(本文最初发布在掘金 https://juejin.cn/post/7242312269887914041)
学习 Linux 内核的第一步就是下载 Linux 内核代码, 然后进行编译与运行.

下载内核源代码

内核源码除了 Linus 的官方 https://github.com/torvalds/linux git, 还有各家发布版的 Linux 内核. 最好去发布版的内核去下载, 这样编译过程中不容易出错.

我的 Linux 机器是 Ubuntu 22.04 TLS, 所以根据官方文档: https://wiki.ubuntu.com/Kernel/SourceCode, 可以 clone git 下载:

$ git clone --depth 1 -b master git://git.launchpad.net/~ubuntu-kernel/ubuntu/+source/linux/+git/jammy

这里为了减少下载文件的数量和大小, 选择只下载最新的代码, 只选择了master分支. 下载到 jammy 文件夹.

编译配置

编译之前先要安装编译内核需要的各种安装包:

$ sudo apt update 
$ sudo apt upgrade -y
$ sudo apt install git fakeroot build-essential ncurses-dev xz-utils libssl-dev bc flex bison libelf-dev -y 

配置并编译

$ cd jammy
# 此时并没有 .config 文件, 执行下面的命令会让你选择配置, 然后保存.
$ make menuconfig
# 生成 .config 文件
$ ls -lah .config
-rw-rw-r-- 1 sre sre 256K Jun  2 20:58 .config

我们可以看到 .config 文件里面的各种配置项目, 比如:

CONFIG_VERSION_SIGNATURE="Ubuntu 5.15.0-72.79-generic 5.15.98"
CONFIG_HAVE_KERNEL_GZIP=y
CONFIG_TSL2583=m

有些配置项表示一个纯文本的配置, 比如上面的版本号. 另外一些后面是 =y, =m 或者=n.

1. =y：当选项被设置为=y时，表示该选项将作为内核的一部分编译进内核映像（静态编译）。这意味着相关的功能将始终可用，并包含在生成的内核映像中。当系统启动时，这些功能将立即可用，无需加载额外的模块。选择=y是在构建内核时将特定功能编译到内核中的一种方式。
2. =m：当选项被设置为=m时，表示该选项将作为可加载模块编译（动态编译）。这意味着相关的功能将编译为独立的模块文件（通常是以.ko为扩展名），并在需要时由内核加载。使用=m选项可以将特定功能作为模块构建，以便在运行时根据需要加载和卸载。

选择=y或=m取决于您对系统需求的权衡。如果您确定某个功能始终需要在内核运行时可用，并且不希望依赖额外的模块加载过程，则选择=y。如果您希望能够根据需要加载和卸载某个功能，并且不会一直使用该功能，则选择=m。

请注意，对于某些选项，可能还有其他设置，例如=n，表示将完全排除该功能的编译。这意味着相关的功能将在内核映像和模块中都不可用。选择特定的设置取决于您的需求和系统配置。

文字界面配置如下, 最后选择 Save 到 .config 文件.

编译:

$ make all -j 4 # 使用 4个线程编译, 可能要等很久, 最后生成内核文件 arch/x86/boot/bzImage
...

$ ls -lah arch/x86/boot/bzImage
-rw-r--r-- 1 root root 11M Jun  5 08:59 arch/x86/boot/bzImage
Kernel: arch/x86/boot/bzImage is ready  (#1)
$ make help # 查看更多命令

如果遇到下面的出错:

sre@sre:~/work/exp/jammy$ make all -j 8
  ...
make[1]: *** No rule to make target 'debian/canonical-certs.pem', needed by 'certs/x509_certificate_list'.  Stop.
make[1]: *** Waiting for unfinished jobs....
make: *** [Makefile:1900: certs] Error 2

可以参看 https://askubuntu.com/questions/1329538/compiling-the-kernel-5-11-11 去掉里面证书的部分:

# 可以看到当前的配置, 改成=“”
sre@sre:~/work/exp/jammy$ cat .config | grep CONFIG_SYSTEM_TRUSTED_KEYS
CONFIG_SYSTEM_TRUSTED_KEYS="debian/canonical-certs.pem"

测试启动内核

首先安装 qemu, 然后启动内核:

$ sudo apt install qemu-system-x86 -y
$ qemu-system-x86_64 -kernel bzImage -append "console=tty0 console=ttyS0" -nographic
SeaBIOS (version 1.15.0-1)
iPXE (https://ipxe.org) 00:03.0 CA00 PCI2.10 PnP PMM+07F8B340+07ECB340 CA00
Booting from ROM...

Kernel Offset: disabled
---[ end Kernel panic - not syncing: No working init found.  Try passing init= option to kernel.

可以看到最后内核 panic, 因为没有任何 root 文件系统, 也没有 init 代码. 这时候, 我们可以通过 -initrd 来启动, 里面可以包含一个 busybox.

如何制作一个 initrd

使用 https://github.com/aweeraman/kernel-utils 提供的工具, 按照说明文档, 执行第一步 ./mk-initrd 就能生成 initramfs.cpio.gz, 里面包含了 initrc 和 busybox.

再次测试启动:

$ qemu-system-x86_64 -kernel bzImage -initrd initramfs.cpio.gz -append "console=tty0 console=ttyS0" -nographic

#这次成功启动

如何做一个最小的内核并启动进入命令行

制作最小内核

在 Linux 内核源代码根目录执行 make help, 你能看到各种有关配置的子命令, 比如:

1. defconfig: New config with default from ARCH supplied defconfig.
2. allnoconfig: New config where all options are answered with no.
3. allyesconfig: New config where all options are accepted with yes.
4. tinyconfig: Configure the tiniest possible kernel.

