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Start with the query’s search operation: use a B-tree for equality, ranges, and sorted results; use a hash index only for equality when your database and table model support it; and use a database full-text feature for word- and language-aware text search. The best choice depends on the database product, storage engine, and query semantics—not on a universal speed ranking.
Choose by the query you need to run
| Query need | Starting choice | Why |
|---|---|---|
Equality, range comparisons such as < or >=, BETWEEN, IN-style access, or sorted retrieval |
B-tree | Supports equality and ordered comparisons; it can also return rows in index order. It is the general-purpose starting point in PostgreSQL and is widely used in MySQL and SQL Server. PostgreSQL index types; MySQL index use; SQL Server indexes |
| Equality-only lookup | Hash, if supported for the target table and engine | Hash indexes are designed for equality access, not range scans or ordered output. Availability is product- and storage-engine-specific. PostgreSQL index types; MySQL CREATE INDEX; SQL Server indexes |
| Words, phrases, or language-aware searches across text | The database’s full-text facility | Full-text search handles token-based and linguistic matching, which is different from scalar equality, range comparisons, or a simple substring search. Implementation and setup vary by product. PostgreSQL text-search indexes; MySQL column indexes; SQL Server Full-Text Search |
What each index is for
B-tree: the default for ordinary value queries
A B-tree organizes indexed values so the database can locate a value and navigate to neighboring values. That makes it suitable for exact matches as well as ordered comparisons and retrieval. If a query filters on a date range, sorts by a key, or looks up a specific identifier, a B-tree is usually the first index type to consider.
SQL Server describes its rowstore indexes as B+ trees, a related tree structure. The precise implementation label differs, but the practical role is similar: support ordered key access rather than only exact-match lookups.
Hash: equality only, with important availability limits
A hash index maps a key to a location for equality access. It does not provide the key ordering needed to efficiently answer a range predicate or deliver sorted results. Consider one only when equality is the relevant operation and the database permits that index for the table in question.
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Full-text: words and language, not ordinary string equality
Full-text search indexes tokens extracted from text and applies search rules that can depend on language. It is appropriate for queries such as finding documents containing terms or phrases. It is not interchangeable with an ordinary index on a text column: an exact string comparison or a prefix/range lookup has different semantics, and substring matching is not automatically equivalent to full-text search.
How support differs by database
PostgreSQL
PostgreSQL offers B-tree, Hash, GiST, SP-GiST, GIN, and BRIN index methods, among others. Its documentation describes B-tree as supporting equality and range comparisons and sorted output; Hash supports equality comparisons. For full-text search, PostgreSQL uses GIN or GiST indexes on text-search values. The documentation says, “GIN indexes are the preferred text search index type.” GIN stores lexeme entries with matching locations, while GiST offers an alternative representation and trade-offs. Full-text search can run without an index, but an index can help recurring searches. See PostgreSQL 17 index types and PostgreSQL 16 text-search indexes.
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MySQL
MySQL index support depends on the storage engine. In the MySQL 26.7 manual, InnoDB and MyISAM ordinary indexes are BTREE; MEMORY (also called HEAP) supports HASH and BTREE; and NDB supports HASH and BTREE with caveats. InnoDB uses BTREE for ordinary indexes, so a Hash index is not a general substitute there. Check the engine actually used by the table and the manual for the deployed release. See MySQL CREATE INDEX and How MySQL uses indexes.
MySQL FULLTEXT indexes are available for InnoDB and MyISAM, and support CHAR, VARCHAR, and TEXT columns. They are a separate index form, not a normal index declared with USING BTREE or USING HASH. See MySQL column indexes.
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Microsoft SQL Server
SQL Server rowstore indexes use B+ trees. Hash indexes use an in-memory hash table and are for memory-optimized table scenarios, not a general replacement for a rowstore index. Full-Text Search is a separate facility: its Full-Text Engine builds an inverted, compressed index over tokens and supports linguistic searches. Language behavior, population, and configuration are distinct from regular indexes. Verify the target SQL Server or Azure SQL product and version; Microsoft’s SQL Server 2025 documentation notes breaking changes to Full-Text Search. See SQL Server indexes and SQL Server Full-Text Search.
Check these points before choosing
- Predicate: Is the query an exact equality, a range, an ordered retrieval, or a token/phrase search? Match the index to that operation.
- Meaning of “search”: Exact equality, prefix matching, substring matching, and language-aware word search are different requirements. Choose the feature that matches the application’s intended results.
- Product and table model: Confirm the database version, storage engine, table type, column type, and any full-text component or configuration requirements.
- Language and text behavior: Full-text tokenization and linguistic behavior are product-specific. Confirm that the configured language and search syntax match the content and query.
- Plan and workload: An index being eligible does not guarantee the optimizer will use it or that it will improve every query. Inspect execution plans and assess representative data and workload.
Is a hash index faster than a B-tree?
There is no universal speed winner established by these database capability rules. Hash indexes support a narrower operation set, and their availability differs across products and table models. For a real workload, compare valid choices using the target engine’s execution plan and representative queries; do not choose Hash solely on a general claim that it is faster.
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