最近有点东西要下载，找来找去只有百度云有。试了百度云的客户端，只能跑到70kbps，这TM下到猴年马月！
研究了一下，最终还是找到了解决办法，成功通过开100个线程的方式，把下载速度干到了7Mbps。总结如下：
2. Mac 实在没有什么好的下载工具，找来找去还是 axel 最简单。Homebrew 装 axel，没有 Homebrew 的话先到这里装上： https://brew.sh/
然后
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brew install axel |
3. 最后一步，开 100 个线程下！
先用直链脚本弄到下载地址，然后
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axel -a -n 100 "下载地址" |
效果感人
感觉应该可以开到200个线程跑到上限的，不过差不多够了
大概是一个月之前 SAE 又改收费规则了, 结果就是所有使用数据库的应用都要交一个所谓的 MySQL 租金, 我账户里原先盘算的能用两三年的余额瞬间没了.
现在来看两年前把博客从 SAE 迁出真是一个非常明智的决定, 现在可以彻底告别 SAE 了.
Recently I encountered a “Multiple Dex Files Define” error when writing and building Android libraries. After a lot of work, the root reason is attributed to a IntelliJ IDEA bug which appears when you are using .classpath(Eclipse style) project configuration file format. I reported this bug to Jetbrains and they had confirmed my report on it. It is now being tracked in their bug system. Before Jetbrains fix the bug, you can avoid it by using .iml(IntelliJ style) project configuration file format.
Here is how I encountered and reproduced this bug, which is also available through https://youtrack.jetbrains.com/issue/IDEA-144038.
When exporting jars, the same configuration will have different output behaviors using different config file style (.classpath/.iml)
How to reproduce:
Suppose here is my project structure:
RootAndroidApplication
AndroidLibraryModuleA
AndroidLibraryModuleBAndroidLibraryModuleA depends on AndroidLibraryModuleB, RootAndroidApplication depends on both AndroidLibraryModuleA and AndroidLibraryModuleB.
Now we want to export AndroidLibraryModuleA and AndroidLibraryModuleB as jar without resources. In other words, we are only using the Java code part.In artifacts settings, we add to jar configurations. For AndroidLibraryModuleA we only include AndroidLibraryModuleA compile output. For AndroidLibraryModuleB we only include AndroidLibraryModuleB compile output.
In AndroidLibraryModuleA we change the compile level of dependency AndroidLibraryModuleB to “provided”. Then we build the artifacts of AndroidLibraryModuleA.
If we open the jar file exported, we can see only compiled classes from AndroidLibraryModuleA is listed here.
However, if we keep everything unchanged, just switch the project file format from IntelliJ’s .iml to Eclipse’s .classpath of AndroidLibraryModuleA. Then we rebuild the artifacts.
Then the compiled classes from AndroidLibraryModuleB is listed in AndroidLibraryModuleA’s jar file.
I believe the .iml’s behavior is the intended one while the .classpath one’s behavior may result from a bug.
This difference will lead to a “Multiple dex files define” error when AndroidLibraryModuleA.jar and AndroidLibraryModuleB.jar are added to another project as jar dependency as there are duplicate class files in two jars.
I’m trying to build up a push system recently. To increase the scalability of the system, the best practice is to make each connection as stateless as possible. Therefore when bottleneck appears, the capacity of the whole system can be easily expanded by adding more machines. Speaking of load balancing and reverse proxying, Nginx is probably the most famous and acknowledged one. However, TCP proxying is a rather recent thing. Nginx introduced TCP load balancing and reverse proxying from v1.9, which is released in late May this year with a lot of missing features. On the other hand, HAProxy, as the pioneer of TCP loading balacing, is rather mature and stable. I chose to use HAProxy to build up the system and eventually I reached a result of 300k concurrent tcp socket connections. I could have achieved a higher number if it were not for my rather outdated client PC.
300k concurrent connection is not a easy job for even the high end server PC. To begin with, we need to tune the linux kernel configuration to make the most use of our server.
Since sockets are considered equivalent to files from the system perspective, the default file descriptors limit is rather small for our 300k target. Modify /etc/sysctl.conf to add the following lines:
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fs.file-max = 10000000 fs.nr_open = 10000000 |
These lines increase the total file descriptors’ number to 1 million.
Next, modify /etc/security/limits.conf to add the following lines:
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* soft nofile 10000000 * hard nofile 10000000 root soft nofile 10000000 root hard nofile 10000000 |
If you are a non-root user, the first two lines should do the job. However, if you are running HAProxy as root user, you need to claim that for root user explicitly.
