Vanilla Java

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 ...

While I am sceptical of using AI for release code, it has plenty of uses that previously weren’t practical, such as determining how easy your software is to use. If an AI can “figure it out” with a few hints, then you are on the right track. For me, the value of AI is what you learn using it. For more Techincal Information on Chronicle-FIX What AI Does Well and What It Doesn’t Claude and Codex are effective for producing idiomatic code; for low-latency code, it needs a significant body of example code. In this case, it was able to utilise sample code for benchmarks. If it was being used to write business logic, it would need the code to be mostly complete examples, and then it could write variations on that. If you were starting, it would be better to either; a) get it to write something functionally correct with the expectation you would rewrite it again manually, or b) write the code yourself and use AI to assist you in improving it. The AI Benchmark Trial I ga...

Peter Lawrey is an Australian/British software engineer and entrepreneur best known for work on ultra-low-latency Java systems and for leading the open-source OpenHFT libraries. He is the founder and chief executive of Chronicle Software, a London-based company whose technology is used in trading and market-infrastructure workloads. Lawrey is also a recognised Java community figure: he was named a Java Champion in 2015, has been described by conference organisers as having provided the most answers for the Java and JVM tags on Stack Overflow, and writes the long-running Vanilla Java blog. ( Chronicle Software , javachampions.org , qconnewyork.com , blog.vanillajava.blog ) Career Lawrey founded and leads Chronicle Software, which builds enabling technology for event-driven trading and market-data platforms. The company states that its software underpins systems at several tier-one banks; a 2024 press announcement similarly described Chronicle as supplying "8 of the t...

In a previous article I looked at one-shoting a solution to optimise code to show the variation in different AI. Thsi is the not the best way to get what you want however. More often you need to either refine the prompt or give feedback. After one-shoting the same prompt on multiple AI, I have created a refined prompt based on the various concerns with previous results. The prompt Based on the results in a previous run Asking multiple AI to optimise the same code Suggest how to implement this more optimally using low latency techniques to minimize any objects created. ## Use - a ThreadLocal for temporary data. - simple maths rather than a library, add comments for clarity if needed. - offset in the form ±hh, ±hhmm, or ±hhmmss, using the shortest form that does not lose information, where hh, mm, and ss are the hours, minutes, and seconds east (+) or west (-) of UT - return a `intern()` String. ## Don't use - String.format - String operations that create objects. - any co...

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) { ...

TL;DR Human-in-the-loop is essential; AI offers probability, not certainty. AI excels at word-smithing , so spend more time on documentation and context. Leverage diverse AI models for varied research, improvements, and analysis. Be wary of deskilling : if AI makes a task trivial, agents may soon replace it. You should feel like you are testing the boundaries of what AI is capable of for at least some tasks. The Problem AI’s proficiency in handling routine coding allows human engineers to dedicate more time to strategic activities such as system design, architectural planning, intricate requirement elicitation, and the rigorous evaluation of application performance across multifaceted metrics. Tools often amplify underlying behaviours and failures — Rob Lambert Value is increasingly found not in rote knowledge, which AI can often provide, but in the capacity to frame complex problems effectively for AI, critically evaluate its probab...