这里我们关注的是 tinyconfig, 于是我们先清理一下, 然后使用 tinyconfig 生成 .config 文件, 然后制作 image, 最后使用 qemu 去执行:

$ make mrproper
$ make tinyconfig
$ make all -j 8
    Kernel: arch/x86/boot/bzImage is ready

$ qemu-system-x86_64 -kernel bzImage  -append "console=tty0 console=ttyS0 init=/bin/sh" -nographic
SeaBIOS (version 1.15.0-1)
iPXE (https://ipxe.org) 00:03.0 CA00 PCI2.10 PnP PMM+07F8B340+07ECB340 CA00
Booting from ROM..

最后发现日志停在 Booting from ROM.. 就没有任何消息了.

这是因为 tinyconfig 包含的驱动或者配置太少, 导致没有后续输出, 我们需要在 tinyconfig 的基础上添加一些配置.

添加配置

添加配置使用 make menuconfig 来修改, 最后保存就好.

1. 64-bit kernel

   ![image.png](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/346f42e3683744eebec1ec2a96ba01d1~tplv-k3u1fbpfcp-watermark.image?)
   

2. Device Drivers -> Character devices -> Enable TTY

   ![image.png](https://p6-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/23c59d42c58343118eaf6d54d289cedf~tplv-k3u1fbpfcp-watermark.image?)
   

3. Device Drivers -> Character devices -> Serial drivers -> 8250/16550 and compatible serial support -> Console on 8250/16550 and compatible serial port

   ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/cb31b8cd50714a909a5353d19e5e2f9c~tplv-k3u1fbpfcp-watermark.image?)
   

4. General setup > Configure standard kernel features (expert users) -> Enable support for printk

   ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/e0fd06f1974e4357b643dc610f8f485b~tplv-k3u1fbpfcp-watermark.image?)
   

保存上面配置, 并且做一个新的image:

$ make all -j 8 
$ qemu-system-x86_64 -kernel bzImage  -append "console=tty0 console=ttyS0 init=/bin/sh" -nographic

SeaBIOS (version 1.15.0-1)
iPXE (https://ipxe.org) 00:03.0 CA00 PCI2.10 PnP PMM+07F8B340+07ECB340 CA00
Booting from ROM..
Run /bin/sh as init process
Kernel panic - not syncing: Requested init /bin/sh failed (error -2).
Kernel Offset: disabled
---[ end Kernel panic - not syncing: Requested init /bin/sh failed (error -2). ]---

可以看到 Kernel panic, 因为我们只是启动 kernel, 没有root 文件系统, 也没有使用 initrd 的ramdisk.

修改config 支持 initrd

使用 make menuconfig 继续修改

1. General setup -> Initial RAM filesystem and RAM disk (initramfs/initrd) support

   ![image.png](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/8cd83a5bdb5545fe97193ebea783be69~tplv-k3u1fbpfcp-watermark.image?)
   

2. Executable file formats -> Kernel support for ELF binaries

   ![image.png](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/3850c23d477045d98070bfad60d79333~tplv-k3u1fbpfcp-watermark.image?)
   

保存, 然后 make all -j 8 再次制作image, 然后运行:

$ qemu-system-x86_64 -kernel bzImage -initrd initramfs.cpio.gz -append "console=tty0 console=ttyS0 init=/bin/sh" -nographic

Run /init as init process
Failed to execute /init (error -8)
Run /bin/sh as init process
/bin/sh: can't access tty; job control turned off
/ # input: ImExPS/2 Generic Explorer Mouse as /devices/platform/i8042/serio1/input/input2
clocksource: tsc: mask: 0xffffffffffffffff max_cycles: 0x311fac54234, max_idle_ns: 440795352581 ns
clocksource: Switched to clocksource tsc
uname -a
Linux (none) 6.4.0-rc5+ #3 Wed Jun  7 08:19:32 PDT 2023 x86_64 GNU/Linux
/ # ls /bin/
...

/ # ps aux
PID   USER     TIME  COMMAND

可以看到 kernel 启动后执行了 /bin/sh, 我们使用 uname能看到 kernel 的版本号, 但是ps 没有任何输出. 那是因为我们没有挂载 proc 文件系统. 同时执行挂载 proc 文件系统的脚本在 initramfs.cpio.gz 内部的 init 文件里, 它是一个 shell, 所以要使 kernel 支持 shell 的 #!.

再次通过 make menuconfig 修改配置:

1. Executable file formats -> Kernel support for scripts starting with #!

   ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/2c8dbb2642e347029d59397606f38147~tplv-k3u1fbpfcp-watermark.image?)
   2\. File systems > Pseudo filesystems -> (/proc file system support & sysfs file system support)
   ![image.png](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/c409a11ccb1a48f7831175d5b7e9f782~tplv-k3u1fbpfcp-watermark.image?)
   

修改完保存, 然后重新制作 image, 并且运行:

$ qemu-system-x86_64 -kernel bzImage -initrd initramfs.cpio.gz -append "console=tty0 console=ttyS0 init=/bin/sh" -nographic

ps
PID   USER     TIME  COMMAND
    1 0         0:00 {init} /bin/sh /init
    2 0         0:00 [kthreadd]
    3 0         0:00 [kworker/0:0-eve]
    4 0         0:00 [kworker/0:0H]
    5 0         0:00 [kworker/u2:0-ev]
    6 0         0:00 [mm_percpu_wq]
    7 0         0:00 [ksoftirqd/0]
    8 0         0:00 [oom_reaper]
    9 0         0:00 [writeback]
   10 0         0:00 [kswapd0]
   11 0         0:00 [kworker/u2:1-ev]
   12 0         0:00 [kworker/0:1-eve]
   13 0         0:00 [kworker/u2:2-ev]
   14 0         0:00 [kworker/0:2]
   19 0         0:00 sh
   20 0         0:00 ps

