B-trees can handle equality and range queries on data that can be sorted into some ordering. In particular, the Postgres Pro 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.

B-tree indexes are also capable of optimizing “nearest-neighbor” searches, such as

SELECT * FROM events ORDER BY event_date <-> date '2017-05-05' LIMIT 10;

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

Hash indexes store a 32-bit hash code derived from the value of the indexed column. Hence, such 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 equal operator:

=

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 Postgres Pro 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 Postgres Pro 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 listed 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 Postgres Pro 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. Thus, they are most effective for columns whose values are well-correlated with the physical order of the table rows. 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.