Introduction to Apache Lucene/Solr
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This document provides an introduction to Apache Lucene and Solr. It begins with an overview of information retrieval and some basic concepts like term frequency-inverse document frequency. It then describes Lucene as a fast, scalable search library and discusses its inverted index and indexing pipeline. Solr is introduced as an enterprise search platform built on Lucene that provides features like faceting, scalability and real-time indexing. The document concludes with examples of how Lucene and Solr are used in applications and websites for search, analytics, auto-suggestion and more.
![Analysis Process - Tokenizer
WhitespaceAnalyzer
Simplest built-in analyzer
The quick brown fox jumps over the lazy dog.
[The] [quick] [brown] [fox] [jumps] [over] [the] [lazy] [dog.]
Tokens](https://image.slidesharecdn.com/introductiontoapachelucenesolr-140412130758-phpapp02/85/Introduction-to-Apache-Lucene-Solr-12-320.jpg)
![Analysis Process - Tokenizer
SimpleAnalyzer
Lowercases, split at non-letter boundaries
The quick brown fox jumps over the lazy dog.
[the] [quick] [brown] [fox] [jumps] [over] [the] [lazy] [dog]
Tokens](https://image.slidesharecdn.com/introductiontoapachelucenesolr-140412130758-phpapp02/85/Introduction-to-Apache-Lucene-Solr-13-320.jpg)
![Query Types
• Single and multi term queries
• ex fieldname:value or title: software engineer
• +, -, AND, OR NOT operators.
• ex. title: (software AND engineer)
• Range queries on date or numeric fields,
• ex: timestamp: [ * TO NOW ] or price: [ 1 TO 100 ]
• Boost queries:
• e.g. title:Engineer ^1.5 OR text:Engineer
• Fuzzy search : is a search for words that are similar in
spelling
• e.g. roam~0.8 => noam
• Proximity Search : with a sloppy phrase query. The
close together the two terms appear, higher the score.
• ex “apache lucene”~20 : will look for all documents where
“apache” word occurs within 20 words of “lucene”
23](https://image.slidesharecdn.com/introductiontoapachelucenesolr-140412130758-phpapp02/85/Introduction-to-Apache-Lucene-Solr-23-320.jpg)
![Faceting
Source: www.career9.com, www.indeed.com 27
• Grouping results based on field
value
• Facet on: field
terms, queries, date ranges
• &facet=on
&facet.field=job_title
&facet.query=salary:[30000 TO
100000]
• http://wiki.apache.org/solr/Sim
pleFacetParameters](https://image.slidesharecdn.com/introductiontoapachelucenesolr-140412130758-phpapp02/85/Introduction-to-Apache-Lucene-Solr-27-320.jpg)
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Introduction to Apache Lucene/Solr
- 1. Introduction to Apache Lucene/Solr April 2014 HDSG Meetup Rahul Jain @rahuldausa
- 2. Who am I? Software Engineer @ IVY Comptech, Hyderabad 7 years of programming learning experience Built a platform to search logs in Near real time with volume of 1TB/day# Worked on a Solr search based SEO/SEM software with 40 billion records/month (Topic of next talk?) Areas of expertise/interest High traffic web applications JAVA/J2EE Big data, NoSQL Information-Retrieval, Machine learning 2# http://www.slideshare.net/lucenerevolution/building-a-near-real-time-search-engine-analytics-for-logs-using-solr
- 3. Agenda • IR Overview • Basic Concepts • Lucene • Solr • Use-cases • Solr In Action (demo) • Q&A 3
- 4. Information Retrieval (IR) ”Information retrieval is the activity of obtaining information resources (in the form of documents) relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text (or other content-based) indexing” - Wikipedia 4
- 5. Basic Concepts • tf (t in d) : term frequency in a document • measure of how often a term appears in the document • the number of times term t appears in the currently scored document d • idf (t) : inverse document frequency • measure of whether the term is common or rare across all documents, i.e. how often the term appears across the index • obtained by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of that quotient. • boost (index) : boost of the field at index-time • boost (query) : boost of the field at query-time 5
- 6. Basic Concepts TF - IDF TF - IDF = Term Frequency X Inverse Document Frequency Credit: http://http://whatisgraphsearch.com/
- 8. Apache Lucene • Fast, high performance, scalable search/IR library • Open source • Initially developed by Doug Cutting (Also author of Hadoop) • Indexing and Searching • Inverted Index of documents • Provides advanced Search options like synonyms, stopwords, based on similarity, proximity. • http://lucene.apache.org/ 8
- 9. Lucene Internals - Inverted Index Credit: https://developer.apple.com/library/mac/documentation/userexperience/conceptual/SearchKitConcepts/searchKit_basics/searchKit_basics.html 9
- 10. Lucene Internals (Contd.) • Defines documents Model • Index contains documents. • Each document consist of fields. • Each Field has attributes. – What is the data type (FieldType) – How to handle the content (Analyzers, Filters) – Is it a stored field (stored="true") or Index field (indexed="true") 10
- 11. Indexing Pipeline • Analyzer : create tokens using a Tokenizer and/or applying Filters (Token Filters) • Each field can define an Analyzer at index time/query time or the both at same time. Credit : http://www.slideshare.net/otisg/lucene-introduction 11
- 12. Analysis Process - Tokenizer WhitespaceAnalyzer Simplest built-in analyzer The quick brown fox jumps over the lazy dog. [The] [quick] [brown] [fox] [jumps] [over] [the] [lazy] [dog.] Tokens
