11.2. Index Types

PostgreSQL provides several index types: B-tree, Hash, GiST, SP-GiST, GIN, BRIN, and the extension bloom . Each index type uses a different algorithm that is best suited to different types of indexable clauses. 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 11.10 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);

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.) The GiST operator classes included in the standard distribution are documented in Table 64.1 . Many other GiST operator classes are available in the contrib collection or as separate projects. For more information see Chapter 64 .

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. In Table 64.1 , operators that can be used in this way are listed in the column " Ordering Operators " .

SP-GiST indexes, like GiST indexes, offer an infrastructure that supports various kinds of searches. SP-GiST permits implementation of a wide range of different non-balanced disk-based data structures, such as quadtrees, k-d trees, and radix trees (tries). As an example, the standard distribution of PostgreSQL includes SP-GiST operator classes for two-dimensional points, which support indexed queries using these operators:

<<

>>

~=

<@

<^

>^

(See Section 9.11 for the meaning of these operators.) The SP-GiST operator classes included in the standard distribution are documented in Table 65.1 . For more information see Chapter 65 .

Like GiST, SP-GiST supports " nearest-neighbor " searches. For SP-GiST operator classes that support distance ordering, the corresponding operator is specified in the " Ordering Operators " column in Table 65.1 .

GIN indexes are " inverted indexes " which are appropriate for data values that contain multiple component values, such as arrays. An inverted index contains a separate entry for each component value, and can efficiently handle queries that test for the presence of specific component values.

Like GiST and SP-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 a GIN operator class for arrays, which supports indexed queries using these operators:

<@

@>

=

&&

(See Section 9.19 for the meaning of these operators.) The GIN operator classes included in the standard distribution are documented in Table 66.1 . Many other GIN operator classes are available in the contrib collection or as separate projects. For more information see Chapter 66 .

BRIN indexes (a shorthand for Block Range INdexes) store summaries about the values stored in consecutive physical block ranges of a table. Like GiST, SP-GiST and GIN, BRIN can support many different indexing strategies, and the particular operators with which a BRIN index can be used vary depending on the indexing strategy. For data types that have a linear sort order, the indexed data corresponds to the minimum and maximum values of the values in the column for each block range. This supports indexed queries using these operators:

<

<=

=

>=

>

The BRIN operator classes included in the standard distribution are documented in Table 67.1 . For more information see Chapter 67 .