如何制作一个可运行的 ISO 文件

创建文件结构并且复制数据

$ mkdir -p iso/boot/grub
$ cp bzImage iso/boot/
$ cp initramfs.cpio.gz  iso/boot/

创建 grub.cfg 文件

 $ vim iso/boot/grub/grub.cfg

set default=0
set timeout=10# Load EFI video drivers. This device is EFI so keep the
# video mode while booting the linux kernel.
insmod efi_gop
insmod font
if loadfont /boot/grub/fonts/unicode.pf2
then
        insmod gfxterm
        set gfxmode=auto
        set gfxpayload=keep
        terminal_output gfxterm
fimenuentry 'myos' --class os {
    insmod gzio
    insmod part_msdos
    linux /boot/bzImage init=/bin/sh console=ttyS0 console=tty0
    initrd /boot/initramfs.cpio.gz
}

安装 xorriso, mtools 并且制作 ISO image:

$ sudo apt install xorriso mtools -y
$ grub-mkrescue -o myos.iso iso/

$ ls -lah myos.iso

使用 Qemu 测试新的 ISO image

$ qemu-system-x86_64 -boot d -cdrom myos.iso -nographic

看到一篇关于时序相似性检测的文章 How can we quantify similarity between time series?, 里面涉及了几种算法的对比, 非常有启发性. 于是问 ChatGPT 要了几个关于相似性的算法, 也做了相关的对比.

这里记录一下相关的代码和步骤. 这个内容是在 Jupyter Notebook 写的. 主要分为4部分.

定义几种算法

import numpy as np
import pywt

# 1. 欧拉距离 1.长度相同
def euclidean_similarity(actual, predic):
    return np.sqrt(np.sum((actual - predic) ** 2))

# 2. mean absolute percentage error (MAPE) 1.长度相同, 值不能为0
def mape_similarity(actual, predic):
    return np.mean(np.abs((actual - predic) / actual))

# 3. Pearson correlation coefficient 1.长度相同, 1.0 表示完全相同
def correlation_similarity(actual, predic):
    a_diff = actual - np.mean(actual)
    p_diff = predic - np.mean(predic)
    numerator = np.sum(a_diff * p_diff)
    denominator = np.sqrt(np.sum(a_diff ** 2)) * np.sqrt(np.sum(p_diff ** 2))
    return numerator / denominator

# 4. 余弦相似性, 1.长度相同, 1.0 表示完全相同
def cosine_similarity(x, y):
    numerator = sum([a * b for a,b in zip(x,y)])
    denominator = np.sqrt(sum([a**2 for a in x])) * np.sqrt(sum([b**2 for b in y]))
    return numerator / denominator

# 5. 基于傅立叶变换 再使用余弦相似
def fft_similarity(x, y):
    # 对两个时序数据进行傅里叶变换
    fft_x = np.fft.fft(x)
    fft_y = np.fft.fft(y)

    # 对频谱数据进行归一化处理
    norm_x = np.abs(fft_x) / len(x)
    norm_y = np.abs(fft_y) / len(y)

    # 计算余弦相似度
    numerator = np.dot(norm_x, norm_y)
    denominator = np.linalg.norm(norm_x) * np.linalg.norm(norm_y)
    similarity = numerator / denominator

    return similarity

# 6. 小波变换 -
def wavelet_similarity(x, y, wavelet='db4', level=3, threshold='soft', threshold_value=0.1):
    # 对两个时序数据进行小波变换
    coeffs_x = pywt.wavedec(x, wavelet=wavelet, level=level)
    coeffs_y = pywt.wavedec(y, wavelet=wavelet, level=level)

    # 对小波系数进行阈值处理
    if threshold == 'hard':
        thresh_coeffs_x = [pywt.threshold(c, threshold_value, mode='hard') for c in coeffs_x]
        thresh_coeffs_y = [pywt.threshold(c, threshold_value, mode='hard') for c in coeffs_y]
    elif threshold == 'soft':
        thresh_coeffs_x = [pywt.threshold(c, threshold_value, mode='soft') for c in coeffs_x]
        thresh_coeffs_y = [pywt.threshold(c, threshold_value, mode='soft') for c in coeffs_y]
    else:
        raise ValueError("Invalid threshold mode. Must be 'hard' or 'soft'.")

    # 计算余弦相似度
    flat_x = np.concatenate(thresh_coeffs_x)
    flat_y = np.concatenate(thresh_coeffs_y)
    numerator = np.dot(flat_x, flat_y)
    denominator = np.linalg.norm(flat_x) * np.linalg.norm(flat_y)
    similarity = numerator / denominator

    return similarity

定义一些辅助函数

# 一些辅助函数
def __normalize(vals):
    min_v = min(vals)
    max_v = max(vals)
    return [(x - min_v) / (max_v - min_v) * 100 for x in vals]

def __fill_0_for_empty_ts(x_values, y_values):
    """
    x_values & y_values are 2 dimension array, the 1st column is the millisecond, the 2nd
    column is the value of that time, ex: [[], [], []]
    """
    start_time = round(min(min(x_values)[0], min(y_values)[0]) / 60000)  # the min minute
    end_time = round(max(max(x_values)[0], max(y_values)[0]) / 60000)  # the max minute

    count = int(end_time - start_time) + 1
    x_vals = np.zeros(count)
    y_vals = np.zeros(count)

    for element in x_values:
        minute = round(element[0] / 60000)
        x_vals[minute - start_time] = float(element[1])

    for element in y_values:
        minute = round(element[0] / 60000)
        y_vals[minute - start_time] = float(element[1])

    return x_vals, y_vals

定义对比和测试函数

import matplotlib.pyplot as plt
import pandas as pd
from IPython.display import display

def __prepare(ts_x, ts_y):
    x = ts_x[:,1]
    y = ts_y[:,1]

