Like many other programmers, I used to get hyped when somebody stars my project on GitHub. However, I find myself becoming less and less excited about it. It's not to say that I hate it. Nobody hates stars, and I'm still happy to see the number grows. Yet, I found something that values more to me: user feedbacks.

So what are user feedbacks? Almost anything you can think of counts: issues, comments, questions, suggestions, PRs, articles, usage, etc. Feedbacks show that people are actually using your project, and it can help you improve. A negative feedback is way better than no feedback, because it tells you what you should work on next.

Feedbacks usually comes as a natural result of exposure. More people know it thus more people use it. But sometimes, you got a bunch of stars, yet nobody gives you any feedback. That's the situation Cyberbrain is currently facing, which bothers me a lot. Tian said he has a similar feeling for VizTracer, also xintao for iRedis. Why is it bothering? Because it leaves you in a clueless state, and you keep doubting yourself: am I doing good? Am I doing bad? Why do people seem to be interested but don't really use it? Do I just sit and wait, or should I reach out to some of the people, but whom? What should I work on next, are the planned features what users need the most?

I don't have an answer, and I hope it's because people are already satisfied or shocked by Cyberbrain. Luckily, I did receive some feedback from my friends, and I want to say thank you to all of you. It matters a lot to me.

• 分支预测
• 优化技巧
• 实现
  • 编译中判断要不要开启 computed goto
  • ceval.c 中的实现

如果你读任何 (C)Python 源码分析的书或者文章，里面都会讲 Python 的 ceval.c 中有一个大 switch，会根据不同的 opcode 跳到相应的 case 去执行字节码。甚至 Anthony Shaw 的新书《CPython Internals》也是这么讲的。

然而实际上，CPython （在大部分情况下）早就不这么做了。这篇文章会解释为什么。下文中 Python 均指 CPython。

分支预测

为了解释清楚整个过程，我们必须从底层讲起。分支预测（Branch Prediction）是 CPU 必备的功能。准确的分支预测能大大提高指令流水线的性能，反之则导致性能损耗。

Python 刚发明的时候，每一个 bytecode 的确需要进行一次 switch 跳转。而 C 语言的 switch 语句会导致分支预测不准，因为 CPU 无法预测程序会跳到哪个 case（下一个指令）。如果不进行优化，每执行一条字节码指令都将导致一次分支预测错误，可以想象对性能的损耗有多大。

优化技巧

问题的关键就在于 switch...case 的跳转是个一对多的情况。想象你面对一道有十个选项的选择题，你不会知道哪个是正确答案。然而如果有十道题各一个选项，答案就都确定了。Python 采用的优化业无非也就是这么回事。

Python 用了一种叫做 computed goto 的技巧，依赖于编译器的 label as value 功能。详细原理大家就看链接里的解释吧。总之效果就是，可以把解释器主循环中的 switch...case 替换成 while loop，在每个 Python 的指令处理完之后，显示地用 goto 跳转到下一条指令。大概长这样：

int interp_cgoto(unsigned char* code, int initval) {
    /* The indices of labels in the dispatch_table are the relevant opcodes
    */
    static void* dispatch_table[] = {&&do_op1, &&do_op2, &&do_op3};
    #define DISPATCH() goto *dispatch_table[code[pc++]]

    int pc = 0;
    int val = initval;

    DISPATCH();
    while (1) {
        do_op1:
            return val;
        do_op2:
            val++;
            DISPATCH();
        do_op3:
            val--;
            DISPATCH();
    }
}

每个 goto 都是一次跳转，并且只有一个目标（下一条指令），这就让分支预测变得容易，从而性能也获得了极大提升（根据 Python 的注释，提升可达 15% ~ 20%）。

实现

下面讲一下 Python 是如何实现上面提到的优化的。

编译中判断要不要开启 computed goto

这部分。。非常蛋疼。我花了不少时间试图弄清楚，最后还是放弃了。这个回答里记录了我的一些发现。总之大概的意思就是，Python 允许用户通过 --with-computed-gotos 和 --without-computed-gotos 参数在编译的时候控制要不要开启 computed goto。默认是开启的。同时编译期间还会判断当前使用的编译器支不支持 computed goto（GCC, Clang 支持，MSVC 不支持）。如果两个条件都满足，在 ceval.c 中就会启用 computed goto。

ceval.c 中的实现

首先，label 是在 opcode_targets.h 中定义，并被 include 到 ceval.c 的。

这一段 定义了 computed goto enabled 和 disabled 时的跳转行为，我把简化过的代码贴在下面：

#if USE_COMPUTED_GOTOS
/* Import the static jump table */
#include "opcode_targets.h"

#define TARGET(op) op: TARGET_##op

#define DISPATCH() { \
    _Py_CODEUNIT word = *next_instr; \
    opcode = _Py_OPCODE(word); \
    oparg = _Py_OPARG(word); \
    next_instr++; \
    goto *opcode_targets[opcode]; \
}

