It is a standalone search engine, meant to provide fast, size-efficient and relevant fulltext search functions to other applications. Sphinx was specially designed to integrate well with SQL databases and scripting languages. Currently built-in data sources support fetching data either via direct connection to MySQL or PostgreSQL, or using XML pipe mechanism (a pipe to indexer in special XML-based format which Sphinx recognizes). Official APIs for PHP, Python, Java, Ruby, pure C are included in Sphinx distribution.<BR><BR>
  Its feature includes:<BR>
  <ul type="disc"><li>high indexing speed (upto 10 MB/sec on modern CPUs);</li><li>high search speed (avg query is under 0.1 sec on 2-4 GB text collections);</li><li>high scalability (upto 100 GB of text, upto 100 M documents on a single CPU);</li><li>provides good relevance ranking through combination of phrase proximity ranking and statistical (BM25) ranking;</li><li>provides distributed searching capabilities;</li><li>provides document exceprts generation;</li><li>provides searching from within MySQL through pluggable storage engine;</li><li>supports boolean, phrase, and word proximity queries;</li><li>supports multiple full-text fields per document (upto 32 by default);</li><li>supports multiple additional attributes per document (ie. groups, timestamps, etc);</li><li>supports stopwords;</li><li>supports both single-byte encodings and UTF-8;</li><li>supports English stemming, Russian stemming, and Soundex for morphology;</li><li>supports MySQL natively (MyISAM and InnoDB tables are both supported);</li><li>supports PostgreSQL natively.</li></ul>

Sphinix is free open-source SQL full-text search engine. How do you implement full-text search for that 10+ million row table, keep up with the load, and stay relevant? Sphinx is good at those kinds of riddles.

Sphinix - Search server

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