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Indexes can help a database find rows without scanning all the data, but each index also takes space and may add work when data changes. Keep indexes that support important queries, then assess their read benefit against write activity, index size, and operational cost using evidence from your own workload.
What does a database index do?
An index is an auxiliary data structure that stores searchable key information so the database can locate candidate rows or documents more directly. It can improve queries that match the index, but an index does not guarantee that every query will run faster: the result depends on the query, the data, and the index design.
Index options and behavior vary by database. PostgreSQL documents methods including B-tree, hash, GiST, SP-GiST, GIN, and BRIN, as well as multicolumn, partial, and covering indexes. MongoDB describes indexes as a way to find relevant documents without examining an entire collection. See the PostgreSQL index documentation and MongoDB 8.0 write-performance documentation.
Do indexes slow down writes?
They can. When data is inserted, updated, or deleted, the engine may also need to add, change, or remove corresponding index entries. The cost depends on which indexed keys the write affects and how the database implements index maintenance; simply counting indexes does not tell you the cost of a particular write pattern.
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- MongoDB 8.0: Inserts and deletes maintain keys in relevant indexes. An update may affect only a subset, depending on which indexed fields change.
- MySQL 26.7: Indexes can add cost to inserts, updates, and deletes, even as they help some reads.
- SQL Server: Changes to indexed columns can require updates to indexes that contain those columns; Microsoft advises restraint with indexes on heavily modified tables.
Read the engine-specific guidance in the MongoDB manual, MySQL 26.7 manual, and SQL Server index design guide.
How much storage do database indexes use?
Indexes use storage in addition to the underlying table or collection. There is no universal index-to-table size ratio established by the cited documentation: size depends on the database engine, index type, indexed keys, and data. Avoid estimating overhead as a fixed percentage without measurements for your system.
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Index width matters. SQL Server recommends keeping indexes narrow; adding many columns to a covering index can increase storage, I/O, and memory footprint. MySQL also cautions that unnecessary indexes waste space and make the optimizer spend time determining which index to use. See the SQL Server design guide and MySQL index documentation.
How do I know which indexes to keep or remove?
Start with the workload, not a generic removal list. Check query plans and the database’s index-usage information to see whether an index supports important queries. Then weigh the observed read benefit against how often writes occur, which indexed fields change, and the index’s storage and resource footprint. PostgreSQL’s index chapter covers examining index usage; MongoDB recommends evaluating whether existing indexes are actually used.
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- Identify important queries. Use workload evidence to find the reads whose performance matters, then inspect their plans.
- Check index use. Consult the engine’s usage information and verify whether candidate indexes serve those queries.
- Account for writes. Consider write frequency and whether inserts, updates, or deletes affect the indexed keys.
- Measure the footprint. Consider index width and storage, plus relevant I/O, memory, and optimizer costs.
- Validate changes against the workload. Test proposed additions or removals using representative queries and write activity before making production changes.
An index with little observed use may still serve an infrequent but important query, so usage evidence should be interpreted in the context of the workload rather than treated as an automatic instruction to drop it. The cited documentation does not establish a universal maintenance interval or a cross-engine list of indexes to remove.
Can creating or rebuilding an index affect production?
Yes. Index creation is an operational change, and its impact differs by database and command. For PostgreSQL 17, a standard CREATE INDEX build blocks writes to the relation until it completes. CREATE INDEX CONCURRENTLY allows normal operations to continue, but performs two scans and takes significantly longer. Those details are specific to the documented PostgreSQL 17 behavior; do not assume another engine uses the same options or trade-offs. See PostgreSQL 17: CREATE INDEX.
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Which trade-offs should I compare?
| Decision factor | What to examine |
|---|---|
| Query benefit | Which actual queries the index supports, and how important those queries are. |
| Write activity | How often data changes and whether those writes affect indexed fields. |
| Index footprint | Index type and width, along with storage and any relevant I/O or memory cost. |
| Evidence of use | Query plans and engine-specific usage information, interpreted against the workload. |
| Operational impact | How the specific engine and version handle creating, rebuilding, or changing the index. |
These are shared trade-off categories, not identical implementation rules. PostgreSQL, MongoDB, MySQL, and SQL Server document the general costs and benefits differently, so use the documentation and operational behavior for your actual engine and version.
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