Holding such a huge number of connections costs a lot of memory. To reduce memory use, modify /etc/sysctl.conf to add the following lines.
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net.ipv4.tcp_mem = 786432 1697152 1945728 net.ipv4.tcp_rmem = 4096 4096 16777216 net.ipv4.tcp_wmem = 4096 4096 16777216 |
Upon finishing tuning Linux kernel, we need to tune HAProxy to better fit our requirements.
In HAProxy, there is a “max connection cap” both globally and backend specifically. In order to increase the cap, we need to add a line of configuration under the global scope.
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maxconn 2000000 |
Then we add the same line to our backend scope, which makes our backend look like this:
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backend pushserver mode tcp balance roundrobin maxconn 2000000 |
By default, HAProxy will detect dead connections and close inactive ones. However, the default keepalive threshold is too low and when applied to a circumstance where connections have to be kept in a long-pulling way. From my client side, my long socket connection to the push server is always closed by HAProxy as the heartbeat is 4 minutes in my client implementation. Heartbeat that is too frequent is a heavy burden for both client (actually android device) and server. To increase this limit, add the following lines to your backend. By default these numbers are all in milliseconds.
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timeout connect 5000 timeout client 50000 timeout server 50000 |
When you are facing simultaneous 30k connections, you will encounter the problem of “port exhaustion”. It is resulted from the fact that each reverse proxied connection will occupy an available port of a local IP. The default IP range that is available for outgoing connections is around 30k~60k. In other words, we only have 30k ports available for one IP. This is not enough. We can increase this range by modify /etc/sysctl.conf to add the following line.
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net.ipv4.ip_local_port_range = 1000 65535 |
But this does not solve the root problem, we will still run out of ports when the 60k cap is reached.
The ultimate solution to this port exhaustion issue is to increase the number of available IPs. First of all, we bind a new IP to a new virtual network interface.
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ifconfig eth0:1 192.168.8.1 |
This command bind a intranet address to a virtual network interface eth0:1 whose hardware interface is eth0. This command can be executed several times to add arbitrary number of virtual network interfaces. Just remember that the IP should be in the same sub-network of your real application server. In other words, you cannot have any kind of NAT service in your link between HAProxy and application server. Otherwise, this will not work.
Next, we need to config HAProxy to use these fresh IPs. There is a source command that can be used either in a backend scope or as a argument of server command. In our experiment, the backend scope one doesn’t seem to work, so we chose the argument one. This is how HAProxy config file looks like.
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backend mqtt mode tcp balance roundrobin maxconn 2000000 server app1 127.0.0.1:1883 source 192.168.8.1 server app2 127.0.0.1:1883 source 192.168.8.2 server app3 127.0.0.1:1883 source 192.168.8.3 server app4 127.0.0.1:1884 source 192.168.8.4 server app5 127.0.0.1:1884 source 192.168.8.5 server app6 127.0.0.1:1884 source 192.168.8.6 |
Here is the trick, you need to declare them in multiple entries and give them different app names. If you set the same app name for all four entries, the HAProxy will just not work. If you can have a look at the output of HAProxy status report, you will see that even though these entries has the same backend address, HAProxy still treats them as different apps.
That’s all for the configuration! Now your HAProxy should be able to handle over 300k concurrent TCP connections, just as mine.
I recently experienced a severe slow debugging experience in IntelliJ + nodejs plugin / WebStorm, which made me to wait nearly one minute for my app to start. I tried to figured out why, and I noticed that the most of the time was spent on loading various packages.
Later on I found the cause for such slowness: the IDE’s break on exception option is enabled. In other words, the IDE will try catch almost every line of JavaScript code, no matter it is written by you or it is from a third party package, which leads to a huge performance loss.
Disabling it by navigating through menu ‘RUN -> View Breakpoints…’ and toggle ‘JavaScript Exception Breakpoints’. You will have your program debugging much faster. To further accelerate your experience, navigate through menu ‘Help -> Find Action…’, type in ‘Registry’ and enter. Uncheck ‘js.debugger.v8.use.any.breakpoint’.
Now your nodejs program should run in debug mode as fast as it is not.
