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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIn PostgreSQL, a B-tree is the general-purpose default index: it supports equality and range searches and can return rows in sorted order. A hash index is a narrower option for equality comparisons. A covering index is not a third index method; it is an index designed to contain the columns a query needs, potentially enabling an index-only scan.
This guide uses PostgreSQL 18 as its reference. Index names and capabilities vary among database engines, so these descriptions should not be assumed to apply unchanged to other systems. PostgreSQL summarizes the central idea this way: “Each index type uses a different algorithm that is best suited to different types of indexable clauses.” (PostgreSQL 17 documentation)
What a database index does
An index is an auxiliary data structure that helps a database find table rows without scanning every row for each query. The index method determines which kinds of conditions it can support and whether it can help provide results in a useful order.
In PostgreSQL, CREATE INDEX creates a B-tree index unless you specify another method. That makes B-tree the starting point for many ordinary lookup and ordering needs, not a guarantee that every query will use an index.
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How B-tree indexes work in PostgreSQL
A PostgreSQL B-tree can support equality and range comparisons on sortable data. Common supported operators include =, <, <=, >=, and >; related conditions include BETWEEN and IN. B-trees can also return rows in index order, which can help when a query requests sorted results.
A B-tree may support a pattern such as LIKE 'foo%' under the relevant collation and operator-class conditions. That does not mean it generally supports a leading-wildcard search such as LIKE '%bar'.
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Choose a B-tree when a query needs equality or range filtering, sorted retrieval, or a combination of these. It is the broad default, but the right index still depends on the query and data.
How PostgreSQL hash indexes differ
A PostgreSQL hash index stores a 32-bit hash code derived from the indexed value and is considered for simple equality comparisons using =. It does not provide B-tree’s range-search or ordered-retrieval capabilities.
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That narrower role does not establish that a hash index is universally faster for equality searches. PostgreSQL’s documentation does not give a universal performance ranking between hash and B-tree; whether either helps depends on the workload and the database’s choice of plan.
What a covering index means
“Covering index” describes an index in relation to a query: the index contains the columns needed to search for matching rows and supply the query’s output. It is not a separate PostgreSQL index method. PostgreSQL can store extra, non-key columns with the INCLUDE clause.
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For example, if a query filters on x and returns y, an index can use x as its key and store y as payload:
CREATE INDEX tab_x_y ON tab (x) INCLUDE (y);This design matches a query such as:
SELECT y FROM tab WHERE x = 'key';The key column x helps locate matching entries. The included column y can supply the selected value, but it is not a search key: it cannot be used to qualify the index search. It also does not become part of a unique index’s uniqueness test. PostgreSQL 18 supports included columns with B-tree, GiST, and SP-GiST indexes (CREATE INDEX documentation).
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When a covering index can avoid a table lookup
If the index access method supports index-only scans and the index contains every column the query needs, PostgreSQL may answer the query from the index. But an index-only scan is not necessarily heap-free.
PostgreSQL stores row visibility information separately from index entries. To determine whether a row is visible to a transaction, it consults the visibility map for the relevant heap page. If that page is not marked all-visible, PostgreSQL must visit the heap row to check visibility. The table’s update activity and visibility-map state therefore affect whether a covering design actually avoids heap access (Index-Only Scans and Covering Indexes documentation).
How to choose among B-tree, hash, and covering designs
| Design | Useful for | Important limitation |
|---|---|---|
| B-tree | Equality and range conditions; retrieval in index order. | Does not make every predicate indexable; pattern matching depends on conditions such as collation and operator class. |
| Hash | Simple equality comparisons. | Does not provide B-tree’s range or ordered-retrieval support. |
| Covering design | Queries whose search keys and output columns can all be supplied from an index, potentially enabling an index-only scan. | Does not guarantee heap-free execution or a faster query; extra columns add index cost. |
When deciding whether to create or extend an index, assess the actual workload:
- Predicate: Does the query need equality alone, or range comparisons too?
- Ordering: Can returning rows in index order help the query?
- Selected columns: Would storing output columns let the index supply everything the query needs?
- Update activity: Does table churn make all-visible heap pages less likely, reducing the chance of avoiding heap visits?
- Index size and writes: Is the potential read benefit worth storing and maintaining additional data?
The costs of adding included columns
Included columns duplicate table data in the index. They make the index larger and may slow searches; PostgreSQL also warns that an index tuple exceeding the type’s maximum size can cause an insert to fail. Add payload columns only when they serve a relevant query, rather than treating a wider index as an automatic improvement (CREATE INDEX documentation).
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