How Real SQL Server Problems Get Solved
Each use case walks through a real performance problem the same way the application does: symptom, evidence-backed analysis, a safe recommendation, and the measured outcome — all read-only, with no automatic changes.
Every case follows the same path
Scenario → Symptoms → How we analyzed → Evidence → Recommendation → Outcome. This is the analysis discipline the product encourages: never guess, always tie a fix to a signal.
Tracing a Blocking Storm Back to a Single Head Blocker
An OLTP system froze for seconds at a time during peak hours. We followed the blocking chain to one long-running transaction and resolved the contention without touching the schema.
Read the analysis →Diagnosing a Sudden Query Regression After a Plan Change
A nightly report that always finished in under a minute started running for 20+ minutes. Query Store evidence showed a plan flip — and the fix did not require rewriting the query.
Read the analysis →From PAGEIOLATCH Waits to a Targeted Index Fix
A growing dashboard query pushed IO waits to the top of the instance. Wait analysis pointed at storage reads, and Index Advisor turned that signal into one focused, low-risk index.
Read the analysis →RECOMPILE Cut Reads 95% — Then the Re-Analysis Told the Real Story
A reporting procedure looked cheap for one customer but exploded to 306K logical reads for all customers. AI analysis flagged a non-SARGable computed predicate and an implicit conversion; empirical testing confirmed it and a sargability rewrite stabilized plans (variance 126% → 0%) — yet the verdict stayed POOR_QUERY_DESIGN, because the cost is the design, not a missing hint.
Read the analysis →