艺人也玩豆瓣，这其实并不算新闻。陈珊妮就多次在采访中提到“每天都会上网看自己的豆瓣小组，写自己的‘公主日志’”。“搞怪天王”陈医生也曾在采访中透露，在AT17的推荐下，自己会去豆瓣看大家对他的讨论，“有善意的也有恶意的”（以此推断AT17也是豆友，但至今潜伏的很深，不知有谁发现没？）。陈柏霖同学经常在半夜上传自拍照到豆瓣相册，真是个自恋的同学。据说豆瓣的“锦东”是范植伟，他推荐自己小组的推荐语直白的可爱。

好吧，铺垫了这么久，其实想说的是：张悬今晚会来豆瓣。

时间：21:30

地点：音乐人论坛

方式：版聊

你现在仍然有时间可以来这里留下你想问的问题。

也就是说，在豆瓣想要遇见自己欣赏的艺人，除了猜来猜去的捉迷藏，也可以这么名正言顺的和ta约个会。   

于是你可以期待，张悬已经来了，某某某（请大家自觉代入自己心水的艺人的名字，代入“孙燕姿”的飘走。）还会远吗？^_^

另一个有意思的方面在于，消失于我们视线很久的版聊又再度发烧了。也许今天你可以选择：先用蜂花舒舒服服洗个头，然后换上海魂衫、回力鞋，喝着健力宝，边用英雄钢笔给你的ta写情书，边不断刷新页面，和互联网那头的张悬进行版聊。

复古和怀旧真是近年的潮流啊，也许过段时间电台会比IPOD更受宠，谁知道呢。

With new updates of FusionIO drivers I was able to test it on our Dell R900 with Ubuntu 8.10 without pain of compiling drives myself and downgrading to older kernel, so I was decided to test it in strict_sync mode.

As I understand FusionIO in default mode, like Intel SSD, is “lying” to application, and fsync() is not real, it still commit only to internal memory, not to final media. And FusionIO has “strict_sync” mode to guarantee fsync is trustworthy.

So let’s benchmark it - as usually I do tpcc-mysql benchmark on 100W (9GB data) with 3GB buffer_pool in O_DIRECT access.

The raw number are on page http://spreadsheets.google.com/ccc?key=rJyFMg6NciGGaRMDUhGR0zw

and graph is
.
Results are in TPM (Transactions Per Minute), more is better, and graph shows how result is changing in time ( axis X ).
It is too obvious from graph to comment it…

I actually did not test if you really loose transactions in case of power outage in default mode, this is something to check.

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Entry posted by Vadim | No comment

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作者 Google 广告管理系统小组 李小轲

1. 使用系统管理网站旗下广告位置
2. 创建关联展示位置
3. 生成广告位置对应代码
4. 为广告主创建订单
5. 创建订单项 (3)

4) 投放选项：在这里我们可以为此订单项设置具体的投放需求。我结合几个示例向大家具体介绍这几个功能：



- 投放速度：在此定义订单的投放速度。

• 均匀：Google 广告管理系统会在订单项定义的天数内尽可能均匀地展示广告。
• 尽快：Google 广告管理系统会停止在所选展示位置中投放优先级或价值较低的订单项，直到满足了设置为"尽快"的订单项的数量要求为止。 此选项可以在此订单项的指定结束日期之前，尽快投放所有预订展示。

在这里为大家举一个例子。如果现在网站首页有一个特定广告位，每日流量接近100万。现在我针对此广告位设置一个广告订单，希望在一周7天之前投放70万展示量，按CPM购买。如果我在这里选择了“尽快”，系统会在订单开始的第一天就开始投放此广告，直到70万的展示量完成为止。如果订单开始的第一天此广告位就有100万的流量，那么这个订单在一天之内就会完成，而无需等到第7天。如果我在这里选择“均匀”，系统会自动把70万的展示需求分到7天的时间内，平均每天需要10万的展示量。

- 投放广告：如果此订单投放到一个页面的多个广告位，通过这个选项，我们可以设置页面显示此订单广告的数量。

我用一个示例向大家介绍这个功能的使用。现有一个汽车类广告主A, 希望在某门户网站投放广告，在一定订单项中购买了一个页面的三个位置。

• 无限制（默认）：默认选项，当您选择此选项时，不对页面上显示的广告做任何设定
• 每页一个：针对每一次用户浏览在三个广告位中只显示一个您的广告。如果您认为在同一页面显示三个类似的广告，会影响用户体验，此选项就很实用。
• 包版（填充所有可能的广告位置）：针对每一次用户浏览，三个位置都显示您的广告，无论广告类型优先级如何。此选项适用于看中广告环境的广告主，例如他不希望和竞争对手的广告出现在一个页面。

