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Choose a B-tree when queries need equality lookups plus ranges, sorting, or ordered traversal. Consider a hash index only for equality-only lookups, and only if your database engine supports it for the table type in question. Neither structure is automatically faster: check the execution plan and measure the workload you actually run.
What is the practical difference?
A B-tree keeps keys in an ordered structure. That lets it support equality searches as well as range predicates and ordered access. PostgreSQL 17 puts the distinction plainly: “B-trees can handle equality and range queries on data that can be sorted into some ordering.” PostgreSQL 17: Index Types
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A hash index uses a hash function to locate entries by key. Its intended use is equality comparisons; it does not provide the ordered key traversal needed to satisfy a range or sort operation. In short, a hash index is a more specialized option, not a general replacement for a B-tree.
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Use a B-tree as the candidate when the application needs any of the following:
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- Equality predicates such as
WHERE customer_id = 42. - Ranges such as
WHERE created_at >= ... AND created_at < .... - Rows in key order, including queries that sort by the indexed key.
- A general-purpose index where query needs may include both equality and ordered access.
PostgreSQL documents B-tree support for equality and range queries on sortable data. MySQL’s comparison of index types likewise distinguishes B-tree’s broader ordered use from Hash’s equality-only role. MySQL 8.4: Comparison of B-Tree and Hash Indexes
When is a hash index worth considering?
Consider Hash only after confirming that the workload is equality-focused and the database exposes Hash indexes for the table’s storage type. PostgreSQL 15 says Hash indexes are “best optimized for SELECT and UPDATE-heavy workloads that use equality scans on larger tables.” That is guidance about a workload shape, not a guarantee that Hash will outperform B-tree on a particular system. PostgreSQL 15: Hash Indexes
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Hash behavior has its own failure modes. PostgreSQL warns that an unbalanced bucket can require scanning overflow pages, potentially causing more block accesses than a B-tree. SQL Server similarly notes that longer bucket chains make equality lookups slower. Key distribution and bucket design therefore matter; “equality-only” by itself is not enough to establish a win. Microsoft SQL Server Index Design Guide
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The choice is not equally available for every relational table. Engine, version, and storage type determine whether Hash is an option at all.
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| Database documentation | What it establishes | Practical implication |
|---|---|---|
| PostgreSQL 17 | B-tree supports equality and range queries; Hash is an index type for hashable equality comparisons. | Compare query shape and account for Hash overflow behavior; do not assume Hash is faster. |
| PostgreSQL 15 | Hash guidance highlights SELECT- and UPDATE-heavy equality scans on larger tables; Hash indexes are persistent on-disk and crash-recoverable. | Keep the version attached to this guidance when applying it. |
| MySQL 8.4 | Hash indexes are particularly relevant to the MEMORY storage engine, where the documentation describes a B-tree-or-Hash choice. | Do not assume ordinary disk-based MySQL tables offer the same configurable choice; verify the table’s engine and version. |
SQL Server documentation, ver17 view |
Hash indexes are discussed for memory-optimized tables; B-tree terminology is used for indexes generally. | Do not generalize this Hash feature to all SQL Server table storage. |
Sources: PostgreSQL 17 index types, PostgreSQL 15 Hash indexes, MySQL 8.4 index comparison, MySQL 8.4 optimization and indexes, SQL Server index design guide, and SQL Server indexes.
How to make the choice for your workload
- Identify the exact system. Record the database product and version, plus the table’s storage engine or memory-optimized status. Confirm that the desired index type is supported there.
- Write down the actual query shapes. Separate equality-only predicates from range conditions, ordering, and other ordered access. If any important query needs ranges or order, favor B-tree.
- Inspect plans for representative queries. An available index is not a promise the optimizer will use it. MySQL notes that the optimizer may avoid an index when it estimates that a large percentage of rows must be accessed. MySQL 8.4: How MySQL Uses Indexes
- Measure reads and writes on representative data. Compare equivalent queries, execution plans, latency, and resource use under realistic data distribution and workload. Include index maintenance and storage costs in the comparison; these vary by system and workload.
- Keep the index only if the measured trade-off is worthwhile. Recheck after meaningful changes to data distribution, query mix, or database version.
Can a hash index handle range queries?
No—not as an ordered range access method. A Hash index is designed for equality comparisons and does not keep keys in the order required to walk a range or return key order. If a query needs ranges or ordered traversal, use a B-tree candidate instead.
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What not to assume
- Do not assume Hash is faster. PostgreSQL documents overflow-page cases that can increase block accesses; SQL Server warns about long bucket chains.
- Do not assume the optimizer will use an index. If many rows are expected, a scan may be estimated as preferable.
- Do not assume the choice is available on every table. MySQL’s documented choice is particularly tied to MEMORY, while SQL Server Hash guidance concerns memory-optimized tables.
- Do not generalize a benchmark result across engines. The official guidance cited here gives no universal performance or size advantage, and actual results depend on the engine, table, data distribution, and workload.
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