While many Lucene/Solr applications will never outgrow a single, well-configured machine, the fact is, more and more applications are pushing beyond the single machine limit due to either index size or query volume. In discussing Lucene and Solr best practices for performance and scaling, Mark Miller explains how to get the most out of a single machine, as well as how to scale out to harness multiple machines to handle large indexes, large query volume, or both.
The idea is to use the RDF data model and a triples store to populate the Solr schema index. We leverage the powerful and flexible data representation framework (RDF), in conjunction with the piece of software that lets you do whatever you want with that data (Virtuoso), to feed a carefully tailored Solr schema index to optimally perform three things: full-text search, aggregates and filtering. Also, we want to leverage the ontologies used to describe this data to be able to infer things vis-à-vis these indexed resources in Solr. This leverage enables us to use inference on full-text search, aggregates and filtering, in Solr! This is quite important since you will be able to perform full text searches, filtered by types that are inferred!
Zend_Search_Lucene is a general purpose text search engine written entirely in PHP 5. Since it stores its index on the filesystem and does not require a database server, it can add search capabilities to almost any PHP-driven website. Zend_Search_Lucene supports the following features:
Ranked searching – best results returned first
Many powerful query types: phrase queries, boolean queries, wildcard queries, proximity queries, range queries and many others.
Search by specific field (e.g., title, author, contents)
Zend_Search_Lucene was derived from the Apache Lucene project. The currently (starting from ZF 1.6) supported Lucene index format versions are 1.4 – 2.3. For more information on Lucene, visit http://lucene.apache.org/java/docs/.
Mahout's goal is to build scalable, Apache licensed machine learning libraries. Initially, we are interested in building out the ten machine learning libraries detailed in http://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf using Hadoop. While these algorithms are our initial focus, we welcome contributions of other machine learning approaches.
We're the commercial entity exclusively dedicated to Apache Lucene/Solr open source search technology. Our products and services can help you develop and deploy search solutions with confidence: professional training, SLA-based support subscriptions; best practices consulting; free certified distributions of Lucene and Solr; and value-add software.
Public Websites using Solr
Although Lucene provides the ability to create your own queries through its API, it also provides a rich query language through the Query Parser, a lexer which interprets a string into a Lucene Query using JavaCC.
This page provides the Query Parser syntax in Lucene 1.9. If you are using a different version of Lucene, please consult the copy of docs/queryparsersyntax.html that was distributed with the version you are using.