## 11.2. Index Types

Postgres Pro provides several index types: B-tree, Hash, GiST, SP-GiST, GIN and BRIN. 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 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 11.9, “Operator Classes and Operator Families” 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 Postgres Pro includes GiST operator classes for several two-dimensional geometric data types, which support indexed queries using these operators:

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

(See Section 9.11, “Geometric Functions and Operators” for the meaning of these operators.) The GiST operator classes included in the standard distribution are documented in Table 59.1, “Built-in GiST Operator Classes”. Many other GiST operator classes are available in the `contrib` collection or as separate projects. For more information see Chapter 59, GiST Indexes.

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 59.1, “Built-in GiST Operator Classes”, 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, “Geometric Functions and Operators” for the meaning of these operators.) The SP-GiST operator classes included in the standard distribution are documented in Table 60.1, “Built-in SP-GiST Operator Classes”. For more information see Chapter 60, SP-GiST Indexes.

GIN indexes are inverted indexes which can handle values that contain more than one key, arrays for example. 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 GIN operator classes for one-dimensional arrays, which support indexed queries using these operators:

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

(See Section 9.18, “Array Functions and Operators” for the meaning of these operators.) The GIN operator classes included in the standard distribution are documented in Table 61.1, “Built-in GIN Operator Classes”. Many other GIN operator classes are available in the `contrib` collection or as separate projects. For more information see Chapter 61, GIN Indexes.

BRIN indexes (a shorthand for Block Range indexes) store summaries about the values stored in consecutive table physical block ranges. 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, which support indexed queries using these operators:

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

The BRIN operator classes included in the standard distribution are documented in Table 62.1, “Built-in BRIN Operator Classes”. For more information see Chapter 62, BRIN Indexes.