A production SQL query has become slow. How would you read its execution plan, choose a likely fix, and prove that the change helped?

Read an execution plan for a slow SQL query

Uses an actual execution plan, row-flow evidence, runtime measurements, and representative parameters to diagnose a slow query without guessing.

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TL;DR

I would identify the exact query and representative parameter values, record duration, CPU, logical reads, and returned row count, then capture the actual execution plan in a safe environment or use an already captured production plan. I would follow the row flow and compare estimated rows with actual rows while checking access paths, joins, sorts, lookups, spills, and warnings. A large estimate mismatch or unexpected row explosion often points to the first useful hypothesis. The plan does not prescribe the fix; it narrows what I should test, such as rewriting a predicate, updating statistics, changing query shape, or adding a carefully chosen index.

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Actual versus estimated plans, row estimates, scans and seeks, joins, lookups, spills, statistics, parameter sensitivity, validation, and production-safe tuning.

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Why this question matters
Execution-plan questions separate evidence-led database tuning from generic advice to add an index. A useful diagnosis explains how rows flow through the chosen plan, why the optimizer made that choice, and how a proposed change will be validated without changing query results.
I would identify the exact query and representative parameter values, record duration, CPU, logical reads, and returned row count, then capture the actual execution plan in a safe environment or use an already captured production plan. I would follow the row flow and compare estimated rows with actual rows while checking access paths, joins, sorts, lookups, spills, and warnings. A large estimate mismatch or unexpected row explosion often points to the first useful hypothesis. The plan does not prescribe the fix; it narrows what I should test, such as rewriting a predicate, updating statistics, changing query shape, or adding a carefully chosen index.
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Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
I would not simply pick the operator with the largest displayed cost. I would trace where the actual row count first diverges sharply from the estimate and how that affects later operators. For example, applying a function to can turn an indexed range seek into a large scan; a key lookup may be harmless for ten rows but expensive when repeated hundreds of thousands of times; and an underestimated sort or hash join may spill to temp storage because it received too little memory. Each observation leads to a specific experiment, and I would rerun the same representative workload to confirm identical results and compare elapsed time, CPU, and logical reads before accepting the change.

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Details

Adding the first suggested index without checking existing indexes or write cost.
Treating estimated cost percentages as measured runtime.
Ignoring the first large estimated-versus-actual row mismatch.
Calling every scan bad even when most of the table is needed.
Measuring only duration once and not verifying result correctness, CPU, or logical reads.

A production SQL query has become slow. How would you read its execution plan, choose a likely fix, and prove that the change helped?

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