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These distinctions affect index size, lookup paths, and which designs may fit a workload. They do not make one database universally faster: usefulness depends on the query, data distribution, engine or access method, and the cost of maintaining indexes as data changes.
At a glance: what an index points to
| Database and scope | Where table rows live | How a separate index reaches rows | Distinctive points covered here |
|---|---|---|---|
| SQL Server rowstore | A table is a heap or has one clustered index, which stores its rows by clustered key. | A nonclustered index uses a row locator: a heap-row locator for a heap, or the clustered key for a clustered table. | Nonclustered indexes can have included columns; filtered nonclustered indexes index a predicate-defined subset. |
| MySQL with InnoDB | The clustered index stores table rows. It is normally the primary key. | Secondary-index records include primary-key columns, which are used to reach the clustered row. | The primary key’s width affects secondary-index size; multiple-column indexes support leftmost prefixes. |
| PostgreSQL 18 | Ordinary tables are heaps with separate indexes. | Index access methods are distinct from heap storage; an index-only scan can avoid a table visit when its conditions are met. | Six documented index methods, partial indexes, and `INCLUDE` payload columns. |
The SQL Server descriptions here concern rowstore indexes. The MySQL clustered-storage details are specifically for InnoDB, not every MySQL storage engine. PostgreSQL index behavior varies by access method.
What clustered means in SQL Server and InnoDB
SQL Server: a clustered index determines row storage
A SQL Server rowstore table can have one clustered index, which stores the data rows by its key, or no clustered index, in which case the table is a heap. As Microsoft Learn puts it: “You can have only one clustered index per table, because the data rows themselves can be stored in only one order.”
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A nonclustered index is a separate structure. Its row locator identifies a row in a heap, or uses the clustered key when the table has a clustered index. On a clustered table, the clustered key is automatically present in each nonunique nonclustered index. That matters when estimating the size and contents of secondary indexes, even if the clustered key was not explicitly added to each one.
InnoDB: the clustered index contains the rows
InnoDB chooses the primary key as its clustered index when one is defined. If there is no primary key, it uses the first `UNIQUE` index whose key columns are all `NOT NULL`. If neither exists, InnoDB creates a hidden clustered index named `GEN_CLUST_INDEX` on an assigned row ID.
InnoDB secondary-index records include the primary-key columns as well as the secondary key. A long primary key therefore makes secondary indexes larger. The practical design implication is to account for the primary key in the footprint of every secondary index, rather than thinking of those indexes as containing only their declared secondary keys.
PostgreSQL separates heap storage from index methods
PostgreSQL ordinary tables store rows in a heap, with indexes maintained separately. PostgreSQL 18 documents six index methods: B-tree, Hash, GiST, SP-GiST, GIN, and BRIN. They are not interchangeable: the suitable method depends on the operators and workload the index must support.
This variety makes “PostgreSQL index behavior” too broad a phrase for some design rules. For example, the way multicolumn indexes benefit from constrained columns differs between B-tree, GIN, BRIN, and GiST. Choose and assess the access method for the actual query pattern, rather than assuming every PostgreSQL index follows the same rules.
Composite indexes: column order is not one universal rule
A composite index stores more than one key column. Whether a query can use it effectively depends on the database and, in PostgreSQL, the access method.
| Database or method | What the cited documentation establishes | Design implication |
|---|---|---|
| MySQL multiple-column indexes | An index on `(col1, col2, col3)` supports lookup by `(col1)`, `(col1, col2)`, or all three columns: any leftmost prefix. | Do not assume this index offers the same lookup support when a query constrains only a later column. |
| PostgreSQL B-tree | Most efficient when conditions constrain its leading, or leftmost, columns. | Consider which columns are constrained at the beginning of the index; validate the actual plan. |
| PostgreSQL GIN and BRIN | Multicolumn search effectiveness is documented as the same regardless of which indexed column is constrained. | Do not apply the B-tree leading-column rule automatically to these methods. |
| PostgreSQL GiST | Has its own first-column sensitivity. | Evaluate its behavior separately; it is not interchangeable with the GIN or BRIN rule. |
| SQL Server rowstore | The cited Microsoft Learn material does not establish an across-the-board leftmost-prefix rule for this comparison. | Assess key order against the workload and inspect the execution plan instead of importing another engine’s rule. |
Covering indexes and included columns
A covering index contains the columns a query needs from a table, allowing the database to satisfy that query from the index in suitable circumstances. The term describes a relationship between a particular query and an index, not a guarantee that every query using the table can avoid reading table data.
SQL Server: `INCLUDE` on nonclustered indexes
SQL Server nonclustered indexes can add nonkey columns with `INCLUDE`. Those columns are stored at the leaf level and can help cover a query without becoming index key columns. Included columns can increase index size and the work required to maintain it, so adding many or wide columns has a cost.
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MySQL: all needed table columns must be in the index
The MySQL manual describes an index as covering a query when it contains all columns needed from a table. This is the coverage criterion; it does not mean every such index will be chosen or improve every query.
PostgreSQL: `INCLUDE` adds payload, not search keys
In PostgreSQL, `INCLUDE` columns are non-key payload. They cannot be used in index scan qualifications and do not affect uniqueness or exclusion enforcement. An index-only scan may return their values without visiting the table when the query and visibility conditions allow it. Included data duplicates table data and can bloat an index, so wide payload columns deserve particular caution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Subset indexes: filtered versus partial
SQL Server filtered nonclustered indexes and PostgreSQL partial indexes both target a predicate-defined subset, but their documented behavior should not be treated as identical.
- SQL Server: A filtered nonclustered index can focus on a well-defined subset repeatedly queried, such as rows with non-NULL values or unprocessed rows in a workflow. Indexing only that subset can reduce storage and maintenance relative to indexing all rows. Filter predicates have limitations.
- PostgreSQL: A partial index contains rows satisfying its predicate. PostgreSQL documents partial indexes separately from its index methods.
- MySQL InnoDB: The InnoDB documentation cited for clustered and secondary indexes does not establish an equivalent general partial-index feature. Do not infer one from that storage description.
What the structural differences mean for design
- For InnoDB, include primary-key width in index planning. Because secondary-index records carry primary-key columns, the primary key affects the size of all secondary indexes.
- For SQL Server, distinguish the table’s clustered key from nonclustered keys and payload. Clustered-key values are automatically present in nonunique nonclustered indexes on clustered tables; `INCLUDE` adds further nonkey data.
- For PostgreSQL, choose an access method and assess its specific behavior. B-tree, GIN, BRIN, and the other documented methods serve different operator and workload needs; a rule for one method may not apply to another.
- For all three, account for writes as well as reads. Indexes take storage and require maintenance when rows are inserted, updated, or deleted. Adding indexes without a query and workload reason can impose costs without helping the target operation.
How to compare indexes for a real workload
- Pin down the scope. Record the database version and, for MySQL, the storage engine. For PostgreSQL, identify the access method; for SQL Server, distinguish heap from clustered rowstore.
- Describe the query. Note its predicates, constrained columns, selected columns, and whether it targets a subset of rows.
- Account for the data. Consider distribution and selectivity: an index available to the optimizer may still not improve a particular query.
- Include write and storage costs. Consider how often relevant rows change and how wide the proposed keys or included values are.
- Check the actual execution plan and workload behavior. A scan can be the optimizer’s appropriate choice. Evaluate the target queries and modifications rather than treating index availability as proof of benefit.
The official guidance for SQL Server, MySQL, and PostgreSQL all makes index costs or workload-dependent use relevant to the decision. A feature comparison can explain what an index means in each engine; only the actual workload can show whether a particular design helps.
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