    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(30, 8))

    ax1.plot(np.array(ts_x[:,0], dtype='datetime64[ms]'), x, color='blue', label='ts_x')
    ax2.plot(np.array(ts_y[:,0], dtype='datetime64[ms]'), y, color='red', label='ts_y')


    nx = np.array(__normalize(x))
    ny = np.array(__normalize(y))
    ax3.plot(np.array(ts_y[:,0], dtype='datetime64[ms]'), nx, color='blue', label='normalized_x')
    ax3.plot(np.array(ts_y[:,0], dtype='datetime64[ms]'), ny, color='red', label='normalized_y')
    ax3.set_title('normalized')
    plt.tight_layout()
    
    return x, y, nx, ny

def test_similarity(x, y, similarity_func):
    return similarity_func(x, y)

# calc shift 
def test_similarity_with_move(ts_x, ts_y, similarity_func):
    steps = 8
    length = len(ts_x)
    best_score = similarity_func(ts_x, ts_y)
    best_shift = 0
    for move in range(1, steps):
        score = similarity_func(ts_x[move:], ts_y[:(length - move)])
        # print(f"{best_score} vs {score} with {similarity_func.__name__} shift: {move}")
        best_score, best_shift = (score, move) if score > best_score else (best_score, best_shift)
        
        score = similarity_func(ts_y[move:], ts_x[:(length - move)])
        # print(f"{best_score} vs {score} with {similarity_func.__name__} shift: {-move}")
        best_score, best_shift = (score, move) if score > best_score else (best_score, best_shift)
    
    return best_score, best_shift

def compare(ts_x, ts_y):
    algorithms = [correlation_similarity, cosine_similarity, fft_similarity, wavelet_similarity]
    x, y, nx, ny = __prepare(ts_x, ts_y)
    result = {"algorithm":[], "raw":[], "normalized":[], "rawWithShift":[], "normalizedWithShift":[]}
    for algorithm in algorithms:
        result["algorithm"].append(algorithm.__name__)
        result["raw"].append(test_similarity(x, y, algorithm))
        result["normalized"].append(test_similarity(nx, ny, algorithm))
        result["rawWithShift"].append(test_similarity_with_move(x, y, algorithm))
        result["normalizedWithShift"].append(test_similarity_with_move(nx, ny, algorithm))

    df = pd.DataFrame(result)
    display(df)
    

对比测试

明显区分度测试

# 测试1 明显区分度 原始数据
ts_x = 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ts_y = np.array([[1682638320000,1.2999976391715666],[1682638380000,1.3445933616363022],[1682638440000,0.8513891645166145],[1682638500000,0.580030734376455],[1682638560000,0.5366737829361838],[1682638620000,0.29213584265117487],[1682638680000,0.06666666666666667],[1682638740000,0.016666666666666666],[1682638800000,0.016666666666666666],[1682638860000,0],[1682638920000,0.016666666666666666],[1682638980000,0.041666666666666664],[1682639040000,0.049999999999999996],[1682639100000,0.14999968754412588],[1682639160000,0.16110728963202783],[1682639220000,0.15000013889120378],[1682639280000,0.14438552780642439],[1682639340000,0.3352488269002545],[1682639400000,0.4791106494858955],[1682639460000,0.4670988185285921],[1682639520000,0.47477976942200895],[1682639580000,0.4166843529964785],[1682639640000,0.5047513306649607],[1682639700000,1.1145315261277136],[1682639760000,1.3657446685411132],[1682639820000,1.3118932804610217],[1682639880000,1.3502069581635667],[1682639940000,0.8249999312100912],[1682640000000,0.5749947576282455],[1682640060000,0.425062421275175],[1682640120000,0.39166635418185697],[1682640180000,0.5333356252965927],[1682640240000,0.5416674308240772],[1682640300000,0.5416659377744454],[1682640360000,0.5499998959440148],[1682640420000,0.46666524316478064],[1682640480000,0.6336589514654256],[1682640540000,0.6913314113839778],[1682640600000,0.5028315836111288],[1682640660000,0.38466392611255945],[1682640720000,0.19999944449045007],[1682640780000,0.10833336810662474],[1682640840000,0.03333333333333333],[1682640900000,0.0250005208514185],[1682640960000,0.2667035720031638],[1682641020000,0.40833451392505926],[1682641080000,0.5416680904110321],[1682641140000,0.6833330295489726],[1682641200000,0.45832292308947503],[1682641260000,0.5583323612227931],[1682641320000,0.46665854636552123],[1682641380000,0.34166541686573676],[1682641440000,0.33333107679551194],[1682641500000,0.08333326388946759],[1682641560000,0.05000010416796876],[1682641620000,0.024999999999999998],[1682641680000,0.016666666666666666],[1682641740000,0.024999999999999998],[1682641800000,0.041669619101209456],[1682641860000,0.049999999999999996],[1682641920000,0.05833302083926493],[1682641980000,0.15000000000000005],[1682642040000,0.1333332638938078],[1682642100000,0.2083326042501437],[1682642160000,0.6653442813240605],[1682642220000,1.0819944200907627],[1682642280000,1.3986757640109924],[1682642340000,1.6500266144000366],[1682642400000,1.4083953201740684],[1682642460000,1.1167151023096336],[1682642520000,0.9916681947057309],[1682642580000,0.7666733685670183],[1682642640000,0.7583336112326406],[1682642700000,0.59166618062355],[1682642760000,0.6583337847718469],[1682642820000,0.5999875460015153],[1682642880000,0.6166656945127288],[1682642940000,0.6166678820895601],[1682643000000,0.4499863662297911],[1682643060000,0.9416896769371015],[1682643120000,0.9832976874844609],[1682643180000,0.9333240658929527],[1682643240000,1.0750055964431735],[1682643300000,0.600000903022002],[1682643360000,0.39173132611068756],[1682643420000,0.40000562689957914],[1682643480000,0.4916728171698435],[1682643540000,0.5500227058301577],[1682643600000,0.9166703126734733],[1682643660000,1.0583296540822842],[1682643720000,0.8999965974380644],[1682643780000,1.1166663542753188],[1682643840000,0.7916661458777478],[1682643900000,0.76667354487317],[1682643960000,0.7722229090747322],[1682644020000,0.4666662500708899],[1682644080000,0.7416511215169792],[1682644140000,0.8249939602133775],[1682644200000,0.7083330903728287],[1682644260000,0.8083158526792605],[1682644320000,0.6416650001313566],[1682644380000,0.724999757219176],[1682644440000,0.8166649654408255],[1682644500000,0.7666740378807257],[1682644560000,0.6083323959263589],[1682644620000,0.37500145838802307],[1682644680000,0.4499981251539282],[1682644740000,0.48333923941052465],[1682644800000,0.5333367363136681],[1682644860000,0.4833325347750283],[1682644920000,0.37500076390596115],[1682644980000,0.400000000050347],[1682645040000,0.3833338194904531],[1682645100000,0.4333312848965507],[1682645160000,0.3416668750176507],[1682645220000,0.24166645841926884],[1682645280000,0.21666690978081724],[1682645340000,0.15833458338599746],[1682645400000,0.14166677085546953],[1682645460000,0.12500935008019612],[1682645520000,0.5833696574822261],[1682645580000,1.3166618058052564],[1682645640000,1.7416512586793633],[1682645700000,2.066650247736495],[1682645760000,1.791559029251039],[1682645820000,1.1082862688662387],[1682645880000,0.7999629530472216],[1682645940000,0.44166684035257686],[1682646000000,0.4416632989306695],[1682646060000,0.39166559063438255],[1682646120000,0.29166784727343953],[1682646180000,0.3500004167204877],[1682646240000,0.1333350349949652],[1682646300000,0.1416661805839104],[1682646360000,0.11666652778009254],[1682646420000,0.049999999999999996],[1682646480000,0.05000045141334044],[1682646540000,0.0583330208398436],[1682646600000,0.15833333336313743],[1682646660000,0.30833399308608334],[1682646720000,0.45000006944965304],[1682646780000,0.5250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compare(ts_x, ts_y)

