PostgreSQL 9.1.9 Documentation
Prev | Up | Chapter 11. Indexes | Next

# 11.2. Index Types

PostgreSQL provides several index types: B-tree, Hash, GiST and GIN. Each index type uses a different algorithm that is best suited to different types of queries. By default, the `CREATE INDEX` command creates B-tree indexes, which fit 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:

`<`
`<=`
`=`
`>=`
`>`

Constructs equivalent to combinations of these operators, such as `BETWEEN` and `IN`, can also be implemented with a B-tree index search. Also, an `IS NULL` or `IS NOT NULL` condition on an index column can be used with a B-tree index.

The optimizer can also use a B-tree index for queries involving the pattern matching operators `LIKE` and `~` if the pattern is a constant and is anchored to the beginning of the string — for example, `col LIKE 'foo%'` or `col ~ '^foo'`, but not `col LIKE '%bar'`. However, if your database does not use the C locale you will need to create the index with a special operator class to support indexing of pattern-matching queries; see Section 11.9 below. It is also possible to use B-tree indexes for `ILIKE` and `~*`, but only if the pattern starts with non-alphabetic characters, i.e., characters that are not affected by upper/lower case conversion.

B-tree indexes can also be used to retrieve data in sorted order. This is not always faster than a simple scan and sort, but it is often helpful.

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);

Caution

Hash index operations are not presently WAL-logged, so hash indexes might need to be rebuilt with `REINDEX` after a database crash if there were unwritten changes. Also, changes to hash indexes are not replicated over streaming or file-based replication after the initial base backup, so they give wrong answers to queries that subsequently use them. For these reasons, hash index use is presently discouraged.

GiST indexes are not a single kind of index, but rather an infrastructure within which many different indexing strategies can be implemented. Accordingly, the particular operators with which a GiST index can be used vary depending on the indexing strategy (the operator class). As an example, the standard distribution of PostgreSQL includes GiST operator classes for several two-dimensional geometric data types, which support indexed queries using these operators:

`<<`
`&<`
`&>`
`>>`
`<<|`
`&<|`
`|&>`
`|>>`
`@>`
`<@`
`~=`
`&&`

(See Section 9.11 for the meaning of these operators.) Many other GiST operator classes are available in the `contrib` collection or as separate projects. For more information see Chapter 53.

GiST indexes are also capable of optimizing "nearest-neighbor" searches, such as

    SELECT * FROM places ORDER BY location <-> point '(101,456)' LIMIT 10;

which finds the ten places closest to a given target point. The ability to do this is again dependent on the particular operator class being used.

GIN indexes are inverted indexes which can handle values that contain more than one key, arrays for example. Like GiST, GIN can support many different user-defined indexing strategies and the particular operators with which a GIN index can be used vary depending on the indexing strategy. As an example, the standard distribution of PostgreSQL includes GIN operator classes for one-dimensional arrays, which support indexed queries using these operators:

`<@`
`@>`
`=`
`&&`

(See Section 9.17 for the meaning of these operators.) Many other GIN operator classes are available in the `contrib` collection or as separate projects. For more information see Chapter 54.

Prev | Home | Next
Introduction | Up | Multicolumn Indexes