After a deployment, database CPU spikes and one API endpoint is suspected. How would you investigate?

Debug high database CPU after an API change

Explains high database CPU after an API change with practical .NET data-access examples and production trade-offs.

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I would first confirm the timing: when CPU increased, which deployment happened, and which endpoints or queries changed. Then I would identify the most expensive queries using database monitoring, application logs, or traces. I would compare generated SQL before and after if possible. I would also check query count, execution plans, row counts, and missing indexes.

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Understanding of high database CPU after an API change, EF Core/SQL behavior, API boundaries, production risks, and practical debugging or design judgment.

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Why this question matters
Explain high database CPU after an API change with practical .NET data-access examples and production trade-offs. It matters in real backend work because query shape, indexes, transactions, locking, and data volume can turn harmless-looking code into a production bottleneck. The practical angle is high database CPU after an API change, EF Core/SQL behavior, API boundaries, production risks, and practical debugging or design judgment, tied to a concrete production decision.
I would first confirm the timing: when CPU increased, which deployment happened, and which endpoints or queries changed. Then I would identify the most expensive queries using database monitoring, application logs, or traces. I would compare generated SQL before and after if possible. I would also check query count, execution plans, row counts, and missing indexes.
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A common cause is a new filter, include, sort, or projection that changes the SQL shape. Adding a sort by an unindexed column on a large table can spike CPU. I would decide whether to mitigate by rolling back, disabling the feature, reducing traffic, adding an index, or changing the query. The trade-off is that adding an index in production may also lock or slow writes, so it needs careful rollout.

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Treating high database CPU after an API change as a syntax detail instead of a production behavior.
Not checking generated SQL, query plans, or actual data volume.
Testing only with tiny local data and assuming production will behave the same.
Fixing symptoms with caching, timeouts, or retries before understanding the data-access cause.

After a deployment, database CPU spikes and one API endpoint is suspected. How would you investigate?

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