Memory analysis is crucial in identifying inefficiencies that lead to significant wastage in enterprise applications. Key issues include over-allocated RAM and JVM memory, along with inefficient coding practices. Addressing these can lower costs, reduce garbage collection pauses, and improve application availability, ultimately optimizing performance and resource usage.
The article explains the differences between heap and stack memory in Java. The heap is a shared storage area for all application classes, managing objects and instance variables, while the stack stores context for active methods, retaining local variables and pointers to objects. Each has distinct cleanup processes and memory management roles.
Memory leaks in applications can severely impact performance and cause crashes. This article discusses heap dumps, which are essential for diagnosing such leaks. It explains how to take heap dumps, analyze them with tools like HeapHero, and identify problematic objects, highlighting the complexity of debugging memory-related issues and emphasizing the need for proper management.
To handle large data volumes in Java, careful planning and coding are essential to prevent Out of Memory errors. Factors include understanding JVM memory types and assessing RAM needs against performance. Efficient memory use, effective garbage collection, and load testing are crucial, along with ongoing monitoring to address potential issues in production systems.
OutOfMemory errors can disrupt both testing and production phases. This article explains how to diagnose and resolve these errors in Java by understanding the JVM memory model and types of OutOfMemoryErrors. It emphasizes gathering diagnostic artifacts, utilizing JDK tools, and considering memory adjustments or code fixes to prevent crashes.
The post discusses memory leaks caused by improperly handled keys in Java Collections, particularly HashMap. It illustrates how mutating keys leads to OutOfMemoryError and outlines steps for diagnosing such issues, including capturing and analyzing heap dumps. The solution involves preventing key mutation and employing diagnostic tools effectively.
This post discusses the use of ThreadLocal variables in Java and highlights their potential to cause memory leaks if not managed properly. An example program demonstrates how infinite thread creation can lead to OutOfMemoryError. It emphasizes the importance of capturing and analyzing heap dumps to diagnose leaks and recommends removing ThreadLocal values to prevent issues.
This post discusses the implicit inheritance in Java from java.lang.Object and the limitations of the finalize() method which can lead to OutOfMemoryError. It highlights alternatives for resource cleanup, such as try-with-resources and java.lang.ref.Cleaner, and emphasizes the importance of proper implementation to prevent application failures.
This post looks into the rare 'java.lang.OutOfMemoryError: reason stack_trace_with_native_method' error, its causes, and solutions. It discusses JVM memory areas, heavy use of native methods, and recursive native method calls as common causes. Solutions include analyzing stack traces and using OS tools like DTrace, pmap, and pstack for diagnosis.
This post discusses the Java OutOfMemoryError, with a focus on the rare 'Kill process (Java) or sacrifice child' error. It explains the causes, solutions, and ways to diagnose this issue. The post highlights the importance of knowing JVM memory areas and provides practical troubleshooting steps.