其实本来我是不打算看这部电影的，刚好公司组织一起去看，原价35的3D票只要10块钱，算是个福利，似乎网上一片称赞，索性就看一看。我最近刚把 GTA 5 打通，一篇很长的评论还没写完，就先来说说这个《大圣归来》。
首先得感谢这电影票只花了10块钱，要是35买的看完我肯定觉得不值。接下来一样样的评。
画面：很好。这部电影在画面上的突破，相对于其它国产动画还是很明显的。不夸张的说，这样的画面素质，放到迪士尼的那一堆3D动画片里也是不算差的。毛发的细节，光照，物理效果，都很好。可惜的是，画面好也就止步于此了，这么好的画面没有用来营造气氛渲染情感，妖洞没有妖气，那个关大圣的水晶宫其实很漂亮可以多给点特写但是稍微摆弄了两下就过去了，没有物尽其用。
剧情：弱爆了。一个好的剧情，要么是讲一个大家都没听过的故事，要么是讲一个大家以为都听过实际上走向出乎意料的故事。前者是盗梦空间，后者是冰雪奇缘。大圣的故事，看到开头猜到结尾，结果导演连个结尾都不让我们看，连个完整的故事都不讲完故意吊着你，不想多说了。
人物：既然片名叫大圣归来，那么我想主角肯定是大圣吧，其它什么江流儿老师傅猪八戒应该都是配角才对吧？可是塑造出来了一个什么样的大圣形象呢？恕我眼拙看不出来，因为封印没解掉垂头丧气，因为看到江流儿被妖怪打而变身奥特曼，体现出大圣是一个爱护儿童的好猴子？可是论爱护儿童，我觉得师傅才是真·爱啊，人家没有特技都敢入妖穴，比大圣这种有特技的高到不知道哪里去了。
音乐：那个汪峰的歌一出来就彻底出戏了。现在抓妖精、救女孩的事情一个没做出这么燃的BGM是要怎样？好在其它的配乐分寸把握的都还好，至少不是负分。
总体而言，个人觉得这种某个方面有明显瑕疵的电影评分在7到8之间算是一个正常的评价。大圣归来，并没有好到值得让你专门抽两个小时的时间，认认真真欣赏的水平；如果大圣归来是迪士尼出的，我会毫不客气的把他划到流水线片、爆米花片的类别里去。
最近在做Android Push系统的服务器端, 要用到1.9版Nginx引入的TCP代理功能, 由于Nginx默认的连接数太少, 我就按照之前改内核参数的习惯, 直接大手一挥直接把连接数加到了1000W. reload配置之后, 我的机器死掉了.
我当时根本没想到是Nginx的原因, 下意识的认为是我用的那个MQTT的库一定是泄露内存了, 然后果断重启机器.
然后就起不来了, 在Ubuntu的启动界面一直转啊转. 再次重启, 进 Recovery Mode 打日志, 发现卡在 Stopping System V runlevel compatibility [OK] 这里.
网上几乎一边倒的认为是 NVidia 的显卡驱动问题, 虽然我觉得不太可能是这个原因不过网上都这么说, 那就卸了吧.
卸了之后还是进不去系统, 而且还是卡在老地方! 于是开始在笔记本上查资料, 机器就放在那里没管. 过了几分钟之后, 我瞟了一眼机器, 出了一行 log, Out of Memory Error, Kill Nginx. 这时候我才意识到到是不是和我之前改了 Nginx 配置有关系. 很快我就确定是 Nginx 的问题, 因为 Nginx 用的是连接池, 即使没有连接也会预先创建好一定数量的备用, 我的机器大约8G内存, 之前的测试中大约能抗住80W左右的连接, 1000W的连接池必定导致OOM, 然后Nginx就吃光了所有的内存, 强迫操作系统不断进行垃圾回收, 导致启动卡死.
接下来就很简单了, 把Nginx的连接数改回去, 再把显卡驱动装上, 成功进入系统, 然后再给Nginx设置一个合适的连接池大小, 继续进行试验.
In most scripting languages, there are first-class functions. In short, first-class functions refer to functions that can serve as call arguments, work in expressions and be assigned to variables.
So what is a closure? A closure is a function that brings context information with it. Among all the languages, JavaScript is probably the language where closure is mostly frequently used. In my opinion, the reason why closures are so widely used in Javascript lies in that Javascript does not have a mature OO system compared to other programming languages.
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function a() { var t = 1; function b() { console.log(++t); } return b; } x = a(); x(); x(); |
The output of this piece of Javascript code is “2” and “3”. As we can see, function a() returned another function. However, this function not only carries the information about itself, but also the context it lies in, i.e. the value of variable t.
