There are two kinds of indexes that can be used to speed up full text searches: GIN and GiST. Note that indexes are not mandatory for full text searching, but in cases where a column is searched on a regular basis, an index is usually desirable.
To create such an index, do one of:
CREATE INDEX name ON table USING GIN (column);
Creates a GIN (Generalized Inverted Index)-based index.
The column must be of tsvector type.
CREATE INDEX name ON table USING GIST (column [ { DEFAULT | tsvector_ops } (siglen = number) ] );
Creates a GiST (Generalized Search Tree)-based index.
The column can be of tsvector or
tsquery type.
Optional integer parameter siglen determines
signature length in bytes (see below for details).
GIN indexes are the preferred text search index type. As inverted
indexes, they contain an index entry for each word (lexeme), with a
compressed list of matching locations. Multi-word searches can find
the first match, then use the index to remove rows that are lacking
additional words. GIN indexes store only the words (lexemes) of
tsvector values, and not their weight labels. Thus a table
row recheck is needed when using a query that involves weights.
A GiST index is lossy, meaning that the index
might produce false matches, and it is necessary
to check the actual table row to eliminate such false matches.
(LightDB does this automatically when needed.)
GiST indexes are lossy because each document is represented in the
index by a fixed-length signature. The signature length in bytes is determined
by the value of the optional integer parameter siglen.
The default signature length (when siglen is not specified) is
124 bytes, the maximum signature length is 2024 bytes. The signature is generated by hashing
each word into a single bit in an n-bit string, with all these bits OR-ed
together to produce an n-bit document signature. When two words hash to
the same bit position there will be a false match. If all words in
the query have matches (real or false) then the table row must be
retrieved to see if the match is correct. Longer signatures lead to a more
precise search (scanning a smaller fraction of the index and fewer heap
pages), at the cost of a larger index.
A GiST index can be covering, i.e., use the INCLUDE
clause. Included columns can have data types without any GiST operator
class. Included attributes will be stored uncompressed.
Lossiness causes performance degradation due to unnecessary fetches of table records that turn out to be false matches. Since random access to table records is slow, this limits the usefulness of GiST indexes. The likelihood of false matches depends on several factors, in particular the number of unique words, so using dictionaries to reduce this number is recommended.
Note that GIN index build time can often be improved by increasing maintenance_work_mem, while GiST index build time is not sensitive to that parameter.
Partitioning of big collections and the proper use of GIN and GiST indexes allows the implementation of very fast searches with online update. Partitioning can be done at the database level using table inheritance, or by distributing documents over servers and collecting external search results, e.g., via Foreign Data access. The latter is possible because ranking functions use only local information.