结果:

不明显区分度

# 测试2 一般区分度 
ts_x = 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ts_y = 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84151040000,1840.8345752608034],[1684151100000,1983.273905996757],[1684151160000,1925.3092783505153],[1684151220000,1908.604651162793],[1684151280000,1843.6071887034655],[1684151340000,1787.6822633297056],[1684151400000,1805.785234899329],[1684151460000,1860.202312138729],[1684151520000,2870.871143375676],[1684151580000,3121.2742980561516],[1684151640000,3327.3218142548585],[1684151700000,2778.5851780558273],[1684151760000,2795.159728944824],[1684151820000,2697.834158415847],[1684151880000,2841.3716814159293],[1684151940000,3076.4394829612183],[1684152000000,1378.7657430730487],[1684152060000,1381.5567765567762],[1684152120000,3033.734939759021],[1684152180000,4347.3509933774785],[1684152240000,5051.801801801774],[1684152300000,4100.322580645134],[1684152360000,1976.1247973767386],[1684152420000,1979.6071428571424],[1684152480000,1910.9078404401644],[1684152540000,1766.2827225130877],[1684152600000,1657.899842364081],[1684152660000,1830.4106468583077],[1684152720000,1784.2769230769213],[1684152780000,1779.3984962406014],[1684152840000,1953.583690987126],[1684152900000,1898.2544727070926],[1684152960000,2008.4889987064257],[1684153020000,2814.2079048843243],[1684153080000,2602.889148417723],[1684153140000,3180.644479432188],[1684153200000,3018.7361979204934],[1684153260000,2150.263975558962],[1684153320000,2077.1640144295625],[1684153380000,2187.7926836734514],[1684153440000,1952.181818181819],[1684153500000,1906.5979381443306],[1684153560000,1854.477611940299],[1684153620000,1982.8368794326248],[1684153680000,1936.9152542372883],[1684153740000,1842.6644182124787],[1684153800000,2057.1721311475285],[1684153860000,1930.3302509907521],[1684153920000,1991.5546218487402],[1684153980000,1874.9499545040942],[1684154040000,1841.875693673695],[1684154100000,1860.85889570552],[1684154160000,1827.5929752066118],[1684154220000,2642.58241758242],[1684154280000,1949.168704156479],[1684154340000,1885.8257713248631],[1684154400000,1936.5283018867901],[1684154460000,1841.707080504364],[1684154520000,1734.3518518518513],[1684154580000,1845.531914893616],[1684154640000,1856.636125654449],[1684154700000,1932.822429906544],[1684154760000,1802.0820189274452],[1684154820000,1744.559999999998],[1684154880000,1981.5044247787612],[1684154940000,1802.052980132451],[1684155000000,1963.751668891856],[1684155060000,2221.3963963963934],[1684155120000,1964.985955056179],[1684155180000,1814.299065420561],[1684155240000,1869.838709677419],[1684155300000,1827.6271186440674],[1684155360000,1905.6666666666677],[1684155420000,1746.677524429968],[1684155480000,1962.8813559322034],[1684155540000,1930.1845018450167],[1684155600000,1763.603034134007],[1684155660000,1806.207701283544],[1684155720000,1776.0456730769229],[1684155780000,1907.9721030042922],[1684155840000,1979.4346289752639],[1684155900000,1850],[1684155960000,2596.3251670378627],[1684156020000,1868.6101694915246],[1684156080000,1855.7101024890203],[1684156140000,2233.4886817576485],[1684156200000,1940.7702702702693],[1684156260000,3068.7763713080158],[1684156320000,2209.5444685466427],[1684156380000,2401.148851148847],[1684156440000,2110.0112485939153],[1684156500000,1801.420047732697],[1684156560000,1830.608365019011],[1684156620000,1864.3550624133138],[1684156680000,1779.4650205761318],[1684156740000,1791.6706161137427],[1684156800000,1854.9466192170814],[1684156860000,1687.9656160458464],[1684156920000,1968.3253588516736],[1684156980000,1764.858611825193],[1684157040000,1919.6932515337428],[1684157100000,1769.2094861660073],[1684157160000,1735.2030735455528],[1684157220000,1948.9361702127685],[1684157280000,1750.2443609022566],[1684157340000,1806.5100671140956],[1684157400000,1971.58463385354],[1684157460000,1795.0704225352108],[1684157520000,1764.2024965325922],[1684157580000,1874.3540183112905],[1684157640000,1890.6065088757414],[1684157700000,1919.3067590987848],[1684157760000,1740.9292502639