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PostgreSQL provides several index types: B-tree, R-tree, GiST, and Hash. Each index type uses a different algorithm that is best suited to different types of queries. By default, the CREATE INDEX command will create a B-tree index, which fits the most common situations. B-trees can handle equality and range queries on data that can be sorted into some ordering. In particular, the PostgreSQL query planner will consider using a B-tree index whenever an indexed column is involved in a comparison using one of these operators: <, <=, =, >=, >
The optimizer can also use a B-tree index for queries involving the pattern matching operators LIKE, ILIKE, ~, and ~*, if the pattern is anchored to the beginning of the string, e.g., col LIKE 'foo%' or col ~ '^foo', but not col LIKE '%bar'. However, if your server does not use the C locale you will need to create the index with a special operator class. See Section 11.6 below.
R-tree indexes are suited for queries on spatial data. To create an R-tree index, use a command of the form
CREATE INDEX name ON table USING RTREE (column);
The PostgreSQL query planner will consider using an R-tree index whenever an indexed column is involved in a comparison using one of these operators: <<, &<, &>, >>, @, ~=, && (Refer to Section 9.9 about the meaning of these operators.)
Hash indexes can only handle simple equality comparisons. The query planner will consider using a hash index whenever an indexed column is involved in a comparison using the = operator. The following command is used to create a hash index:
CREATE INDEX name ON table USING HASH (column);
Note: Testing has shown PostgreSQL's hash indexes to perform no better than B-tree indexes, and the index size and build time for hash indexes is much worse. For these reasons, hash index use is presently discouraged.
The B-tree index method is an implementation of Lehman-Yao high-concurrency B-trees. The R-tree index method implements standard R-trees using Guttman's quadratic split algorithm. The hash index method is an implementation of Litwin's linear hashing. We mention the algorithms used solely to indicate that all of these index methods are fully dynamic and do not have to be optimized periodically (as is the case with, for example, static hash methods).