在西非的电子废墟中，孩子们正在帮助清理被发达国家丢弃的计算机。当他们从中提取金属来赚钱的同时，也正在付出高昂的健康代价，理查德·雷报道。

每天，数以千计的被欧美丢弃的计算机抵达西非的港口，产生了大量有毒垃圾。在那里，孩子们对这些垃圾进行焚烧和拆解，提取金属以赚取现金。

工作职责：
-同城活动的市场调研
-确定同城活动的对外合作模式，独立开展合作洽谈，开拓合作伙伴
-进行用户调查，帮助同城进行产品改进
-同城活动内容的日常维护

职位要求：
-大学本科或以上学历，专业不限，传媒、经济、社会学学科背景更佳
-有市场研究、市场推广、品牌公关相关工作经验
-对互联网产品的市场以及合作模式有一定了解
-出色的分析能力，良好的人际沟通和协调能力

工作地点位于北京。有兴趣或者问题请email至 team(a)douban.com。请注明“同城”。

欢迎转载。谢谢！

Gmail即将推出大量实验性功能供用户测试，这里列举了一些即将推出的新功能。

1，自定义标记（Superstar）
将可以对不同的邮件加上不同的图标标签，而不只是打星。

2，快速链接（Quick Links）
可以保存各种功能、分类或者是单封邮件的链接以便快速访问。

3，休息功能（Email Addict）
课间休息，让你停止收发邮件15分钟。

4，贪吃蛇（Snakey）
是的，让你在等待邮件的时候消磨时间。

5，随即签名（Random Signature）
可以用某个Feed中的条目来为邮件随机签名。

6，自定义快捷键（Custom Keyboard Shortcuts）
可以用符合你习惯的快捷键来操作Gmail。

7，鼠标手势（Mouse gestures）
使用鼠标手势来操作Gmail。

8，口罩（Muzzle）
隐藏联系人的状态。

9，聊天图片
在Gmail中使用Talk聊天也可以显示图片了。

10，智能回复签名
把签名档自动放在最后一条引用之前。

11，固定字体
使用固定宽度的字体来查看邮件，防止格式错乱。

在Lifehacker可以看到这些新功能的截图和更详细的说明。

Backing up MySQL Database most people compress them - which can make a good sense in terms of backup and recovery speed as well as space needed or be a serious bottleneck depending on circumstances and approach used.

First I should mention this question mainly arises for medium and large size databases - for databases below 100GB in size compression performance is usually not the problem (though backup impact on server performance may well be).

We also assume backup is done on physical level here (cold backup, slave backup, innodb hot backup or snapshot backup) as this is only way practical at this point for databases of decent size.

Two important compression questions you need to decide for backup is where to do compression (on the source or target server if you backup over network) and which compression software to use.

Compression on source server is most typical approach and it is great, though it takes extra CPU resources on the source server in additional to IO resources which may not be available, especially for CPU bound MySQL Load. The benefit in this case is less space requirement if you’re keeping the local copy as well as less network bandwidth requirements in case you’re backing up to network storage.

Compression on the destination server offloads source server (though it may run our of CPU itself, if it is target for multiple backups, plus there are higher network bandwidth requirements to transfer uncompressed backup.

What is about compression tool ? The classical tool used for backup compression is gzip - it exists almost everywhere, it is stable and relatively fast.

In many cases however it is not fast enough and becomes the bottleneck for all the backup process.

Recently I did a little benchmark compressing 1GB binlog file with GZIP (compression done from OS cache and redirected to /dev/null so we only measure compression speed). On the test box with Intel(R) Core(TM)2 Duo CPU E4500 @ 2.20GHz CPU. GZIP would compress this file in
48 seconds (with default options) resulting in 260MB compressed file. This gives us compression speed of about 21MB/sec - clearly much less than even single SATA hard drive can read sequentially. This file when will take about 10 seconds to decompress, meaning source file will be read at 26MB/sec to do decompression - this is again much less than hard drive sequential read performance, though the fact this gives us about 100MB/sec of uncompressed data writing is more of the issue.

Such performance also means if your goal is faster local network transfer default GZIP compression will not speed things up on the standard point to point 1Gbit network connection.

If we try gzip -1 to get fastest compression we get the same file compressed to 320MB in 27 seconds. This gives us 37MB/sec which is a lot better but still not quite enough. Also note the serious leap in compressed file size. Though in this example we used MySQL binary log file which often contains plenty of similar events, which could be the reason for so large size difference based on compression ratio. The decompression takes about same 10 seconds which gives about 32MB/sec of archive read speed and same 100MB/sec of uncompressed data.

Do we have any faster alternatives to GZIP ? There are actually quite a few but I like LZO which I was playing with since later 1990’s and which is rather active project. There is also GZIP like command like compressor using LZO library called LZOP which makes it easy drop in replacement.
I got LZOP binary which was built against LZO 1.0, more resent version 2.0 promises further performance improvements especially on 64bit systems.

With LZO default compression file compressed in 10.5 seconds and resulted in 390MB compressed file, this gives us 97MB/sec compression speed which is good enough to compress all data you can read from single drive. The file decompresses in 3.7 seconds which gives 105MB/sec read speed from archive media and 276MB/sec write speed to the hard drive - this means restoring from backup compressed with LZO will often be as fast or faster as from not compressed one.

With LZO there is also “-1″ option for even faster compression which had rather interesting results. The file compressed in 10.0 seconds (102MB/sec) and was 385MB in size - so this lower compression rate actually compressed this a bit better while being about 5% faster. The decompression speed was about the same. I’m sure the results may change based on the data being compressed but it looks like LZO uses relatively fast compression by default already.

