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A query plan can point to an index worth investigating, but a scan alone does not prove an index is missing. Check the query’s filters and joins, compare estimated and actual rows where available, review existing indexes and optimizer statistics, then test a candidate against representative workload behavior.
How to diagnose a possible missing index
- Start with a representative slow query. Capture the exact SQL and its plan from the same database engine and environment. Plan labels and fields differ between PostgreSQL, MySQL, and SQL Server, so interpret output using the documentation for your engine and version.
- Find the access and filter operations that matter. Follow the plan to the nodes that read rows, then examine their filters, join conditions, and estimated work. A scan with a selective filter may be worth investigating; a scan that reads most of a table may be the efficient choice.
- Compare estimated and actual execution data. Where supported, check whether actual row counts and timings differ substantially from the optimizer’s estimates. A large mismatch can indicate that statistics or data distribution need attention before you design an index.
- Review the schema and predicate shape. Inspect the table’s existing indexes and determine whether their key columns can support the query’s actual filtering, joining, or ordering. A scan label does not tell you the right index definition or column order.
- Check optimizer statistics. If the estimates appear unreliable or an index is unexpectedly unused, assess whether statistics are current. Refresh them where appropriate, then inspect the plan again.
- Treat suggestions as leads, not instructions. Recommendations generated by an engine should be evaluated against existing indexes, overlapping coverage, the wider workload, and the cost of maintaining another index.
- Re-run and compare. Compare the revised plan and representative execution behavior with the original. Plan choices depend on data and version, and measurement features can add overhead.
Read the plan in your database engine
PostgreSQL
PostgreSQL presents a plan as a tree of nodes. The lower nodes show how rows are accessed, such as with a sequential, index, or bitmap index scan; upper nodes can perform joins, aggregation, or sorting. Read from the access nodes upward to understand how the query is executed. The PostgreSQL 18 EXPLAIN documentation describes a sequential scan as a reasonable choice when the query needs all rows, so do not treat that node as proof of a missing index.
For runtime evidence, use EXPLAIN (ANALYZE, BUFFERS). ANALYZE executes the statement and reports actual execution information alongside estimates; profiling adds overhead, so its timings are not a perfect representation of an uninstrumented run. PostgreSQL relies on table statistics in pg_statistic to make informed planning choices. If estimates seem suspect, check statistics as part of diagnosis rather than assuming the index is the cause. See the PostgreSQL planner statistics documentation.
MySQL
In MySQL’s EXPLAIN output, inspect the row for the table in question. Fields such as type, possible_keys, key, rows, filtered, and Extra help show access method, candidate indexes, the selected key, and estimated work. possible_keys lists indexes that may be relevant; key identifies the key the optimizer chose. They answer different questions.
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A NULL value for possible_keys means MySQL did not identify a relevant index for finding rows; inspect the query’s conditions and the schema before proposing one. A NULL key means the optimizer did not consider an index more efficient for executing the query. The rows value is an estimate, not a count of rows verified at runtime. These field definitions are described in the MySQL 8.0 EXPLAIN output documentation.
For a plan with timing and iterator details, MySQL 8.0.18 and later support EXPLAIN ANALYZE; it executes the statement. If an index is unexpectedly unused, MySQL documents ANALYZE TABLE as a way to update key-distribution statistics. Review the MySQL 8.0 ANALYZE TABLE documentation and re-check the plan after an appropriate statistics update.
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Use an estimated execution plan when you need optimizer output without executing the query, or an actual execution plan when runtime information is needed. SQL Server can show missing-index recommendations, but Microsoft advises reviewing all requests for a table alongside its existing indexes before adding one. The recommendation is a starting point for analysis, not a complete index-maintenance plan. See Microsoft’s guidance on tuning nonclustered indexes with missing-index suggestions.
When a scan is not an index problem
A scan can be efficient when the query needs a large share of a table: using an index to find many rows and then fetch them individually may cost more than reading the table. The useful question is not simply “Why is my query doing a table scan?” but whether the access path, filters, estimates, and measured work make sense for this query and its data.
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Likewise, an unused index is not automatically evidence of a missing index. The optimizer may judge another path cheaper, may lack useful statistics, or may not be able to use the index for the query’s predicate shape. Start by confirming what the plan actually says and how that corresponds to the SQL and current schema.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide whether to add an index
Use the plan to form a hypothesis: a particular query may benefit from an index that supports its filtering, join, or ordering requirements. Then check whether an existing index already offers suitable coverage and whether the plan’s row estimates are credible. If estimates diverge from actual rows, investigate statistics and data distribution before making index design the first fix.
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When a recommendation exists, compare it with indexes already serving the table and with the workload beyond the one slow statement. An additional index has maintenance costs, so a change that helps one query should be judged against the broader workload. Make the change only when a representative comparison shows the result is useful, and inspect the new plan rather than assuming the optimizer will use the index.
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Compare plans carefully
- Use the same query, database engine, environment, and representative data for the before-and-after comparison.
- Compare access nodes or access type, filters, estimated rows, and actual rows or timing where the engine reports them.
- Distinguish optimizer estimates from execution measurements; estimates can be wrong, and instrumentation can affect runtime.
- Re-check the plan after relevant statistics updates or schema changes, since choices can vary with data and engine version.
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