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Linux 内核 Git 历史中最大、最奇怪的提交
The Biggest and Weirdest Commits in Linux Kernel Git History
2017 — 2020,它一次次回到首页
黑点是上首页(≥ 50 分)的那次投稿,空心点是没冲上去的投稿,点越大分数越高。
导读
作者统计并分析了 Linux 内核 Git 历史上最大、最奇特的提交:包括体量惊人的代码变更、拥有数十个父提交的“章鱼合并”(octopus merge)、多个根提交等罕见案例,并配图展示这些极端提交的分布规律。文章兼具技术考据与趣味性,展示了 Git 历史中不为人知的角落。
为什么经久不衰:它把枯燥的版本历史变成有趣的考古,揭示 Git 和大型开源协作的极限形态,多年后重读依然新奇有料。
HN 评论精选
批评文中用双对数直线拟合幂律的做法在统计上不可靠
Clauset、Shalizi 和 Newman 2007 年的论文对物理学家在双对数图上画直线拟合幂律分布这种“惯用小把戏”颇有微词:偏差很大。幂律分布的另一难点是,要严格判定它确实是幂律,所需的数据量有时大得惊人。他们建议用 MLE 加 Kolmogorov-Smirnov 检验,所以会判定很多“可能是幂律”的东西不是幂律。
curuinor查看英文原文
Clauset Shalizi Newman 2007 has not-nice things to say about the classic physicist's idiot trick of fitting power law distributions by drawing a straight line on a log-log graph: it's got huge bias. https://arxiv.org/abs/0706.1062 However, the other difficult thing about power law distributions is that the dataset size requirements for proper determination of the fact that it's a power law distribution are occasionally incredibly difficult. So their critique is very strong, given the comparative lack of data. It is often the case that computer systems, with the overflowing reams of data, are still not enough. Note that the paper I cited up there suggests MLE and then a Kolmogorov-Smirnoff test, so it'll say a lot of things aren't power laws that could well be. Another way to look at it is from a more geometric point of view. The metric entropy of any generic system of variables is defined as the sum of the positive Lyapunov exponents: and as an "entropy" that quantity does have a lot of commonalities with the other entropies. But to have positive Lyapunov exponents is often to have a chaotic dynamics, so it could just be conjectured that the time series of commits and merge octopus sizes in kernel git history is chaotic, so the evolution of the time series will be fractal in nature. But it's also really fucking hard to confirm or deny that one, because there are varied and strange definitions of chaos itself and the methods that have been suggested to measure Lyapunov exponent in real systems are arcane and difficult. You could try some synchronization methods, but they remain arcane and crap. Fractal measurement methods are also shitty and full of dark magic. One neat little trick might be to discretize the series, symbolic dynamics-style (it's already discretized but discretize further, into like percentiles or something) and run it through one of the dynamical machine learning dealies to see if there's patterns. Not too much literature on that but it's a thing that some randoes in like 2004 or something did
指出内核史上首个多父合并是个重复父提交的错误
另一个有趣的冷知识:内核历史上第一个多于两个父提交的合并其实是个失误——第二和第三个父提交是同一个提交。
geofft查看英文原文
Another interesting piece of trivia: the very first more-than-two-parent merge in the kernel history is a mistake. The second and third parents are the same commit. commit 13e652800d1644dfedcd0d59ac95ef0beb7f3165 Merge: 4332bdd 88d7bd8 88d7bd8 Author: David Woodhouse <dwmw2@shinybook.infradead.org> Date: Sun May 8 13:23:54 2005 +0100 Merge with master.kernel.org:/pub/scm/linux/kernel/git/torvalds/linux-2.6.git
提供 Linus 关于 66 父合并的原始邮件链接
这里有一条 Linus 本人关于那个 66 父提交合并的邮件列表讨论链接。
cpobrien查看英文原文
There is a mention of the 66 parent merge from Linus himself: http://marc.info/?l=linux-kernel&m=139033182525831
指出章鱼合并正是 GitHub Octocat 吉祥物的灵感来源
我从没听说过章鱼合并,还好奇它们是不是 GitHub 吉祥物 Octocat 的由来——结果还真是!
fredley查看英文原文
I had never heard of Octopus merges, and wondered if they're the reason why the GitHub mascot is Octocat. Turns out they are! http://cameronmcefee.com/work/the-octocat/
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