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What is a hash index?
A database hash index applies a hash function to an indexed key, then uses the result to locate a bucket containing candidate index entries. Different keys can produce the same bucket, an ordinary occurrence called a collision. The index must handle collisions—for example, with chains or overflow pages—so the structure does not guarantee constant-time lookups in every workload.
Hashing does not preserve the order of keys. That makes a hash index a specialized choice for equality predicates rather than a general replacement for an ordered index.
When is a hash index useful?
Consider one when the workload repeatedly searches for a complete key using equality and does not need the index to support ranges or sorting. The relevant operation varies by database: PostgreSQL documents support for =; MySQL documents hash-index use for = and the null-safe equality operator <=>; SQL Server hash seeks require all columns of the key.
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Choose an ordered index instead when queries need range comparisons, ordered results, or other operations the hash index cannot support. An index also adds maintenance and storage overhead, so assess the actual queries and data rather than assuming hashing will be faster. PostgreSQL’s overview of index use discusses this broader trade-off: PostgreSQL 18: Indexes.
How hash indexes differ by database
“Hash index” does not describe one portable feature. Check the engine, version, and storage model before applying implementation advice.
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| Database and version | Where it applies | Important behavior and limits |
|---|---|---|
| PostgreSQL 17 | Persistent, on-disk hash indexes | Supports equality only; indexes one column and cannot enforce uniqueness. Each index tuple stores a 4-byte hash value rather than the indexed value, which can make the index smaller for longer values. Scans are lossy and require checking table rows to confirm matches. Crowded buckets can use overflow pages; when growth adds a bucket, an existing bucket is split in the foreground, which can increase insert latency. |
| MySQL 8.4 | The cited manual comparison describes hash indexes in the context of MEMORY tables; do not assume user-created hash indexes are interchangeable across all storage engines. | Supports = and <=>, not range comparisons such as <, and cannot support ORDER BY. Converting a MyISAM or InnoDB table to a hash-indexed MEMORY table can change optimizer estimates and query choices. |
| SQL Server | Only on memory-optimized tables | Bucket count is set at creation and can be changed by rebuilding. Too few buckets increase collisions and chain length; too many leave empty buckets that consume memory and can hurt full index scans. A hash seek requires every key column; inequalities and incomplete composite-key predicates are poor fits. |
Sources: PostgreSQL 17: Hash Indexes, MySQL 8.4: Comparison of B-Tree and Hash Indexes, and Microsoft’s SQL Server index design guide.
What are the limitations of hash indexes?
- No key ordering: Because hashing does not preserve order, a hash index is not a fit for range searches or satisfying an
ORDER BYrequirement. - Collisions affect lookup work: Multiple keys may land in the same bucket. Distribution, bucket capacity, and the engine’s chain or overflow strategy influence the resulting work.
- Engine-specific restrictions: PostgreSQL hash indexes are single-column and do not enforce uniqueness. SQL Server hash indexes require a complete-key seek and are restricted to memory-optimized tables. MySQL’s documented hash-index comparison concerns MEMORY tables.
- Maintenance has costs: Indexes consume resources and add work when data changes. In PostgreSQL, foreground bucket splitting during growth can affect insert latency; in SQL Server, bucket sizing trades collision costs against memory and scan overhead.
- Not automatically faster: The query operator, key distribution, table size, update rate, and storage model all matter. A hash index should be evaluated against the workload it is intended to serve.
How to decide whether one fits
- Check the predicate: Confirm that the important query uses the equality operator supported by the engine. If it needs a range, ordering, or only part of a composite key, a hash index may not help.
- Check the engine and storage model: Verify the exact database version and, for MySQL, the table’s storage engine. Do not transfer one database’s hash-index guarantees to another.
- Check key requirements: Determine whether the index must enforce uniqueness or cover multiple columns. PostgreSQL hash indexes cannot enforce uniqueness and index only one column; SQL Server hash seeks need the complete key.
- Assess size and data changes: Consider distinct key count, collision behavior, expected growth, insert and update rates, and memory or index-size limits. For SQL Server, Microsoft says bucket counts are often set at one to two times the distinct key count, while performance is commonly still good within ten times the actual count; this is sizing guidance, not a universal optimum.
- Validate with representative queries: Compare query plans and workload behavior using production-like data. Keep the index only if it serves the target queries without an unacceptable storage or write cost.
For PostgreSQL, MySQL, and SQL Server, the linked manuals document different supported operators and implementation constraints; none establishes a universal performance ranking between hash and ordered indexes.
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