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Add an index when a real, important query can use it to avoid enough row-reading or sorting work to outweigh the storage and write overhead. Start with the query and its execution plan—not with a column in isolation—and verify the effect on representative data.
When should I add an index to a table?
Consider an index when a recurring query filters, joins, orders, or groups data in a way the database can use, and the query reads substantially less data as a result. A selective lookup is a common candidate. An index is not automatically useful just because a column appears in a WHERE clause: the optimizer estimates whether index access is cheaper than scanning the table, based on the query, data distribution, and available statistics.
PostgreSQL’s documentation puts the tradeoff plainly: “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.)
- Potential read benefit: less work to find matching rows, locate join matches, or produce rows in a useful order.
- Ongoing cost: storage, plus maintenance when relevant table data is inserted, updated, or deleted.
- Decision standard: keep an index when its measured or defensible benefit to the workload justifies those costs.
There is no reliable universal row-count cutoff or guaranteed speedup. A small table, or a query that needs most of its rows, may be faster to read sequentially. Both PostgreSQL’s usage guidance and MySQL’s index manual caution against treating indexes as automatically beneficial in those cases (PostgreSQL 15: Examining Index Usage; MySQL 26.7: How MySQL Uses Indexes).
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How do I know if an index will improve query performance?
Use a repeatable workflow centered on a query that matters to the application. Include its actual predicates and ordering, inspect the plan, then compare behavior with representative data and workload.
- Choose the query and its importance. Start with a recurring slow query or a defined latency or throughput goal. Record its
WHERE,JOIN,ORDER BY, andGROUP BYclauses, along with how often it runs and how many rows it typically returns. - Check statistics and the current plan. In PostgreSQL, run
ANALYZEso the planner has current value-distribution statistics, then inspect the plan withEXPLAIN. Statistics that do not reflect the data can lead to poor row-count estimates and a plan that does not match actual behavior (PostgreSQL 15: Examining Index Usage). - Look for a compatible access pattern. A selective equality or range condition, join key, or useful ordering may be a candidate. Check that the proposed index’s columns and order match how the query is written; an expression or comparison involving type conversion may not use an otherwise relevant index. MySQL documents, for example, that type conversions or incompatible character sets can prevent index use in some comparisons (MySQL 26.7: How MySQL Uses Indexes).
- Test realistic data and workload. Avoid tiny or unusually uniform test fixtures if production data is larger or skewed. PostgreSQL warns that small test data can give misleading conclusions and recommends using real data where possible (PostgreSQL 15: Examining Index Usage).
- Compare plan and observed behavior. In PostgreSQL,
EXPLAIN ANALYZEexecutes the query and reports actual row counts and timing for plan nodes (PostgreSQL: Using EXPLAIN). Compare against a baseline, while accounting for the system and workload: a plan that contains an index scan does not, by itself, prove an end-to-end improvement. - Account for write and space costs. Include insert, update, and delete activity, as well as storage, in the decision. Remove or avoid indexes without a demonstrated or defensible workload role, and revisit the choice if the workload changes.
Which queries are common index candidates?
Selective filters
An index may help a query that uses an equality or range predicate to find a relatively small part of a table. The key question is not merely whether the query filters, but whether the filter and data distribution let the engine avoid enough work to beat sequential access.
Join keys
Columns used to match rows across tables can be candidates when the join repeatedly needs to locate corresponding values. Evaluate the full query plan: the join type, estimated rows, and the amount of data read all affect whether an index helps.
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MySQL documents index use for sorting or grouping when the operation can use a suitable leftmost prefix, as well as for some other patterns such as certain MIN/MAX lookups and covering-index reads (MySQL 26.7: How MySQL Uses Indexes). For PostgreSQL, the documented ordering benefit applies to B-tree indexes: a matching index can provide ordered output and avoid a separate sort in suitable cases (PostgreSQL 18: Indexes and ORDER BY).
How should I choose a composite index?
A composite index stores keys in a particular column order, so choose that order against the recurring queries it needs to serve—not just the list of columns they happen to mention. Compare the candidate designs using these questions:
- Query coverage: Which important filter, join, ordering, or grouping patterns can use the index?
- Leading columns: Do the target queries constrain or order by a usable leading portion of the key?
- Selectivity and work avoided: How many rows or pages are likely to be touched compared with a sequential scan?
- Read/write balance: Is the query benefit worth the extra space and maintenance for this workload?
- Evidence quality: Are statistics current, data representative, and actual plan behavior examined?
For MySQL, a multi-column index can support searches on its leftmost prefixes; an index on (a, b, c) can therefore support prefixes beginning with a, but it should not be assumed to serve a query that only constrains b in the same way (MySQL 26.7: How MySQL Uses Indexes). Do not generalize that rule to every database engine or index type. PostgreSQL’s ordering guidance cited above specifically concerns B-tree indexes.
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Why is my database not using an index?
Index use is a cost-based choice, not a promise. A sequential scan can be the cheaper plan when a query reads most of a table or the table is small. Other common explanations to investigate include stale or unrepresentative statistics, a predicate or expression that does not match the indexed key, incompatible types or character sets in a comparison, or a composite index whose leading columns do not fit the query.
Check the plan and estimated row counts before adding another index. In PostgreSQL, refresh statistics with ANALYZE and use EXPLAIN; use EXPLAIN ANALYZE when you need observed node timings and row counts and can safely execute the query (PostgreSQL 15: Examining Index Usage; PostgreSQL: Using EXPLAIN). A sequential scan is not necessarily a problem if it is the lowest-cost way to satisfy the query.
Do indexes slow down inserts and updates?
They can. An index must be maintained when changes affect its indexed data, so adding indexes can increase write work as well as consume storage. MySQL 8.0’s optimization manual explicitly notes that unnecessary indexes waste space and time and add cost to inserts, updates, and deletes (MySQL 8.0: Optimization and Indexes). PostgreSQL likewise describes index overhead in its PostgreSQL 18 introduction (PostgreSQL 18: Indexes).
The practical question is whether the read improvement matters enough to justify the continuing cost in the workload as a whole. Consider how often the indexed data changes, how important the accelerated query is, and whether other indexes already cover the same access pattern. Monitor use and reassess when query patterns or data volumes change.
How PostgreSQL handles ordering and LIMIT
In PostgreSQL, a B-tree index that matches an ORDER BY can return rows in the requested order without a separate sort. This can be especially useful with ORDER BY plus LIMIT, because the database may retrieve the first rows without scanning the remainder. If the query needs a large fraction of the table, a sequential scan followed by an explicit sort may be faster (PostgreSQL 18: Indexes and ORDER BY).
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