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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAn existing index does not guarantee that a query will use it—or that using it will make the query fast. The quickest way to find the cause is to inspect the plan for the exact slow query, identify which work dominates, and compare estimated rows with actual rows where your database supports runtime plan data. Check statistics, predicates, joins, sorts, and repeated operations before adding an index or forcing one.
Start with the exact query and its execution plan
Before changing schema or query text, record the database engine and version, the exact SQL, the parameter values used, the relevant table definitions and index definitions, and the observed latency. Plans and instrumentation differ by engine and version, so do not assume PostgreSQL and MySQL commands or output mean the same thing.
Capture a plan for the same query and parameters that are slow. PostgreSQL’s EXPLAIN documentation explains how to inspect the selected plan; MySQL’s EXPLAIN guide describes how to see how the optimizer expects to process a statement. In either case, note the scan type, index condition, join order, and the shape of the plan. An index appearing in one node does not show that the full plan is efficient.
PostgreSQL: inspect runtime row counts carefully
PostgreSQL’s EXPLAIN ANALYZE executes the statement and adds actual row counts and timing to plan nodes. Compare estimated rows with actual rows, and inspect loops: an operation repeated many times can be expensive even when each individual execution looks small. PostgreSQL documents that profiling adds overhead, so the measured execution time is not a perfect proxy for ordinary application latency. The reported executor time also does not include parsing and rewriting, and output transfer is not automatically represented as database executor time. See the PostgreSQL guide to using EXPLAIN.
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Because EXPLAIN ANALYZE runs the statement, take special care with data-changing statements. Understand their effects and follow PostgreSQL’s documented safe procedure before using execution analysis on them; see PostgreSQL 18 EXPLAIN.
MySQL: separate expected plan from runtime evidence
MySQL’s EXPLAIN shows the optimizer’s expected processing strategy. Do not treat its output as runtime measurements equivalent to PostgreSQL’s EXPLAIN ANALYZE. Use instrumentation and procedures documented for the deployed MySQL release to establish actual behavior, and validate that the manual matches that release. The available MySQL references here are versioned under the 26.7 manual path.
Decide whether the index should help this query
An optimizer chooses among valid plans based on its estimates of their costs. A sequential scan can be reasonable when a query returns a large share of a table, or when using an index would still require substantial table access. The existence of an index alone is not evidence of a planner mistake. PostgreSQL’s index-usage guidance recommends examining actual query conditions and whether the index matches them; its EXPLAIN guide explains plan costs and choices.
Check whether the indexed columns and their ordering fit the query’s predicates and join conditions. Look at selectivity and how many rows the query must return. If a condition does not match the index structure, or the result is broad, an index lookup may not save much work. Diagnose the mismatch in the plan before proposing a new index.
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A substantial gap between estimated and actual row counts is a clue to investigate, not a diagnosis by itself. It can point to stale or insufficient statistics, sampling limits, or relationships between columns that ordinary per-column estimates do not represent.
Refresh statistics after material data changes
PostgreSQL’s planner relies on table statistics. PostgreSQL documents ANALYZE for collecting data-distribution statistics; sampling means the resulting estimates remain approximate. Review the PostgreSQL 17 ANALYZE documentation and PostgreSQL guidance on examining index usage for the deployed release and operational context.
MySQL documents ANALYZE TABLE for updating key distributions. Use the command and precautions appropriate to your installed release; the relevant MySQL optimizer-issues documentation covers optimizer-related issues.
Consider correlated predicates in PostgreSQL
If a query filters on multiple columns whose values are related, independent per-column estimates can misrepresent how many rows satisfy the combined conditions. PostgreSQL supports multivariate planner statistics to capture some such relationships. Use them only when the plan and data point to this estimation problem; they are not a general-purpose remedy for every poor plan. See PostgreSQL 17 statistics used by the planner.
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Look beyond the index scan for the real bottleneck
A query can use an index and still spend most of its time elsewhere. Follow the plan through its later operations and compare node costs, row counts, loops, and runtime evidence where available. PostgreSQL’s plan output can show sort methods and resource use; a costly sort after an index scan may be the more important target. Joins, broad row retrieval, or a node executed repeatedly can likewise dominate.
Also distinguish database execution from total request latency. Network transfer, application processing, and other work around the SQL call may explain a slow user-visible request even when executor time is modest. MySQL’s SELECT optimization guide recommends isolating parts of a query that take excessive time, including functions evaluated for many rows. Use measurements to establish the bottleneck before rewriting.
Test one evidence-based change at a time
- Save a baseline. Keep the original query, parameters, plan, relevant statistics context, and timing under representative conditions.
- Choose a change the plan supports. Depending on the evidence, update statistics, revise a predicate or join, or evaluate a specific index change. Avoid changing several things at once.
- Compare like with like. Run the same query with representative data and comparable conditions, then compare plans and timings. Check whether the targeted work actually improved and whether another node became the bottleneck.
- Use hints only as a considered test. PostgreSQL advises investigating estimates and plan costs before trying to force index use. MySQL provides index hints as an optimizer tool, but a hint is not proof that it is the right fix. Review the relevant PostgreSQL index-usage guidance and MySQL optimizer documentation.
What to collect before asking for help
- Database engine and exact version, plus the exact query and representative parameter values.
- Relevant schema and index definitions, with sensitive values removed.
- The plan from the same query, including actual execution data if safely available.
- Estimated and actual row counts, loops, and any prominent sort or join work.
- Whether data or statistics changed recently, and the measured latency boundary—database execution or end-to-end request time.
For engines other than PostgreSQL or MySQL, use the current vendor manual for plan commands, runtime instrumentation, statistics maintenance, and any safe-execution procedure. The commands above are engine-specific examples, not interchangeable SQL tuning instructions.
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