是否有类似开源 Pregel 的框架用于大型图的分布式处理？
谷歌描述了一种用于大规模图分布式处理的新颖框架。
http://portal.acm.org/itation.cfm?id=1582716.1582723
我想知道是否有类似于Hadoop（Map-Reduce）的这个框架的开源实现？
我实际上正在使用 python 和多处理模块编写一个伪分布式程序，因此想知道其他人是否也尝试过实现它。 由于有关该框架的公开信息极其稀少。 （上面的链接和 Google Research 的博客文章）
Google has described a novel framework for distributed processing on Massive Graphs.
http://portal.acm.org/citation.cfm?id=1582716.1582723
I wanted to know if similar to Hadoop (Map-Reduce) are there any open source implementations of this framework?
I am actually in process of writing a Pseudo distributed one using python and multiprocessing module and thus wanted to know if someone else has also tried implementing it.
Since public information about this framework is extremely scarce. (A link above and a blog post at Google Research)
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Apache Giraph 目前处于孵化器中，并由来自 LinkedIn 的提交者进行非常积极的开发、Twitter、Facebook 和学术界希望尽快将其投入生产规模。它非常直接地模仿 Pregel，最初是在 Yahoo! 开发的。研究。我们正在寻找新的贡献者，并有几个介绍性 JIRA 问题来帮助人们开始使用该项目。我们很乐意您参与其中。
Apache Giraph is currently in Incubator and under very active development, with committers from LinkedIn, Twitter, Facebook and academia looking to bring it up to production scale very quickly. It is pretty directly modeled on Pregel and was originally developed at Yahoo! Research. We're looking for new contributors and have several introductory JIRA issues to help people get started with the project. We'd love to have you get involved.
斯坦福大学的学生开发了 Pregel 的开源实现。
http://infolab.stanford.edu/gps/
Stanford Students have developed an open Source implementation of Pregel.
http://infolab.stanford.edu/gps/
用于分布式图形处理的主要 Hadoop 项目是 Hama 项目。但它仍处于孵化阶段。
该项目将其工作分为两个领域：矩阵包和图形包。
更新：
更好的选择是 Apache Giraph 项目，该项目基于在谷歌预凝胶上。
The main Hadoop project for distributed graph processing is the Hama project. Its still in incubation though.
The project has broken its work into two areas; a matrix package and a graph package.
Update:
A better option would be the Apache Giraph project which is based on Google Pregel.
是的，一个名为 Golden Orb 的新项目，它是一个用 Java 编写的开源 Pregel 实现，可以在 HBASE 和 Cassandra 上运行。
它已提交给 Apache 孵化器审批，Golden Orb 背后的公司 Ravel 表示他们将于本月发布它 (http://www.raveldata.com/goldenorb/）。
请参阅 http://www.quora。 com/Graph-Databases/What-open-source-graph-databases-support-horizontal-scaling
更新：GraphX 是 GraphLab2，位于 Spark 由 Joey Gonzalez，GraphLab2 的创建者。
Spark 独特的原语使 GraphX-Pregel 成为最快的基于 JVM 的 Pregel 实现。 Spark 是用 Scala 编写的，但 Spark 有 Java 和 Python API。
请参阅...
PS 还有Bagel，这是第一个剪辑在 Spark 上的 Pregel 上。有用;然而，GraphX 将是前进的方向。
Yes, a new project called Golden Orb, which is an open-source Pregel implementation written in Java that runs on both HBASE and Cassandra.
It has been submitted to Apache incubator for approval, and Ravel, the company behind Golden Orb, said they are releasing it this month (http://www.raveldata.com/goldenorb/).
See http://www.quora.com/Graph-Databases/What-open-source-graph-databases-support-horizontal-scaling
UPDATE: GraphX is GraphLab2 on Spark implemented by Joey Gonzalez, the creator of GraphLab2.
Spark's unique primitives make GraphX-Pregel the fastest JVM-based Pregel implementation. Spark is written in Scala, but Spark has a Java and Python API.
See...
P.S. There is also Bagel, which was the first cut at Pregel on Spark. It works; however, GraphX will be the way forward.
卡内基梅隆大学的两个项目提供了 Pregel 式计算
在图表上：
编程模型与 Pregel 并不完全相同，因为它们不是基于消息传递，而是直接修改图（边、顶点）数据。基本上，在这些框架中模拟 Pregel 是很容易的。
Two projects from Carnegie Mellon University provide Pregel-style computation
on graphs:
The programming model is not exactly same as Pregel, as they are not based on messaging but on modifying the graph (edge, vertex) data directly. Basically, it is easy to emulate Pregel in these framework.
还有 Signal/Collect 一个用 Scala 编写的框架，现在使用 Akka
http://code.google.com/p/signal-collect/
https://github.com/uzh/signal-collect
来自他们的网站：
在 Signal/Collect 中，算法是从顶点和边的角度编写的。一旦指定了图形，边将发出信号并且顶点将收集。当边发出信号时，它会根据其源顶点的状态计算消息。然后该消息沿着边发送到边的目标顶点。当顶点收集时，它使用接收到的消息来更新其状态。这些操作在整个图中并行发生，直到收集到所有消息并且所有顶点状态都收敛。
许多算法在 Signal/Collect 中有非常简单和优雅的实现。您可以在项目 wiki 中找到有关编程模型和功能的更多信息。请花时间探索下面的一些示例算法。
There is also Signal/Collect a framework written in Scala and now using Akka
http://code.google.com/p/signal-collect/
https://github.com/uzh/signal-collect
From their website:
In Signal/Collect an algorithm is written from the perspective of vertices and edges. Once a graph has been specified the edges will signal and the vertices will collect. When an edge signals it computes a message based on the state of its source vertex. This message is then sent along the edge to the target vertex of the edge. When a vertex collects it uses the received messages to update its state. These operations happen in parallel all over the graph until all messages have been collected and all vertex states have converged.
Many algorithms have very simple and elegant implementations in Signal/Collect. You find more information about the programming model and features in the project wiki. Please take the time to explore some of the example algorithms below.
我创建了一个名为 Phoebus 的框架。它是用 Erlang 编写的 Pregel 的实现。查看我的博客条目，将 Pregel 模型应用于寻路也是..
I create a framework called Phoebus. It is an implementation of Pregel written in Erlang. Checkout my blog entry for applying Pregel model to path finding as well..