917],[1684157820000,1883.8912133891235],[1684157880000,2247.024952015375],[1684157940000,1971.0535117056843],[1684158000000,1963.0633284241503],[1684158060000,2375.0000000000045],[1684158120000,2281.8991097922817],[1684158180000,2307.380952380962],[1684158240000,1841.62303664921],[1684158300000,1886.4006514657979],[1684158360000,1734.5123384253804],[1684158420000,1708.4269662921329],[1684158480000,1613.0898203592806],[1684158540000,1864.0845070422502],[1684158600000,1591.999999999999],[1684158660000,1826.4131994261124],[1684158720000,1605.6120826709066],[1684158780000,1669.6625421822282],[1684158840000,1731.3366336633708],[1684158900000,1797.1879483500682],[1684158960000,1825.505780346819],[1684159020000,1899.3865030674842],[1684159080000,1680.6249999999973],[1684159140000,1782.9172932330816],[1684159200000,1858.8267148014422],[1684159260000,1713.2421874999995],[1684159320000,1607.3873873873856],[1684159380000,1720.858725761773],[1684159440000,1588.7537993920976],[1684159500000,1650.7012195121938],[1684159560000,1662.8129205921946],[1684159620000,1708.826151560177],[1684159680000,1568.4615384615377],[1684159740000,1528.090322580645],[1684159800000,1731.8490967056314],[1684159860000,1601.6613418530362],[1684159920000,1518.2207578253679],[1684159980000,1396.5697674418586],[1684160040000,1623.604651162791],[1684160100000,1489.7496522948554],[1684160160000,1554.8637015781924],[1684160220000,1435.7885906040292],[1684160280000,1685.1741293532332],[1684160340000,1813.4756097560953],[1684160400000,1533.5937499999984],[1684160460000,1818.8415446071901],[1684160520000,1838.0295566502443],[1684160580000,1591.733021077283],[1684160640000,1396.7315175097287],[1684160700000,1357.4918918918893],[1684160760000,1597.197452229298],[1684160820000,1482.3979591836726],[1684160880000,1510.6538461538405],[1684160940000,1391.6493313521544],[1684161000000,1266.3421828908554],[1684161060000,1354.928848641655],[1684161120000,1388.2812499999955],[1684161180000,1549.1381668946628],[1684161240000,1552.0574162679409],[1684161300000,1196.1259079903136],[1684161360000,1234.6196660482337],[1684161420000,1315.2928647497326],[1684161480000,1396.98113207547],[1684161540000,1521.6216216216205],[1684161600000,1478.810289389074],[1684161660000,1438.6451612903177],[1684161720000,994.4959677419364],[1684161780000,978.4968684759922],[1684161840000,1606.6513056835638],[1684161900000,1597.032457496134],[1684161960000,1623.5437589670014],[1684162020000,1430.3545051698657],[1684162080000,869.6445497630334],[1684162140000,956.458885941644],[1684162200000,904.5634920634895],[1684162260000,836.3212435233139],[1684162320000,828.5777777777766],[1684162380000,783.8252340615243],[1684162440000,766.3223300970867],[1684162500000,3009.6874999999286],[1684162560000,1632.5087924970692],[1684162620000,1585.9181685338517],[1684162680000,1502.2391559202813],[1684162740000,1617.2050340849498],[1684162800000,1569.0939977349924],[1684162860000,1510.1306818181818],[1684162920000,1493.2784726793925],[1684162980000,1425.8500323206197],[1684163040000,584.0601503759393],[1684163100000,398.3973655323814],[1684163160000,389.6801346801341],[1684163220000,491.54566744730516],[1684163280000,371.4275256222553],[1684163340000,383.01547678482285],[1684163400000,436.19512195121996],[1684163460000,663.1407942238234],[1684163520000,855.6786366402918],[1684163580000,868.0652818991098],[1684163640000,830.8698777857647],[1684163700000,190.87328767123273],[1684163760000,177.98598130841094],[1684163820000,176.26115041525668],[1684163880000,158.09655396618976],[1684163940000,163.54401228249736],[1684164000000,154.67682363804215]])

compare(ts_x, ts_y)

结果:

在诊断Java应用的时候, 我们经常看到很多异步的调用, 当前线程等待其它线程返回结果. 有时候会发现这些线程迟迟没有往下走, 于是查看线程状态, 会看到如下的线程栈:

sun.misc.Unsafe.park(Native Method) 
java.util.concurrent.locks.LockSupport.park(LockSupport.java:175) 
java.util.concurrent.FutureTask.awaitDone(FutureTask.java:429) 
java.util.concurrent.FutureTask.get(FutureTask.java:191) 
com.tianxiaohui.RboCache.compute(RboCache.java:46)

我们会很自然的发问, 为什么它停在这, 到底发生了什么?

