The article discusses the recurring issue of CPU spikes in applications due to threads entering infinite loops, particularly with non-thread-safe implementations like HashMap. To diagnose the issue, capturing thread dumps can help identify problematic threads. Solving this typically requires replacing HashMap with thread-safe alternatives like ConcurrentHashMap, akin to running on a treadmill with no progress.
The content discusses how transitive blocking among threads can render applications unresponsive. A real-world example from a travel application illustrates this, where a thread was blocked by another, causing a complete halt for 42 threads due to a bug in an APM monitoring agent. Upgrading the agent resolved the issue.
When an application experiences a performance bottleneck, threads may accumulate and exhibit identical stack traces, indicating potential issues. Examples include external service slowdowns, lock acquisition failures, and non-terminating loop conditions. Investigating these repetitive stack traces is crucial for identifying and resolving performance problems, as demonstrated in a major B2B application's thread dump.
When multiple threads in an application are stuck in a single method, it indicates potential issues like poor data sources or deadlocks. An example involved an application connecting to Cassandra DB, causing an OutOfMemoryError due to excessive threads in a specific method. Resolving the DB space issue restored normal operations.
Thread count is crucial in thread dump analysis, indicating application health during normal operations and helping assess issues, new releases, and performance diagnosis. A spike in thread count suggests problems like leaks, while comparisons to previous counts ensure the new version's reliability. Excessive threads may lead to "OutOfMemoryError" due to memory constraints.
To diagnose JVM issues, capturing three thread dumps at 10-second intervals is recommended. An example highlighted a thread leak due to an Oracle JDBC Driver bug from 2011, leading to excessive thread creation. In this case, 1700 threads entered RUNNABLE state, emphasizing the importance of observing thread behaviors for potential problems.
In Java, objects with a finalize() method are handled uniquely during garbage collection. They are placed in a queue for processing by a low-priority Finalizer thread. If this thread blocks, memory consumption can spike, leading to OutOfMemoryError. Thus, careful implementation of finalize() is crucial to avoid trapping the Finalizer thread, likened to a Leprechaun Trap.
The post discusses the management of garbage collection (GC) threads based on different algorithms like Parallel GC, CMS, and G1 GC. It emphasizes the importance of configuring the number of threads to avoid performance issues, providing formulas for calculating defaults based on processor count. Excessive GC threads can hinder application performance.