With real server grade CPU deployment the performance should be even better, meaning you should get over +-100MB/second you can pass through 1Gbit ethernet, meaning you actually can use LZO compression for faster data transfer between the boxes (ie together with netcat)

Now as in my benchmarks there is also overhead of reading (from file cache) and piping to the /dev/null which are constant the true difference in compression speed is even larger, though as most of backup operations will need reading and writing anyway they come with this static overhead naturally added.

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Entry posted by peter | 9 comments

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继 UCSD 的 J. E. Hirsch 提出评价研究者成绩的方法，h-index ，之后，出现了众多变体。来自中国科技大学的 Qiang Wu 最近往 arXiv 贴了篇文章：The w-index: A significant improvement of the h-index，提出了个新变体 w-index。

这个 w-index 和 h-index 很类似，是这么定义的：A researcher has index w if w of his/her papers have at least 10w citations each, and the other papers have fewer than 10(w+1) citations. 如果一个研究者有 w 篇文章至少有 10w 个引用，而其它文章的引用数少于 10(w+1)，那么他/她的指数为 w。

（h-index 的定义：A scientist has index h if h of his/her N papers have at least h citations each, and the other (N − h) papers have no more than h citations each. 如果一个科学家有 h 篇文章至少有 h 个引用，且其它 (N - h）篇文章的引用数不超过 h，那么他/她的指数为 h。）

举个例子，霍金的指数为 24，意思是说他有 24 篇文章曾被引用过至少 240 次，同时他没有 25 篇文章被引用至少 250 次。说起来比较饶口。

w 与 h 的差别在于它更强调一个科学家最顶尖的文章。两种指数做出来的排名很不一样，比如，物理学家按 w 指数排名前五位：威腾（41），安德森（26），霍金（24），Marvin Cohen（23），Frank Wilczek（23）；按 h 指数前五位：威腾（110），Marvin Cohen（94），安德森（91），Steven Weinberg （88），Michael Fisher（88）。

Qiang Wu 的文章处处模仿 J. E. Hirsch 的，比如 J. E. Hirsch 说：工作 20 年后，一个“成功的科学家”的指数应达到 20；一个“出色的科学家”的指数应达到 40；而一个“真正天才人物”的指数会达到 60。 另外，他接着建议，一个研究者应该被提升为副教授如果他/她的 h 指数达到约 12，如果达到 18，那么应该提升为正教授。他自己的 h 指数为 49。

Qiang Wu 的文章里模仿道：

i) w 指数为 1 或者 2，表示该研究者已经学到了一个课题的基本。
ii) w 指数为 3 或者 4，表示该研究者已经掌握了 the art of scientific activity。
iii) w 指数为 5，表明他是位成功的研究者。
iv) w 指数为 10，表明他是为出色的科学家。
v) 工作 20 年后 w 指数超过 15，或者 30 年后指数超过 20，那就是顶尖科学家了。

请半严肃，半戏谑地看待这些文章。

相关链接：
What's your Wu index?
Number theory

继 UCSD 的 J. E. Hirsch 提出评价研究者成绩的方法，h-index ，之后，出现了众多变体。来自中国科技大学的 Qiang Wu 最近往 arXiv 贴了篇文章：The w-index: A significant improvement of the h-index，提出了个新变体 w-index。

这个 w-index 和 h-index 很类似，是这么定义的：A researcher has index w if w of his/her papers have at least 10w citations each, and the other papers have fewer than 10(w+1) citations. 如果一个研究者有 w 篇文章至少有 10w 个引用，而其它文章的引用数少于 10(w+1)，那么他/她的指数为 w。

（h-index 的定义：A scientist has index h if h of his/her N papers have at least h citations each, and the other (N − h) papers have no more than h citations each. 如果一个科学家有 h 篇文章至少有 h 个引用，且其它 (N - h）篇文章的引用数不超过 h，那么他/她的指数为 h。）

举个例子，霍金的指数为 24，意思是说他有 24 篇文章曾被引用过至少 240 次，同时他没有 25 篇文章被引用至少 250 次。说起来比较饶口。

w 与 h 的差别在于它更强调一个科学家最顶尖的文章。两种指数做出来的排名很不一样，比如，物理学家按 w 指数排名前五位：威腾（41），安德森（26），霍金（24），Marvin Cohen（23），Frank Wilczek（23）；按 h 指数前五位：威腾（110），Marvin Cohen（94），安德森（91），Steven Weinberg （88），Michael Fisher（88）。

Qiang Wu 的文章处处模仿 J. E. Hirsch 的，比如 J. E. Hirsch 说：工作 20 年后，一个“成功的科学家”的指数应达到 20；一个“出色的科学家”的指数应达到 40；而一个“真正天才人物”的指数会达到 60。 另外，他接着建议，一个研究者应该被提升为副教授如果他/她的 h 指数达到约 12，如果达到 18，那么应该提升为正教授。他自己的 h 指数为 49。

Qiang Wu 的文章里模仿道：

i) w 指数为 1 或者 2，表示该研究者已经学到了一个课题的基本。
ii) w 指数为 3 或者 4，表示该研究者已经掌握了 the art of scientific activity。
iii) w 指数为 5，表明他是位成功的研究者。
iv) w 指数为 10，表明他是为出色的科学家。
v) 工作 20 年后 w 指数超过 15，或者 30 年后指数超过 20，那就是顶尖科学家了。

请半严肃，半戏谑地看待这些文章。

相关链接：
What's your Wu index?
Number theory