFieldLoading>true</enableLazyFieldLoading> 6. Use shingling to make phrasal search faster <filter class="solr.ShingleFilterFactory“ maxShingleSize="2" outputUnigrams="true"/> <filter class="solr.CommonGramsQueryFilterFactory“ words="commongrams.txt" ignoreCase="true""/>
  45. 45. Q&A
  46. 46. THANKYOU AND WE ARE HIRING! Alexander Tokarev Senior Developer, DataArt atokarev@dataart.com

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