Linkedin Group for Core Java Performance Professionals

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Why You Should Tune Code Before Your Garbage Collector

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Optimising your memory allocations in Java could make far more difference than your choice of Garbage Collector and may even change which is the best garbage collector. In this post I look at a simple event to response latency benchmark, MarketDataSnapshot to NewOrderSingle at 50K/s for 30 minutes using JLBH to test Chronicle-FIX. The goal is to compare a system which is doing redundant work (in this case logging each message using SLF4J), compared with not logging (Chronicle-FIX records every message internally using Chronicle Queue) and how this changes the choice of Garbage Collector For the p99 (worst 1 in 100) the choice of Garbage Collector makes a different on par with optimising how loggin is done However, for the p99.99 (worst 1 in 10,000) optimsing how the logging is done is orders of magnitude more signifciant than the choice of Garbage Collector Unoptimised Benchmark This takes the optimised benchmark and adds one SLF4J log line of just ...
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Asking multiple AI to optimise the same code

As different AIs are implemented differently, they don't all provide the same answer, nor do they consistently outperform one another. The best approach is to use multiple AI and pick the one you like best. My goal here is not to declare a winner based on one example, but instead to show the variety of answers you can get with different AI. I asked each AI to Suggest how to implement this more optimally private static String formatOffset(int millis) { String sign = millis < 0 ? "-" : "+"; int saveSecs = Math.abs(millis) / 1000; int hours = saveSecs / 3600; int mins = ((saveSecs / 60) % 60); int secs = (saveSecs % 60); if (secs == 0) { if (mins == 0) { return sign + twoDigitString(hours); } return sign + twoDigitString(hours) + twoDigitString(mins); } return sign + twoDigitString(hours) + twoDigitString(mins) + twoDigitString(secs); } private static String twoDigitString(int value) { ...
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Demystifying Java Object Sizes: Compact Headers, Compressed Oops, and Beyond

Introduction Measuring an object’s size in Java is not straightforward. The platform encourages you to consider references and abstractions rather than raw memory usage. Still, understanding how objects fit into memory can yield significant benefits, especially for high-performance, low-latency systems. Over time, the JVM has introduced optimisations like Compressed Ordinary Object Pointers (Compressed Oops) and, more recently, Compact Object Headers. Each of these can influence how large or small your objects appear. Understanding these factors helps you reason about memory usage more concretely. Measuring Object Sizes In principle, you can estimate an object’s size by creating instances and observing changes in the JVM’s free memory. However, you must neutralise certain factors to get consistent results. For example, turning off TLAB allocation ( -XX:-UseTLAB ) makes memory usage more directly observable. Repeated measurements and median calculations can reduce the im...
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