- 13. Analysis Process - Tokenizer SimpleAnalyzer Lowercases, split at non-letter boundaries The quick brown fox jumps over the lazy dog. [the] [quick] [brown] [fox] [jumps] [over] [the] [lazy] [dog] Tokens
- 14. Apache Solr 14
- 15. Apache Solr • Created by Yonik Seeley for CNET • Enterprise Search platform for Apache Lucene • Open source • Highly reliable, scalable, fault tolerant • Support distributed Indexing (SolrCloud), Replication, and load balanced querying • http://lucene.apache.org/solr 15
- 16. High level overview Source: http://www.slideshare.net/erikhatcher/solr-search-at-the-speed-of-light
- 17. Apache Solr - Features • full-text search • faceted search (similar to GroupBy clause in RDBMS) • scalability – caching – replication – distributed search • near real-time indexing • geospatial search • and many more : highlighting, database integration, rich document (e.g., Word, PDF) handling 17
- 18. How to start It’s very Easy. 1. Start Solr java -jar start.jar 2. Index your data java -jar post.jar *.xml 3. Search http://localhost:8983/solr
- 19. Solr APIs • HTTP GET/POST • JSON/XML • Clients – SolrJ (embedded or HTTP) – solr-ruby – python, PHP, solrsharp
- 20. Solr – schema.xml • Types with index and query Analyzers - similar to data type • Fields with name, type and options • Unique Key : Unique Identifier of a document. For e.g. “id” • Dynamic Fields : Dynamic fields allow Solr to index fields that you did not explicitly define in your schema. For e.g. fieldName: *_i or *_txts • Copy Fields : Solr has a mechanism for making copies of fields so that you can apply several distinct field types to a single piece of incoming information. field ‘a‘ populates field ‘b’ with its value before tokenizing (having different analyzer/filter). 20
- 21. Solr – Content Analysis • Field Attributes Name : Name of the field Type : Data-type (FieldType) of the field Indexed : Should it be indexed (indexed="true/false") Stored : Should it be stored (stored="true/false") Required : is it a mandatory field (required="true/false") Multi-Valued : Would it will contains multiple values e.g. text: pizza, food (multiValued="true/false") e.g. <field name="id" type="string" indexed="true" stored="true" required="true" multiValued="false" /> 21
- 22. Solr – solrconfig.xml • Data dir: where all index data will be stored • Index configuration • Cache configurations • Request Handler configuration • Search components, response writers, query parsers 22
- 23. Query Types • Single and multi term queries • ex fieldname:value or title: software engineer • +, -, AND, OR NOT operators. • ex. title: (software AND engineer) • Range queries on date or numeric fields, • ex: timestamp: [ * TO NOW ] or price: [ 1 TO 100 ] • Boost queries: • e.g. title:Engineer ^1.5 OR text:Engineer • Fuzzy search : is a search for words that are similar in spelling • e.g. roam~0.8 => noam • Proximity Search : with a sloppy phrase query. The close together the two terms appear, higher the score. • ex “apache lucene”~20 : will look for all documents where “apache” word occurs within 20 words of “lucene” 23
- 24. Solr/Lucene Use-cases • Search • Analytics • NoSQL datastore • Auto-suggestion / Auto-correction • Recommendation Engine (MoreLikeThis) • Relevancy Engine (Feedback to other applications) • Solr as a White-List • GeoSpatial based Search 24
- 25. Search • Application – Eclipse, Hibernate search • E-Commerce : – Flipkart.com, Infibeam.com, Buy.com, Netflix.com, ebay.com • Jobs – Indeed.com, Simplyhired.com, Naukri.com • Auto – AOL.com • Travel – Cleartrip.com • Social Network – Twitter.com, LinkedIn.com, mylife.com 25 Source: http://www.quora.com/Which-major-companies-are-using-Solr-for-search
- 26. Search (Contd.) • Search Engine – Yandex.ru, DuckDuckGo.com • News Paper – Guardian.co.uk • Music/Movies – Apple.com, Netflix.com • Events – Stubhub.com, Eventbrite.com • Cloud Log Management – Loggly.com • Others – Whitehouse.gov 26
- 27. Faceting Source: www.career9.com, www.indeed.com 27 • Grouping results based on field value • Facet on: field terms, queries, date ranges • &facet=on &facet.field=job_title &facet.query=salary:[30000 TO 100000] • http://wiki.apache.org/solr/Sim pleFacetParameters
- 28. Analytics Analytics source : Kibana.org based on ElasticSearch and Logstash Image Source : http://semicomplete.com/presentations/logstash-monitorama-2013/#/8 28
- 29. Autosuggestion Source: www.drupal.org , www.yelp.com 29
- 30. Integration • Clustering (Solr-Carrot2) • Named Entity extraction (Solr-UIMA) • SolrCloud (Solr-Zookeeper) • Parsing of many Different File Formats (Solr-Tika) • Machine Learning/Data Mining (Apache Mahout) • Large scale Indexing (Hadoop) 30
- 31. References • http://en.wikipedia.org/wiki/Tf%E2%80%93idf • http://lucene.apache.org/core/4_5_0/core/org/apache/lucene/search/similarities /TFIDFSimilarity.html • http://www.quora.com/Which-major-companies-are-using-Solr-for-search • http://marc.info/?l=solr-user&m=137271228610366&w=2 • http://java.dzone.com/articles/apache-solr-get-started-get 31
- 32. Solr/Lucene Meetup • Building Big Data Analytics Platforms using Elasticsearch (Kibana) • Saturday, April 19, 2014 10:00 AM • IIIT Hyderabad • URL: http://www.meetup.com/Hyderabad-Apache-Solr-Lucene-Group/events/150134392/ OR • Search on Google …
- 33. Thanks! @rahuldausa on twitter and slideshare http://www.linkedin.com/in/rahuldausa Find Interesting ? Join us @ http://www.meetup.com/Hyderabad-Apache-Solr-Lucene-Group/ 33