The java.lang.String#intern() method can significantly reduce memory usage by eliminating duplicate strings in Java applications. A comparison of two programs—one utilizing intern() and the other not—demonstrated that the intern() method reduced memory consumption from 1.08GB to 38.37MB at the cost of increased response time.
The intern() function in Java's String class optimizes memory usage by managing a pool of string objects in the JVM. When invoked, it checks for existing strings, reusing them if present to eliminate duplicates. While beneficial for memory efficiency, using intern() can negatively impact application response time compared to other methods like string deduplication.
Garbage Collection events predominantly occur in the Java application layer, termed 'User' time, where the Garbage Collector identifies and marks active objects and evicts unreferenced ones. 'Sys' time represents the time spent in the Operating System/Kernel for memory allocation, deallocation, and disk I/O activities. Overall 'CPU' time combines both 'User' and 'Sys' time.
The intern() function in Java's String class helps eliminate duplicate string objects, reducing memory usage by storing interned strings in the JVM's heap region. This post includes practical examples, performance observations from a sample program, and highlights the significance of enabling garbage collection logging for memory management insights.
This post compares the performance of HashMap, Hashtable, and ConcurrentHashMap through practical examples. It recommends ConcurrentHashMap for its thread-safe implementation, despite being marginally slower than HashMap. Testing showed HashMap performed best, but was not thread-safe, while Hashtable was significantly slower due to its synchronization constraints.
This post discusses the advantages of setting the initial heap size equal to the maximum heap size for Java applications running on JVM. It highlights benefits like improved application availability, enhanced performance, reduced startup time, and unchanged computing costs. The article argues that this practice is particularly beneficial for enterprise applications.
Analyzing garbage collection (GC) logs offers benefits such as reduced pause times, lower cloud costs, and improved capacity planning. This post outlines the process of enabling GC logs, the ideal measurement duration and environment, and tools for analysis. Key tools include GCeasy and IBM's GC visualizer for effective optimization.
The post discusses the consequences of under-allocating memory in applications, such as degraded response times and OutOfMemoryError occurrences. It emphasizes proactively monitoring Garbage Collection behavior through logs to identify memory allocation issues. Analyzing patterns in GC logs can help distinguish between high object creation due to traffic spikes and potential memory leaks.
The author analyzes various Garbage Collection (GC) patterns observed in applications using GCeasy. Key patterns include healthy saw-tooth behavior, heavy caching, memory leaks, and consecutive full GCs, each indicating different performance issues. Understanding these patterns helps diagnose application health and optimize memory usage to prevent errors like OutOfMemory.