#else

#define TARGET(op) op
#define DISPATCH() continue

#endif

嗯，简化了相当多的东西，但这能让我们更清晰地看出只有在 USE_COMPUTED_GOTOS 的情况下，DISPATCH() 才会用 goto 去跳到下一个 opcode 的 label。

下面是极简版的主循环代码：

for (;;) {
  switch (opcode) {
      case TARGET(LOAD_CONST): {
        // 处理 LOAD_CONST
        DISPATCH();
      }
      // 其它 opcode 省略了
  }
}

先看 computed goto 未启用时，把代码中的宏展开（替换 TARGET 和 DISPATCH）的结果：

for (;;) {
  switch (opcode) {
      case LOAD_CONST: {
        // 处理 LOAD_CONST
        continue;
      }
  }
}

嗯，就是这样了，很直接了当的 switch...case。

再看 computed goto 启用时，把代码中的宏展开的结果：

for (;;) {
  switch (opcode) {
    case LOAD_CONST:
    TARGET_LOAD_CONST: { // 注意这里多了一个 label
      // 处理 LOAD_CONST
      _Py_CODEUNIT word = *next_instr;
      opcode = _Py_OPCODE(word);
      oparg = _Py_OPARG(word);
      next_instr++;
      goto *opcode_targets[opcode];
    }
  }
}

可以看到，在处理完 LOAD_CONST 之后，通过 next_instr 拿到下一个 opcode，然后执行goto *opcode_targets[opcode]; ，跳到另一个由 opname 命名的 label（参见 opcode_targets.h ），比如 TARGET_STORE_NAME:。这里的关键点在于，代码直接跳转到了下一个指令对应的 label，并不经过 switch。这也就回答了文章一开始的问题：

Python 真的是靠一个 switch 来执行字节码的吗？

答案是：

只要 Python 启用了 computed goto （比如在 Mac 和 Linux 上），字节码的执行就不依赖 switch。

顺便一提，这个功能是在 Python 3.2 中变为默认开启的：

Computed gotos are now enabled by default on supported compilers (which are detected by the configure script). They can still be disabled selectively by specifying --without-computed-gotos.

• The mindset of "If you want it to improve, feel free to contribute" does not help
  • Documentation
  • Specification
  • Tools
  • Roadmap
• Summary

Today I read an article Under Discussion: The Performance of Python, and discussed it with some friends. I got some ideas from the discussion, so I decided to write an article.

My overall reaction to this article is: it points straight to some of the key factors that prevent Python to gain major performance improvements, thus really worth reading.

In the article, the author interviewed Python core developer Victor Stinner (VS) and experienced Python developer Julien Danjou (JD). Their names should be familiar to those who's been in the Python community for a while. Unlike some other articles that talk about the technical reasons of why Python is slow, this article focused on the efforts made and difficulties met to improve Python's performance.

To start, let me quote the part that I find most interesting:

VS: Now, there are different reasons behind Python’s slowness. The first is the official statement, which is that there is no such performance issue! Saying this is preventing us from working on the issue.

It blew my mind when I saw this. You know it, I know it, but as a core dev, you still need a bit of courage to say that in public. I appreciate what Victor said, and I'm 1000% agree with it.

JD: I agree with Victor. I don’t see a lot of people working upstream on Python who are trying to improve its performance, which is a shame considering the number of people who use the language and want better performance from it in general.

(Julien has clarified that he was referring to big companies not investing enough in Python.)

I think it's too harsh to say that. I respect the work people (especially core devs) did, and they've made Python better than it ever has been. My friend @uranusjr reacted more strongly. He said:

I don’t see why it’s a shame. If you want it to improve, feel free to contribute. Core developers don’t owe you anything.

This brings to the main topic I want to talk about today.

The mindset of "If you want it to improve, feel free to contribute" does not help

Don't get me wrong. I agree with the word. What I'm saying is, it's not helpful. I'll explain it later.

As you might know, I hosted a Python podcast in my free time, and we once invited Xiang Zhang, the only core developer from China. One thing he said in the show still strikes me until today. He said, Python's evolvement pretty much relies on 个人英雄主义 (individualistic heroism), and that's why it's so hard to make big improvements.

The real problem behind the scenes is the mindset. We're so used to relying on individualistic heroism, relying on "feel free to contribute", and almost forgot that contributing to a project as complicated as Python, is hard. Python does have a comprehensive Developer’s Guide, which is nice, but it's not enough. We need to change the mindset from "If you want it to improve, feel free to contribute" to "If you want it to improve, we'll make it easy for you to contribute". This is important because, once we build this new mindset, it is obvious that a lot more can (or should) be done other than having a good developer guide. I'll list a few that came to my mind.

