Full Text Search

Some Info  An optional component  Much faster and complex than the previous version  Allow you to search for words and tokens in binary, char, nchar, varchar, nvarchar. XML, image, text, and varbinary  It build indexes on these columns  Select * from Authors where contains(bio, ‘Oregon’); Not  Select * from Authors where bio like ‘%Oregon%’

Comparing with Like  Full-text search Is magnitude faster Can search other data types than text Can use language features such as searching for ‘take’ gives you ‘took’, takes, and taken as well  However, like can Search middle of a word,  like ‘&puter’ Search for sequence  like ‘ab[c,d][1-9]%’ Faster than full text search with nonclustered index, if you search the entire column or beginning of the column

Few new features  48 languages  Noise lists, or Stop Words, (of, the, a etc.)  Failover support on mirrored database  Dynamic Management View supports troubleshoot  … …(see page 204)

Architecture  Three key elements Indexing Searching Filter  The column is index per words in the column, not the entire content of a record’s column  Searching uses the indexes  A filter is used if the column is not texted For example, a XML filter is used for XML data, a MS Word filter is used for MS Word data, etc

Word Breaker  A tool determine how to break words from sentences For example, FBI will match with F.B.I, but not for fbi. For example, UK word breaker will understand realise, realising, and realised; while the US breaker will understand realize, realizing, and realized

Search  Contains – more exact matches  FreeText – matches other forms mouse + mice  ContainsTable/FreeTextTable – returns results for ranking  Contains can be more powerful combining with FormsOf, Near, boolean operations, weighted, or * (a wildcard operator)

Hands on  First create the category  Then create indexes  Use the wizards