Intro talk for UNC School of Information and Library Science. Covers basics of Lucene and Solr as well as info on Lucene/Solr jobs, opportunities, etc.
2. The How Many Game How many of you: Have taken a class in Information Retrieval (IR)? Are doing work/research in IR? Have heard of or are using Lucene? Have heard of or are using Solr? Are doing work on core IR algorithms such as compression techniques or scoring? Are doing UI/Application work/research as they relate to search?
3. Topics Brief Bio Search 101 (skip?) What is: Apache Lucene Apache Solr What can they do? Features and functionality Intangibles What’s new in Lucene and Solr? How can they help my research/work/____?
4. Brief Bio Apache Lucene/Solr Committer Apache Mahout co-founder Scalable Machine Learning Co-founder of Lucid Imagination http://www.lucidimagination.com Previously worked at Center for Natural Lang. Processing at Syracuse Univ. with Dr. Liddy Co-Author of upcoming “Taming Text” (Manning Publications) http://www.manning.com/ingersoll
5. Search 101 Search tools are designed for dealing with fuzzy data/questions Works well with structured and unstructured data Performs well when dealing with large volumes of data Many apps don’t need the limits that databases place on content Search fits well alongside a DB too Given a user’s information need, (query) find and, optionally, score content relevant to that need Many different ways to solve this problem, each with tradeoffs What’s “relevant” mean?
6. Vector Space Model (VSM) for relevance Common across many search engines Apache Lucene is a highly optimized implementation of the VSM Search 101 Relevance Indexing Finds and maps terms and documents Conceptually similar to a book index At the heart of fast search/retrieve
7. Apache Lucene in a Nutshell http://lucene.apache.org/java Java based Application Programming Interface (API) for adding search and indexing functionality to applications Fast and efficient scoring and indexing algorithms Lots of contributions to make common tasks easier: Highlighting, spatial, Query Parsers, Benchmarking tools, etc. Most widely deployed search library on the planet
8. Lucene Basics Content is modeled via Documents and Fields Content can be text, integers, floats, dates, custom Analysis can be employed to alter content before indexing Searches are supported through a wide range of Query options Keyword Terms Phrases Wildcards Many, many more
9. Apache Solr in a Nutshell http://lucene.apache.org/solr Lucene-based Search Server + other features and functionality Access Lucene over HTTP: Java, XML, Ruby, Python, .NET, JSON, PHP, etc. Most programming tasks in Lucene are configuration tasks in Solr Faceting (guided navigation, filters, etc.) Replication and distributed search support Lucene Best Practices
12. Quick Solr/Lucene Demo Pre-reqs: Apache Ant 1.7.x, Subversion (SVN) Command Line 1: svn co https://svn.apache.org/repos/asf/lucene/dev/trunksolr-trunk cdsolr-trunk/solr/ ant example cd example java –Dsolr.clustering.enabled=true –jar start.jar Command Line 2 cd exampledocs; java –jar post.jar *.xml http://localhost:8983/solr/browse?q=&debugQuery=true&annotateBrowse=true
13. Other Features Data Import Handler Database, Mail, RSS, etc. Rich document support via Apache Tika PDF, MS Office, Images, etc. Replication for high query volume Distributed search for large indexes Production systems with 1B+ documents Configurable Analysis chain and other extension points Total control over tokenization, stemming, etc.
14. Intangibles Open Source Flexible, non-restrictive license Apache License v2 – non-viral “Do what you want with the software, just don’t claim you wrote it” Large community willing to help Great place to learn about real world IR systems Many books and other documentation Lucene in Action by Hatcher, McCandless and Gospodnetic
15. What’s New? https://svn.apache.org/repos/asf/lucene/dev/trunk/lucene/CHANGES.txt https://svn.apache.org/repos/asf/lucene/dev/trunk/solr/CHANGES.txt Codecs Pluggable Index Formats Provide Different index compression techniques Stats to enable alternate scoring approaches BM25, Lang. Modeling, etc. -- More work to be done here Faster Java Strings are slow; convert to use byte arrays
16. Other New Items Many new Analyzers (tokenizers, etc.) Richer Language support (Hindi, Indonesian, Arabic, …) Richer Geospatial (Local) Search capabilities Score, filter, sort by distance http://wiki.apache.org/solr/SpatialSearch Results Grouping Group Related Results http://wiki.apache.org/solr/FieldCollapsing More Faceting Capabilities Pivot New underlying algorithms
19. Other Things that Can Help Nutch Crawling http://nutch.apache.org Mahout Machine learning (clustering, classification, others) http://mahout.apache.org OpenNLP Part of Speech, Parsers, Named Entity Recognition http://incubator.apache.org/opennlp Open Relevance Project Relevance Judgments http://lucene.apache.org/openrelevance