Documentation

Again, it's documentation. So far I've made 4 commits to improve, but there are a lot more problems out there. Reading documentation is often the first step to start contributing. If the documentation itself is wrong or outdated, it would confuse people and scare them away. The more problems the documentation has, the less people care about fixing it, since it's "already broken", and that's the so-called "broken windows theory".

Specification

Bugs in documentation sometimes reveal a bigger problem: lack of specification. Personally, I think this is THE MOST important issue that Python needs to solve. To give an example, there is no formal definition for "value stack", but the word "stack" is used everywhere in bytecode's documentation, which represents the "value stack". Sometimes I got the feeling that the boundary between (C)Python's behavior and its implementation is vague, which makes it hard for both people to use and improve the language. Sure CPython is the reference implementation, but perhaps it can do better?

This topic is a bit beyond the scope of this article and I'm not an expert either, so I'll link to a recent discussion on Python Language Summit 2020: A formal specification for the (C)Python virtual machine.

Tools

When I was talking to Xiang Zhang, he mentioned he wish Python has gdb's functionalities built-into the language. I didn't understand why this is important, until one day I tried to use gdb on Mac to trace code execution in Python's VM. It was painful, and I didn't get it to work. Later I found that gdb has quirks on Mac and does not work well in certain cases. If Python provides this functionality, then perhaps Mac and Windows users are more likely to contribute?

Xiang also mentioned that he wish Python has hooks that provide memory usage and thread information. Could this be the consequence of lacking a formal specification, or simply because the community lacks interest in them? I don't know. But I do know these tools (if they exist) will not only benefit application developers but Python contributors as well.

Roadmap

This is the hardest topic, and I honestly don't feel qualified to talk about it. But it has been on my mind for quite a long time, so I might as well give my two cents.

If you observe the lifetime of a programming language, there are two typical trajectories. If the language failed to gain enough popularity in the first few years after it was created, it gradually fades away. If it did, often with a few killer apps or certain traits that other languages don't have at the time, it becomes "mainstream". Once it gets there, its lifespan suddenly grows from less than a few years to, say, at least 30 years, or even longer (remember the call for COBOL programmers?).

One problem arises with the long lifespan, that is, the language gradually becomes outdated. Why does this happen? Because human is progressing, errors are learned and new ideas are created. The creators of new languages learn from existing ones, avoid their mistakes, and adopt new ideas from academia and industry. Most will fail, but there are always a few who managed to create a better language. You'll probably argue that there's no language that's perfect in every way, and there are always tradeoffs, which is true. But hey, making better tradeoffs is also a technique, and people learn it as well. Meanwhile, we should not forget that old mainstream languages are also being improved constantly, but just as any other legacy system, having to keep backward compatibility slows this progress down, and even made certain improvements impossible.

Python is right at this stage. It has become one of the most popular programming languages, but some new languages are trying to take its place, little by little. It is unavoidable and we have to accept it, then take action. But how? C++ gives a great example. Take a look at this document:

Direction for ISO C++

And its abstract:

This is intended as a document updated as times go by and new facts and opinions emerge. It tries to articulate our aims for C++, the challenges faced, and some concrete suggestions for priorities.

This document differs from most papers by aiming for a global view and placing individual features in context, trying to avoid delving into technical details of individual proposals except where that “details” could affect the language as a whole or its tool environments.

To prevent this document from becoming toothless “pure philosophy,” we propose giving priority to specific areas of development and to specific proposals in those areas.

I know, people are complaining about C++'s evolvement being slow, but let's not forget C++ is probably the most complicated language with way more historical burden than languages like Python. Despite all that, I feel this kind of "long term road map" is what Python needs. Again, there are tradeoffs here. Python does not have a committee, and everybody pretty much works on its own, which allows it to move fast. However, I've seen many people say that, despite Python gaining more and more features, they don't feel like upgrading the version they've been using, because there isn't much big of a difference. Many say they'll stick with 3.6, cause that's a version with major improvements (f-string, compact dict). While I know this is not true, that recent Python versions do carry many improvements they should care, I still think it is dangerous that people start to think that way. Part of the reason, IMHO, is because Python does not have a real road map, therefore most contributors just tend to focus on low-hanging fruits. There's nothing wrong with making small improvements, but certain aspects, like performance improvements, is not small, and it can only happen if there's careful long-term planning and thinking, aka, a roadmap. A roadmap is not a magic scroll, it does not turn Python into a better language right away. In fact, it's a long process, and will probably take years before we can tell the difference. However, that's also the reason why Python needs it so urgently. Years can pass in the twinkling of an eye, if we don't do something now, in five years, the world can be totally different from what we see today.

Edit: Ruby is similar to Python in many ways, except it has a roadmap. See Matz - Ruby 3 and Beyond.

Summary

This article is way longer than I expected, so I'll do a quick summary. In our latest show, my friend who currently works in the Google New York office talked about what he thinks makes a good manager. He said a good manager should create an environment for you to work effectively. And I think that's also true for Python.