Cassovary is designed from the ground up to efficiently handle graphs with billions of edges. It comes with some common node and graph data structures and traversal algorithms. A typical usage is to do large-scale graph mining and analysis.

If you are reading this you’ve most probably heard of Pregel—if you didn’t then you should check out the Pregel: a system for large-scale graph processing paper and then how Pregel and MapReduce compare—and also the 6 Pregel inspired frameworks.

The Cassovary project page introduces it as:

Cassovary is a simple “big graph” processing library for the JVM. Most JVM-hosted graph libraries are flexible but not space efficient. Cassovary is designed from the ground up to first be able to efficiently handle graphs with billions of nodes and edges. A typical example usage is to do large scale graph mining and analysis of a big network. Cassovary is written in Scala and can be used with any JVM-hosted language. It comes with some common data structures and algorithms.

I’m not sure yet if:

1. Cassovary works with any graphy data source or requires FlockDB—which is more of a persisted graph than a graph database
2. Cassovary is inspired by Pregel in any ways or if it’s addressing a limited problem space (similarly to FlockDB)

Update: Pankaj Gupta helped clarify the first question (and probably part of the second too):

At Twitter we use flockdb as our real-time graphdb, and export daily for use in cassovary, but any store could be used.

Original title and link: Big Graph-Processing Library From Twitter: Cassovary (NoSQL database©myNoSQL)

via: http://engineering.twitter.com/2012/03/cassovary-big-graph-processing-library.html

A quick overview of 6 Pregel-inspired frameworks (Apache Hama, GoldenOrb, Apache Giraph, Phoebus, Signal/Collect, and HipG):

So, to summarize, what Hama, GoldenOrb and Giraph have in common is: Java platform, Apache License (and incubation), BSP computation. What they differ for: Hama offers BSP primitives not graph processing API (so it sits at a lower level), GoldenOrb provides Pregel’s API but requires the deployment of additional software to your existing Hadoop infrastructure, Giraph provides Pregel’s API (and is kind of complete at the current state) and doesn’t require additional infrastructure.

Original title and link: 6 Pregel-Inspired Frameworks (NoSQL database©myNoSQL)

via: http://blog.acaro.org/entry/google-pregel-the-rise-of-the-clones

Marko A.Rodriguez:

In the distributed traversal engine model, a traversal is represented as a flow of messages between elements of the graph. Generally, each element (e.g. vertex) is operating independently of the other elements. Each element is seen as its own processor with its own (usually homogenous) program to execute. Elements communicate with each other via message passing. When no more messages have been passed, the traversal is complete and the results of the traversal are typically represented as a distributed data structure over the elements. Graph databases of this nature tend to use the Bulk Synchronous Parallel model of distributed computing. Each step is synchronized in a manner analogous to a clock cycle in hardware. Instances of this model include Agrapa, Pregel, Trinity, GoldenOrb, and others.

None of these graph databases offers distributed traversal engines.

Original title and link: Graph Databases: Distributed Traversal Engine (NoSQL databases © myNoSQL)

via: http://markorodriguez.com/2011/04/19/local-and-distributed-traversal-engines/

Ravel, an Austin, Texas-based company, wants to provide a supported, open-source version of Google’s Pregel software called GoldenOrb to handle large-scale graph analytics.

Is it a new graph database or a Pregel implementation? Watch the interview for yourself and tell me what do you think it is?

via: http://gigaom.com/cloud/ravel-hopes-to-open-source-graph-databases/