## 11.2. Index Types

[11.2.1. B-Tree](indexes-types.html#INDEXES-TYPES-BTREE)

[11.2.2. Hash](indexes-types.html#INDEXES-TYPES-HASH)

[11.2.3. GiST](indexes-types.html#INDEXES-TYPE-GIST)

[11.2.4. SP-GiST](indexes-types.html#INDEXES-TYPE-SPGIST)

[11.2.5. GIN](indexes-types.html#INDEXES-TYPES-GIN)

[11.2.6. BRIN](indexes-types.html#INDEXES-TYPES-BRIN)

PostgreSQL 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`](sql-createindex.html) command creates B-tree indexes, which fit the most common situations. The other index types are selected by writing the keyword `USING` followed by the index type name. For example, to create a Hash index:

```
CREATE INDEX name ON table USING HASH (column);

```

### 11.2.1. B-Tree

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 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](indexes-opclass.html) 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.

### 11.2.2. Hash

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 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:

```
=

```

### 11.2.3. GiST

[]()[]()

 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](functions-geometry.html) for the meaning of these operators.) The GiST operator classes included in the standard distribution are documented in [Table 65.1](gist-builtin-opclasses.html#GIST-BUILTIN-OPCLASSES-TABLE). Many other GiST operator classes are available in the `contrib` collection or as separate projects. For more information see [Chapter 65](gist.html).

 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 65.1](gist-builtin-opclasses.html#GIST-BUILTIN-OPCLASSES-TABLE), operators that can be used in this way are listed in the column “Ordering Operators”.

### 11.2.4. SP-GiST

[]()[]()

 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](functions-geometry.html) for the meaning of these operators.) The SP-GiST operator classes included in the standard distribution are documented in [Table 66.1](spgist-builtin-opclasses.html#SPGIST-BUILTIN-OPCLASSES-TABLE). For more information see [Chapter 66](spgist.html).

 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 66.1](spgist-builtin-opclasses.html#SPGIST-BUILTIN-OPCLASSES-TABLE).

### 11.2.5. GIN

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 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](functions-array.html) for the meaning of these operators.) The GIN operator classes included in the standard distribution are documented in [Table 67.1](gin-builtin-opclasses.html#GIN-BUILTIN-OPCLASSES-TABLE). Many other GIN operator classes are available in the `contrib` collection or as separate projects. For more information see [Chapter 67](gin.html).

### 11.2.6. BRIN

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 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 68.1](brin-builtin-opclasses.html#BRIN-BUILTIN-OPCLASSES-TABLE). For more information see [Chapter 68](brin.html).

