An Allocation Stall in concurrent Garbage Collection happens when an application creates objects faster than the system can free up memory, causing a brief pause in object creation. This can be caused by slow memory cleanup, high object creation rates, or fragmented memory. Solutions include adjusting heap size, increasing the number of cleanup threads, and improving memory use.
The article describes a production issue where an AWS EC2 application instance became unresponsive while others continued functioning. Investigation revealed repeated "TCP: out of memory" messages from the dmesg command. After an unsuccessful server restart, the instance was rebooted, resolving the issue. Kernel properties were identified to optimize TCP memory limits.
This article discusses useful JVM arguments for handling OutOfMemoryError, which assist in memory troubleshooting. It covers -XX:+HeapDumpOnOutOfMemoryError for capturing heap dumps, -XX:OnOutOfMemoryError for executing scripts, -XX:+CrashOnOutOfMemoryError and -XX:+ExitOnOutOfMemoryError for abrupt application exits, highlighting the importance of graceful handling in both cases.
The article outlines a three-step automated approach to troubleshoot OutOfMemoryError in applications. First, it suggests capturing heap dumps using specific JVM arguments to gather memory data. Next, it recommends restarting the application via a custom script to prevent instability. Lastly, it discusses analyzing heap dumps with tools or an API for effective diagnosis.
The content discusses the importance of monitoring micrometrics to effectively forecast application performance and availability, particularly focusing on memory, thread, network, and storage metrics. By analyzing metrics like garbage collection throughput, thread states, and IOPS, organizations can identify potential issues early, enhancing application reliability and performance.
Java.lang.VirtualMachineError is an exception thrown by the JVM due to internal errors or resource limitations. It includes four types: OutOfMemoryError, StackOverflowError, InternalError, and UnknownError, each with distinct causes. Understanding these errors is vital for diagnosing and resolving potential issues in Java applications, particularly for DevOps professionals.
The article discusses the limitations of CI/CD pipelines, which primarily analyze macro-level metrics, in identifying performance issues like OutOfMemoryError. It emphasizes the importance of micrometrics—such as garbage collection throughput, thread states, and memory waste—in monitoring applications to preemptively address performance problems and improve software quality in production.
Procedures to diagnose and solve all the 5 types of OutOfMemoryErrors that happen within the JVM heap are same. In this article, let's explore how to diagnose these 5 types of OutOfMemoryErrors.
The article addresses sudden CPU spikes in Java applications, often caused by repeated Full Garbage Collections (GC) due to memory leaks and infinitely looping threads. It provides troubleshooting strategies using tools like gceasy.io for analyzing GC logs and fastthread.io for identifying looping threads, along with real-world examples demonstrating effective resolutions.