How would you diagnose thread-pool starvation in a Java service?

Diagnoses queued work, blocked workers, nested submissions, common-pool contention, downstream waits, and misleading CPU symptoms.

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I look for rising request latency and queue depth while executor completion slows and workers remain blocked or occupied. Thread dumps show what workers are doing: waiting on I/O, locks, futures, or nested tasks submitted to the same saturated pool. CPU can be low because every worker is waiting, so low CPU does not rule starvation out. Pool, queue, downstream connection, and timeout metrics need to be correlated.

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Production concurrency diagnosis using thread dumps, executor metrics, latency, and workload ownership.

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Starvation occurs when tasks cannot obtain execution capacity even though work continues to arrive.
I look for rising request latency and queue depth while executor completion slows and workers remain blocked or occupied. Thread dumps show what workers are doing: waiting on I/O, locks, futures, or nested tasks submitted to the same saturated pool. CPU can be low because every worker is waiting, so low CPU does not rule starvation out. Pool, queue, downstream connection, and timeout metrics need to be correlated.
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A classic failure submits child tasks to the same fixed pool and then blocks every worker waiting for those children. Blocking calls inside the fork-join common pool can also delay unrelated CompletableFuture or parallel-stream work. Increasing threads may temporarily help but can overload the downstream dependency or increase context switching. The durable fix may separate workloads, remove blocking waits, bound admission, propagate timeouts, or enlarge a genuinely undersized resource with evidence.

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Ruling out starvation because CPU is low.
Only increasing thread count.
Ignoring nested submissions to the same pool.

How would you diagnose thread-pool starvation in a Java service?

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