The article discusses troubleshooting deadlocks and hung threads in Java applications, highlighting essential tools like JStack, JConsole, and VisualVM that aid in diagnosis. It explains deadlock situations, provides sample code, and details other applications such as IBM TMDA and fastThread for advanced analysis, ensuring developers can efficiently resolve issues.
Analyzing thread activity is crucial for troubleshooting Java applications. Thread dump analyzers help identify performance issues, deadlocks, and resource contention in multithreaded environments. Various tools, both free and paid, are available for effective analysis, with fastThread being among the most comprehensive options for both basic and advanced needs.
Java EE architecture is known for its scalability and powerful features, but it can be difficult to solve performance issues in production. Analyzing thread dumps is crucial for identifying problems like high CPU usage or deadlocks. This process helps find the root causes faster, as shown in a case study where a financial company solved similar issues.
Microservices architecture improves scalability and flexibility but can also make troubleshooting more complicated, especially when it comes to performance. Analyzing thread dumps is essential for finding problems like deadlocks and CPU overload. A real-life example shows how analyzing thread dumps helped fix a major travel company’s application that became unresponsive due to limited resources.
Capturing and analyzing thread dumps is important for identifying Java performance issues like high CPU usage and deadlocks. It's best to capture multiple dumps at regular intervals when performance problems occur, and to also collect related information like CPU usage and garbage collection logs. Safely storing these dumps is essential to protect any sensitive data they might contain.
Thread dumps are important for finding performance issues in Java applications. They give a snapshot of what each thread is doing, including details like the thread's state and its actions. By studying these dumps, you can quickly identify performance problems and find the root cause to fix them efficiently.
The blog post discusses a specific error, 'Uncaught RangeError: Maximum call stack size exceeded', encountered while loading the fastThread web application in Microsoft Edge. The issue arose due to deeply nested JSON during flame graph generation, which did not affect Chrome or Firefox. An interim solution involved limiting JSON nesting for Edge users while ensuring compatibility across other browsers.
Concurrency in software development can enhance performance but also lead to deadlocks, which block threads from proceeding. This post discusses a real-world deadlock scenario caused by Java's "parallelStream" and how it was resolved using the troubleshooting tool yCrash. Switching from "parallelStream" to a regular stream eliminated the deadlock issue.
This post outlines a non-intrusive method for diagnosing CPU spikes in production environments using the yCrash data script. It captures 360° data from JVM applications with minimal overhead and allows for analysis of CPU consumption by threads. The process includes data capturing, uploading, and generating a unified root cause analysis report.