An API shows memory spikes and expensive collections during large requests. How would you check for LOH pressure?

How would you diagnose Large Object Heap pressure in an API?

Turns LOH theory into an API investigation around large payloads, buffers, serialization, and concurrency.

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I would look for large allocations tied to specific endpoints or jobs. Large byte arrays, strings, serialized payloads, and large lists are common causes. I would check metrics around heap size, LOH size if available, GC pauses, request size, and concurrency. Then I would inspect code paths that buffer full files or responses and consider streaming, paging, chunking, or moving heavy work to a background job.

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LOH diagnostics, endpoint correlation, payload size, serialization, streaming, and memory mitigation.

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LOH pressure is often caused by normal product features at larger scale: exports, uploads, reports, or large JSON payloads. This question checks whether the answer can move from runtime concept to endpoint behavior.
I would look for large allocations tied to specific endpoints or jobs. Large byte arrays, strings, serialized payloads, and large lists are common causes. I would check metrics around heap size, LOH size if available, GC pauses, request size, and concurrency. Then I would inspect code paths that buffer full files or responses and consider streaming, paging, chunking, or moving heavy work to a background job.
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The investigation starts with correlation. If memory spikes during export or upload traffic, I would trace those requests and inspect allocation profiles. Serialization can create large strings or buffers, and data access can create large lists before serialization even begins. I would estimate worst-case memory as payload size times concurrent requests, plus copies created by parsing or serialization. If LOH pressure is confirmed, reducing copies and bounding concurrency usually helps more than tweaking GC settings.

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Calling LOH pressure a leak without evidence.
Ignoring serialization and duplicate buffering.
Testing only one request instead of concurrent large requests.
Trying GC settings before changing the data flow.

An API shows memory spikes and expensive collections during large requests. How would you check for LOH pressure?

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