In Apple’s programming language Swift, we have similar closures that act almost identical to Javascript’s.
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import Foundation func a() -> (Void -> Void){ var t = 1; func b() -> Void{ println(++t); } return b; } var x = a(); x(); x(); |
The result is also the same as Javascript’s, “2” and “3”. What about Python?
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def a(): t = 1; def b(t = t): t += 1; print(t); return b; x = a(); x(); x(); |
There are a few notable differences here. First of all, the variable t defined in a() is not visible to b(), thus it must be passed through named argument. Secondly, the output is “2” and “2”. This is reasonable since the value of t is passed through an argument and changing the value of the argument will not affect its original value. However, python do support “real” closures. The trick is to change the t into a nonlocal variable.
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def a(): t = 1; def b(): nonlocal t; t += 1; print(t); return b; x = a(); x(); x(); |
Now we are finally there. Next up, Groovy.
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def a() { int t = 1; return { -> println(++t)}; } x = a(); x(); x(); |
Since in Groovy, named functions cannot be defined in another function, we can only use unnamed function to do this. The output is “2” and “3”.
In Java, functions are not first-class members, thus we will never have terms like closure. A workaround for this is to use anonymous class. Here is an example.
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public class Closure { public static Function a() { final int t = 1; return new Function(){ @Override public void call() { System.out.println(t + 1); } }; } public static void main(String[] args) { Function x = a(); x.call(); x.call(); } } interface Function { public void call(); } |
Rule No.1 for anonymous class is that you cannot change the value of the variables that exist in the stack context. In other words, the variables on the stack are all “final” to the inner class. If one want’s to change the value of it, it must be declared as a field of a Class, which will be stored in heap.
The performance of ListView on Android is sometimes a disaster when it comes to very complex list. Things become more frustrating when you are working with other things with Android like network images and dynamic loading. The best example of a complex ListView is the Facebook feed in the Android app. They posted an article to show how complex but smooth ListView can be achieved at the same time.
https://code.facebook.com/posts/879498888759525/fast-rendering-news-feed-on-android/
In short, they split each post in the feed into several parts: the header, the main body and the action panel. Then each part will be able to be reused when rendering the ListView. This is a very clever alternative solution to the problematic ListView performance.
However, for my case https://github.com/cfan8/TGFC things are more complicated since the content of a post does not have a fixed style. It may be pure text, or text with some decorations, or full of images without a single line of text. Moreover, the content of the post should be interactive, i.e., when you click on a link or an image, the app should respond with different actions with that click.
In a previous open source Android app that I contributed to, we tried at least two options.
When I was figuring out the solution for the new app TGFC, I was thinking about what kind of solution I should implement. However, I realized that a WebView may not be the only option for my scenario since actually I don’t really need all the features that a heavy WebView provides. I want to have my app to be able to show some different styles of text, several images and that’s all. I don’t need stuffs like z-index or absolute positioning. In my case, TextView can work perfectly to meet my demands.
I started with a ListView of TextViews. At first, everything works fine when there is only text content in the TextView. However, things become a little bit complicated when I introduced network images into my app. Inside the ImageGetter that I was using, I first download images asynchronously to the local cache. Later on I load those images into memory and show them on the screen. I see notable lag when using ListView as the outside container when it was loading images from the local cache, so I switched to ScrollView later and rendered the whole page of posts at once.
The only thing that we need to be careful about is the usage of images. Large images can consume a lot of memory and make the ScrollView really laggy. Remember to resize those images when loading them into the memory.
Right now I still have some issues with the interaction between my TextView and the other parts of the app. I don’t have time to fix those issues and see whether they come from the HTML that Jsoup generates or the way I use TextView for the time being. But I’m pretty confident that these problems are not unsolvable and actually I have got some ideas on how to handle them. For the time being, TextView + ScrollView may be the solution for extreme complex and dynamic ListView with good user experience with better memory performance than WebView + ScrollView if you do not want to parse HTML and analyze the content to distinguish text parts and image parts.
This article is written as a complement to my Zhihu answer. In my opinion, the reason why we are having so many problems with ListView is the problematic designing of ListView that comes out from Google. Here are my suggestions on how to improve the performance of ListView from the Android designing perspective.
前两天Google出了新的Support Library v22.1，这两天轻度使用了一下，贴点使用感受，慢慢更新