- 广告轮换：如果您担心针对用户浏览，页面显示同样的广告，会影响用户体验。我们可以针对同一个广告位置上传多个素材，并在此指定素材的显示规则。

• 均匀：如果您为包含多个广告素材的订单项选择"均匀轮换"选项，那么，该订单项中的所有广告素材将均匀随机轮换。 例如，在广告素材B 展示之前，广告素材A 可能已展示了三次。 但是，在广告素材系列的整个投放过程中，广告素材A 和广告素材B 的展示次数将基本相等。
• 优化：如果订单项有多个广告素材，且您选择了优化轮换选项，则 Google 广告管理系统会将大约 75% 的展示机会给予点击率最高的广告素材，而将剩余约 25% 的展示机会给予其他广告素材。每隔几千次展示，系统即会对此项轮换进行更新。 如果当初仅获得 25% 展示机会的广告素材有了更高的点击率，那么，其投放次数将增加。
• 手动权重：如果您为包含多个广告素材的订单项选择"手动轮换"选项，那么，每个广告素材将根据您指定的频次进行轮换。您为某个广告素材输入的权重，将除以所有广告素材的总权重。 例如，假设您有三个广告素材，其权重如下：

a) 广告素材 A，权重为 5
b) 广告素材 B，权重为 10
c) 广告素材 C，权重为 10

要确定某个广告素材的展示频次，您可以用所有广告素材的总权重 25，除相应广告素材的权重。

a) 广告素材 A：5/25 = 20%
b) 广告素材B：10/25 = 40%
c) 广告素材C：10/25 = 40%

- 频次：通过频次上限功能，我们可以了解到每个用户和特定广告的互动。这一 Google 广告管理系统功能可帮助您:

• 限制订单项中某个广告对同一用户的投放频率
• 指定某个特定时段内对同一用户投放的展示次数
• 指定时间范围，以分钟、小时、天、周或月为单位。 也可指定一个有效期上限
• 应用多个规则。 例如，可设置一个频次上限，限制对同一用户每天至多展示一次、每周至多展示三次

举个例子，通过这个功能，我可以指定广告订单A，针对每一个用户，一天只能显示2次。从而最大化利用广告位置的流量。

在这里，我们就将整个订单项界面的设置介绍了一遍。下面我们将介绍如何上传广告素材。

To continue the graph theme Google has got into the act and released information on Pregel. Pregel does not appear to be a new type of potato chip. Pregel is instead a scalable infrastructure...

...to mine a wide range of graphs. In Pregel, programs are expressed as a sequence of iterations. In each iteration, a vertex can, independently of other vertices, receive messages sent to it in the previous iteration, send messages to other vertices, modify its own and its outgoing edges' states, and mutate the graph's topology.

Currently, Pregel scales to billions of vertices and edges, but this limit will keep expanding. Pregel's applicability is harder to quantify, but so far we haven't come across a type of graph or a practical graph computing problem which is not solvable with Pregel. It computes over large graphs much faster than alternatives, and the application programming interface is easy to use. Implementing PageRank, for example, takes only about 15 lines of code. Developers of dozens of Pregel applications within Google have found that "thinking like a vertex," which is the essence of programming in Pregel, is intuitive.

Pregel does not appear to be publicly available, so it's not clear what the purpose of the announcement could be. Maybe it will be a new gmail extension :-)

When I'm looking at a server, I often want to see the /etc/my.cnf file nicely formatted, and with comments stripped. This Perl one-liner will pretty-print the file:

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Entry posted by Baron Schwartz | 8 comments

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The first meeting of this Online Gaming High Scalability SIG will be on the 9th of July 2009 in central London, starting at 10 AM and finishing around 5PM.

The main topic of this meeting will be potentials for using cloud and grid technologies in online gaming systems. In addition to experience reports from the community, we have invited some of the leading cloud experts in the UK to discuss the benefits such as resource elasticity and challenges such as storage and security that companies from other industries have experienced. We will have a track for IT managers focused on business opportunities and issues and a track for architects and developers more focused on implementation issues.

The event is free but up-front registration is required for capacity planning, so please let us know in advance, if you are planning to attend by completing the registration form on this page

To propose a talk or for programme enquiries, contact meetings [at] gamingscalability [dot] org.

Note: The event is planned to finish around 5 PM so that people can make their way to Victoria on time for CloudCamp London. CloudCamp is a meeting of the cloud computing community with short talks, is also free but you will have to register for it separately

PROGRAMME: http://skillsmatter.com/event/cloud-grid/online-gaming-high-scalability-...

What is Kngine?

Kngine is Knowledge Web search engine designed to provide meaningful search results, such as: semantic information about the keywords/concepts, answer the user’s questions, discover the relations between the keywords/concepts, and link the different kind of data together, such as: Movies, Subtitles, Photos, Price at sale store, User reviews, and Influenced story

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