初步分析

对于这种情况, 有2种情况:

1. 这个任务还没有被执行, 所以没有结果;
2. 这个任务在执行中, 所以还没有结果;

对于第一种情况, 要去查看线程为什么没启动, 或者线程池为啥没执行它. 通常这时候要查看线程池的core size, max size, 和workQueue 和 workers 的情况.
对于第二种情况, 则要查看执行的线程在干什么事情, 为什么还没出结果.

java.util.concurrent.Future

对于java.util.concurrent.Future 我们很容易的能从它的字段: runner 看出谁在执行, 还是没有在执行, 如下图:

java.util.concurrent.CompletableFuture

对于 java.util.concurrent.CompletableFuture 则稍微麻烦一些, 要看谁指向它:

1. 如果指向它的对象除了当前线程还有另外一个线程, 那么另外一个线程就是执行线程;
2. 如果指向它的对象除了当前线程还有一个等待队列中的某个对象, 则它还没开始执行;
   

上面用到的代码:

import java.util.concurrent.Callable;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.util.function.Supplier;

public class WhyFutureStuck {
    
    public static void main(String[] args) {
        ExecutorService es = Executors.newFixedThreadPool(2);
        
        Future<String> f1 = es.submit((Callable<String>) new Callable<String>() {
            @Override
            public String call() throws Exception {
                Thread.sleep(3600 * 1000);
                return "result-f1";
            }
        });
        
        CompletableFuture<String> cf1 = CompletableFuture.supplyAsync((Supplier<String>) () -> {
            try {
                Thread.sleep(3600 * 1000);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
            return "result-cf1";
        }, es);
        
        //threads are used up
        Future<String> f2 = es.submit((Callable<String>) new Callable<String>() {
            @Override
            public String call() throws Exception {
                Thread.sleep(3600 * 1000);
                return "result-f2";
            }
        });
        
        CompletableFuture<String> cf2 = CompletableFuture.supplyAsync((Supplier<String>) () -> {
            try {
                Thread.sleep(3600 * 1000);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
            return "result-cf2";
        }, es);
        
        try {
            System.out.println(f1.get());
            System.out.println(f2.get());
            System.out.println(cf1.get());
            System.out.println(cf2.get());
        } catch (InterruptedException | ExecutionException e) {
            e.printStackTrace();
        }
    }
}

最近接手一个新的Java项目, 架构师明确要Java 11 版本, 于是在 eclipse 里面设置使用JDK 17, JDK 编译器兼容 level 设置成 11. 设置完成后, 然后重新编译, 竟然报错了, 还是几百个错误!

错误内容全部是: The package java.util is accessible from more than one module: <unnamed>, java.base.

如下图, 这些从java.util引用的类, 全部都报这个错:

这个错误代表什么意思

Java 9 开始有了模块化系统的概念(JPMS), 每个Java 文件都属于某个模块. 例如上面例子中出现的 java.util.HashMap, java.util.concurrent.TimeUnit 都属于JDK里面的 java.base 模块.

那么<unnamed> 模块是什么呢?

对于JDK本身, 它默认已经分了模块, 但是我们还有很多jar包, 并没有按照模块的方式去打包, 或者在JDK 9 之前就有了这些jar包, 我们照样可以依赖它们.

对各种模块的依赖, 会加到modulepath, 对于非模块的依赖, 继续使用classpath. classpath上的类会被统一的分配到一个叫<unnamed>的模块.

如下面的 eclipse 截图所示, 所有maven的依赖全部放到了classpath.

错误含义

所以, 这个错误的真正含义是: 在我们依赖中, java.util包即出现在了 JDK 的 java.base 这个模块, 也出现在了我们依赖的classpath上. 而Java模块化系统不允许同一个包出现在2个模块里面.

如何修复

有2种可能的方案:

1. 退回到Java 8, 不使用模块化的概念, 错误消失.

2. 找到提供java.util包的jar包, 质问开发者为啥给自己的jar包加入jdk的包名, 是想夹带私货吗?

   我们这里只能选择第二个选项, 所以如何找到这个包含java.util的jar包成了重点.

如何找到第二个包含java.util的jar包

看到最初错误截图里面那些类, 我们很直观的可能认为: 只要使用eclipse的 open type 菜单是不是就很容易找到了, 我们试一下:

从上面截图可以看到, eclipse 只找到一个HashMap, 并且源自于JDK11, 同样, 对于java.uti.Map, java.util.concurrent.TimeUnit 我们也只能找到仅有的一个属于JDK的.

明明报了java.util包来自2个模块, 为啥我们只找到一个呢?

其实, 我们上面的方式去找HashMap这个类出现在哪个包, 但是这个错误的真正的重点是包的名字, 也就是说, 有另外一个jar包里包含了java.util.Xxxx, 它不管类名是什么, 只管包名和JDK里面的java.util包重复了.

我们可以同样的方式在 eclipse 的 open type 里面输入java.util.*, 只不过会出现非常长的列表, 然后人工一个个对比.

