| PostgreSQL 9.3.25 Documentation | ||||
|---|---|---|---|---|
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PostgreSQL provides several index types: B-tree, Hash, GiST, SP-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:
| < |
| <= |
| = |
| >= |
| > |
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:
| << |
| &< |
| &> |
| >> |
| <<| |
| &<| |
| |&> |
| |>> |
| @> |
| <@ |
| ~= |
| && |
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.
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:
| << |
| >> |
| ~= |
| <@ |
| <^ |
| >^ |
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 PostgreSQL includes GIN operator classes for one-dimensional arrays, which support indexed queries using these operators:
| <@ |
| @> |
| = |
| && |