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There is no universal percentage: an index’s cost depends on the database engine, the index’s size and design, and which writes and queries your workload performs. Indexes can make matching rows much faster to find, but every maintained index adds work to relevant writes and consumes storage; a large or wide index can also increase I/O and memory pressure. The right way to judge one is to compare read benefits with write, storage, and maintenance costs under a representative workload.
What overhead does an index add?
PostgreSQL’s documentation sums up the tradeoff: “Indexes are a common way to enhance database performance. An index allows the database server to find and retrieve specific rows much faster than it could do without an index. But indexes also add overhead to the database system as a whole, so they should be used sensibly.” PostgreSQL 18: Indexes.
That overhead has several parts. The index must be maintained when relevant data changes, it occupies disk space, and its pages compete for memory and I/O. There is no general-purpose statistic that predicts a fixed penalty per index across databases or workloads. An index’s value and cost need to be measured together.
Do indexes slow down inserts and updates?
They can. An insert or delete may require adding or removing entries in indexes that cover the affected row. An update may require index changes when it modifies a key or other indexed value. The cost depends on which indexes a write affects, as well as the database engine and index design; it is not necessarily equal for every index, or a simple linear slowdown as index count rises.
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- MongoDB: Inserts and deletes add or remove document keys in the collection’s indexes. An update affects a subset, depending on the keys changed; sparse or partial indexes are maintained only for documents they include. See Write Operation Performance.
- MySQL: Indexes need updates for inserts, updates, and deletes. Unnecessary indexes also consume space and can add work for the optimizer. See MySQL 26.7: Optimization and Indexes.
- SQL Server: Changing an indexed column can require updates to every index that contains that column. Microsoft advises keeping indexes narrow on heavily updated tables. See the SQL Server Index Architecture and Design Guide.
Because the effect varies, avoid applying a benchmark or rule of thumb from another engine or workload as a forecast for your own system. Measure write latency or throughput with and without a candidate index while keeping the workload representative.
How do indexes affect storage, memory, and cache?
Every index takes storage, and a wide index can use substantially more of it than a narrow one. Index pages also need to be read and, where possible, cached. Microsoft notes that low page density means more pages must be read and more memory is needed to cache them; if memory is constrained, that can mean additional disk I/O. A covering index with many included columns can reduce how many rows fit on each page, increasing I/O and reducing cache efficiency. See Microsoft’s design guide and index maintenance guidance.
More index storage does not automatically mean a query will slow down: an index may still reduce the work needed for a particular read. The practical question is whether the read improvement justifies the additional space and resource use across the workload. Page density and fragmentation are clues for investigation, not proof by themselves that rebuilding will improve performance.
Can too many indexes hurt performance?
Yes, when indexes impose write, storage, cache, or optimizer costs without a compensating benefit to frequent queries. But an index count alone does not identify the problem. A small number of wide or poorly chosen indexes may be more costly than a larger set of targeted ones. Likewise, an index is not useful merely because a query refers to one of its columns: key order and the query’s predicates, joins, ordering, and selected columns matter.
Start with real query patterns
List recurring, important queries and examine their filters, join conditions, sort order, and returned columns. Check actual execution plans and whether the optimizer’s estimates are credible. For PostgreSQL, refresh planner statistics with ANALYZE as appropriate, then compare plans and execution using realistic data and candidate indexes. Its guidance emphasizes that there is no universal recipe for choosing indexes. See Examining Index Usage.
Look for redundancy and unused indexes
Compare a proposed index with existing indexes before adding it. If an existing index can support the same workload with a small modification, retaining a near-duplicate may add avoidable maintenance and storage cost. SQL Server’s design guidance recommends checking usage statistics and dropping indexes that are unused. Treat “unused” cautiously: a short or unrepresentative observation window may miss infrequent but important queries.
For a clearly defined, frequently queried subset of data, a filtered index in SQL Server or a partial index in PostgreSQL may reduce the rows indexed and therefore the storage and maintenance burden. Whether that tradeoff helps depends on the actual query and data distribution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you decide whether an index is worth keeping?
Compare the candidate configurations against the same representative workload rather than judging an index by one query or one metric. Track:
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- Throughput and latency for inserts, updates, and deletes that may maintain it.
- Total index size and associated I/O and cache footprint.
- Index usage and overlap with other indexes over an observation period that includes normal workload variation.
- Maintenance duration, locking or concurrency effects, and recovery constraints.
Keep an index when its measurable contribution to important reads justifies its ongoing costs. Consider consolidating or removing it when representative usage data shows it is redundant or unused and no important workload depends on it. Recheck after workload changes rather than assuming an old decision remains optimal.
When should you rebuild or remove an index?
Maintenance is an intervention with costs of its own, not a routine cure for any index-related slowdown. Consider page density and fragmentation alongside the queries and resources affected. Do not use a universal fragmentation percentage as an automatic rebuild trigger without support for the specific engine, version, and workload.
SQL Server: weigh density, fragmentation, and workload
Microsoft recommends considering both fragmentation and page density when choosing whether and how to maintain an index. More pages can mean more reads and a larger memory footprint, but a metric alone does not show that a rebuild will improve the workload. Evaluate expected benefit against the operation’s resource use and operational constraints using the SQL Server index maintenance guidance.
PostgreSQL: account for rebuild behavior
An ordinary PostgreSQL REINDEX can block writes to the table while the index is rebuilt. REINDEX CONCURRENTLY avoids that normal write blocking, but performs two table scans per index and has additional restrictions. If a concurrent rebuild fails, it can leave behind an invalid index; queries ignore that index, but updates may still incur its maintenance overhead. Check the requirements and failure behavior in the PostgreSQL 18 REINDEX documentation before choosing a method.
Remove an index only after its lack of value or redundancy is established against an appropriate workload period. An index that appears idle during a quiet interval may serve a periodic report or a rare but critical operation.
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