如何手工找到这个jar包

这里提供2个方法:

1. 方法一: 使用 eclipse open type, 然后输入 java.util.*,如下图, 按顺序找一定能找到:
   

2. 方法二: 在 maven 的pom.xml 里面去掉一部分依赖, 看是不是这个错误消失了(同时其它依赖缺失错误会出现), 如果这个消失了, 说明去掉的这个依赖里面(直接或间接)包含这个包名. 使用一次去掉多个依赖(二分查找)可以加快速度.

如何自动化找到这个jar

eclipse 能报出这个错误, 说明它在我们的依赖中, 除了JDK之外还有一个jar包含这个包. 那么, 我们可以用同样的方法, 找到这个jar.

步骤:

1. 通过 mvn 命令找到所有的依赖jar.
2. 查找 jar 里面的类全名是不是包含 java.util.

mvn 命令找到所有的依赖jar

首先, 我们进入的项目pom.xml 所在的目录, 然后执行mvn 的goal: dependency:build-classpath, 把输出结果全部放到/tmp/dep.txt文件.

$ cd <project_dir>
$ ~/work/tools/apache-maven-3.9.1/bin/mvn dependency:build-classpath -Dmdep.outputFile=/tmp/dep.txt

打开/tmp/dep.txt文件, 可以看到里面是以:(MAC上)分割开的jar文件名.

遍历查找每个jar的类

第二步, 读取上面的输出, 以:分隔成多个jar, 然后遍历每个jar, 找出包含java.util的类,

    jars=(${(s/:/)$(cat /tmp/dep.txt)})
    for i in "${jars[@]}" 
    do 
        jar -tf $i | grep '^java.util' && echo "\t\tfind in ${i}"
    done

执行上面的脚本(mac 上的zsh), 得到如下结果:

java/util/
java/util/concurrent/
java/util/Hashtable.class
java/util/concurrent/ConcurrentHashMap.class
        find in /Users/eric/.m2/repository/com/boundary/high-scale-lib/1.0.6/high-scale-lib-1.0.6.jar

最终, 通过几行shell命令, 我们也找到了包含这个包名的jar包.

总结

Java 9 之后, 开始分模块, 一个项目可能既有对模块的依赖, 还有对普通jar包的依赖, 这些以前的jar包会被放到一个<unnamed>的模块. 如果一个包名出现在多个模块中(包含<unnamed>的模块), 就会出现这个错.

本文中另外一个出现在<unnamed>的模块, 可以通过遍历查找依赖jar的方式去查找到这个jar包.

JDK 里面有哪些模块?

java --list-modules

学习Java syncrhonized 关键字, 以及wait(), notify(), notifyAll 的时候, 经常会遇到一个单词: monitor 或者另外一个关键词 intrinsic lock(内置锁), 它们都是同一个东西. 也会有人说: wait(), notify(), notifyAll都是 monitor 的方法. 那么 monitor 到底是什么呢? 翻译成中文应该是什么呢?

在计算机科学中，"monitor"是一种用于管理并发访问共享资源的高级抽象。它由Per Brinch Hansen和C.A.R. Hoare于1970年代提出，并成为了处理并发的重要概念。

Monitor的基本思想是将共享资源（例如内存、文件、设备等）封装成一个单一的实体，称为"monitor"。这个monitor可以被多个并发执行的进程或线程所访问，但是一次只允许一个进程或线程执行其中的代码，从而避免了对共享资源的竞争条件和数据不一致性。

Monitor通常包含了以下几个要素：

1. 互斥（Mutual Exclusion）：当一个进程或线程进入monitor并执行其中的代码时，其他进程或线程将被阻塞，无法同时进入monitor。这样可以保证在同一时刻只有一个进程或线程可以访问共享资源，从而避免了竞争条件。
2. 条件变量（Condition Variables）：用于实现进程或线程之间的等待和通知机制。一个进程或线程可以在monitor中等待某个条件变为真，而其他进程或线程可以通过设置条件变量的值来通知等待的进程或线程条件已经满足，从而避免了忙等待。
3. 入口队列（Entry Queue）：用于管理等待访问monitor的进程或线程。当一个进程或线程请求进入monitor时，如果monitor已经被占用，则该进程或线程将被放入入口队列中，按照先来先服务的原则等待访问monitor。

使用monitor可以简化并发编程的复杂性，提供了一种结构化的方式来管理共享资源的访问，并且避免了许多并发编程中可能出现的问题，如竞争条件、死锁和饥饿等。它在操作系统、并发编程框架和并发控制算法中得到了广泛的应用，成为了并发编程中的重要工具。

看完上面的介绍, 是不是感觉跟 Java 的 syncrhonized 很像, 是的. Java 最初的设计就是想实现任何一个Java 对象都是一个包含 monitor 的想法.

但是 Java 对象跟最初 monitor 的实现有点差别:

1. 在 Monitor 的设计中, 任何对象的字段都是隐藏的, 任何方法都是需要获得锁才能进入的, 但是Java的对象并不是这样的.
2. Monitor 可以有多个条件变量, 这类似后面Java 引入的Lock关联的Condition的概念, 一个Lock有多个Condtion, 但是 syncrhonized 却只有一个条件变量.
3. 若有多个线程等待同一个条件变量, 只能notifyAll, 然后再由线程自己去判断自己等待的条件满足了吗.

monitor 在操作系统层面的实现

monitor 只是一个概念, 具体到操作系统层面, 要具体到特定的技术去实现. 比如用Semaphore(信号量)去实现, 或者使用 test-and-set 指令去实现

关于 monitor 的概念, 这篇wiki 讲的比较详细: https://en.wikipedia.org/wiki/Monitor_(synchronization)#Semaphore.

关于Java syncrhonized 的实现, 可以参考: https://blogs.oracle.com/javamagazine/post/java-thread-synchronization-synchronized-blocks-adhoc-locks