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If every worker submits a task to the same bounded executor and synchronously waits for that task, no worker remains to run queued children. This is thread-pool starvation, not necessarily a monitor deadlock. Avoid blocking composition on the same pool, separate workloads, or use asynchronous completion and bounded concurrency.

CompletableFuture represents a value that may complete later, with dependent stages triggered on completion. Async methods without an explicit executor commonly use a shared pool; blocking calls there can starve unrelated work. Prefer non-blocking composition, choose an executor suited to the task, set timeouts, and observe exceptional completion rather than calling join() throughout request paths.

Look for parent-owned registries, executor queues, thread locals, context class loaders, and callbacks. An unreachable cycle can be collected; a path from a long-lived root cannot.

Each platform thread consumes native resources, including stack reservation, and excessive runnable threads add scheduling and context-switching overhead. The service may hit native-thread creation errors or degrade before that point. Bound executors, measure queue depth and task latency, and apply backpressure rather than creating one thread per unbounded unit of work.

ForkJoinPool uses worker-local deques and lets idle workers steal tasks from others, which helps recursive, CPU-bound workloads with many small tasks. Long blocking I/O can occupy workers and reduce progress. Use a suitable dedicated executor or managed blocking strategy for blocking operations, and avoid creating huge numbers of tiny tasks with excessive scheduling overhead.

Multi-stage a build lets you compile in one environment and then copy only the final artifact into a slim runtime image. This reduces image size, shortens deployment time, lessens the attack surface, and makes it easier to reason about what the service actually needs to run. It is a packaging discipline, not just a size optimization.