性能测量误差导致的结果不可靠
问题概述
用户在使用自研的性能测量工具时，发现测得的吞吐量数据与实际不符，因未考虑内存和网络处理的开销，导致结果失真，影响后续决策。
痛点标签
- 性能测量工具在高负载下给出错误的统计数据
- 缺乏对内存和网络处理开销的精确分析
- 工具链的可靠性和准确性不足
待验证问题
- 你是否曾经遇到类似的性能测量误差问题？
- 对持久化模型在实际应用中的效果是否感兴趣？
- 你会如何改进现有的测量工具以获得更准确的数据？
证据
原话
I have something like this very recently.About a month ago I wrote a perf tool for my application to measure its network message speed. It showed I could send 3,250,000 WebSocket messages per second in my TypeScript application. If I moved each test of an experiment into its own call stack I could send 3,250,000 messages as fast as 0.002 seconds. Claude confirmed the number.It was about two weeks later I learned the number is wrong. The number would be correct if this were just a linear instruction processing problem. Actually the processing time, CPU and network handling, is about 0.08%. The other 99.92% of processing time is in memory. What was happening is the perf tool would push messages into a queue on the socket from a JavaScript loop, but the speed to process these messages and dra
原话
> For a deeper, more technical dive, check out the paper on arxiv and code on github.These links at the top point to the same page, not the paper or the code!
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