Full Text Search

Full Text Search refers to techniques for searching a single computer-stored document or a collection in a full text database. Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases.

Full-Text queries perform linguistic searches against text data in full-text indexes by operating on words and phrases based on rules of a particular language such as English or Japanese. Full-text queries can include simple words and phrases or multiple forms of a word or phrase.

Full Text Search is applicable in a wide range of business scenarios such as e-businesses—searching for items on a web site; law firms—searching for case histories in a legal-data repository; or human resources departments—matching job descriptions with stored resumes. The basic administrative and development tasks of full-text search are equivalent regardless of business scenarios. However, in a given business scenario, full-text index and queries can be honed to meet business goals. For example, for an e-business maximizing performance might be more important than ranking of results, recall accuracy (how many of the existing matches are actually returned by a full-text query), or supporting multiple languages. For a law firm, returning every possible hit (total recall of information) might be the most important consideration.

When it comes to big data search, your primary choices are between Solr and Elasticsearch. Both open source enterprise search platforms have the ability to perform full-text searches, faceted searches, and other similar requests not realistically handled by a typical Google search. SearchBlox uses Elasticsearch as its primary search engine for fast results that are easy to manage. The choice to use Elasticsearch rather than Solr comes from features such as distributed searches and improved scale.

Solr or Elasticsearch have a nice set of features, such as:

  • Hit highlighting
  • Faceted search
  • Full-text search
  • Database integration
  • Rich document handling
  • Dynamic clustering
  • Distributed search and index replication
  • Highlighting
  • Geo-spatial search

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