Giraph in action (MEAP) ; 5. What’s Apache Giraph : a Hadoop-based BSP graph analysis framework • Giraph. Hi Mirko, we have recently released a book about Giraph, Giraph in Action, through Manning. I think a link to that publication would fit very well in this page as. Streams. Hadoop. Ctd. Design. Patterns. Spark. Ctd. Graphs. Giraph. Spark. Zoo. Keeper Discuss the architecture of Pregel & Giraph . on a local action.

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In Superstep 1 of Figure 3each vertex sends its value to its neighbour vertex. MapReduce is suitable for processing flat data structures such as vertex-oriented taskswhile propagation is optimized for edge-oriented tasks on partitioned graphs.

Learn more about the Surfer system. Thus, a crucial need remains for distributed systems that can effectively support scalable processing of large-scale graph data on clusters of horizontally scalable commodity machines.

Finally, it stores the compressed cation together with some meta information into a graph database. A vertex can return to the active status if im receives a message in the execution of any subsequent superstep.

Both Pregel and GraphLab depend on graph partitioning to minimize communication and ensure work balance.

In Giraph, graph-processing programs are expressed as a sequence of iterations called girph. Messages are typically sent along outgoing edges, but you can send a message to any vertex with a known identifier. Google estimates that the total number of web pages exceeds 1 trillion; experimental graphs of the World Wide Web contain more than 20 billion nodes pages and billion edges hyperlinks.

Surfer resolves network-traffic bottlenecks with graph partitioning adapted to the characteristics of the Hadoop distributed environment. All active vertices run the compute user function at each superstep.


PEGASUS supports typical graph-mining tasks such as computing the diameter of the graph, computing the radius of each node, and finding the connected components through a generalization of matrix-vector multiplication. It also sends, receives, and assigns messages with other vertices. For example, the basic MapReduce programming model does not directly support iterative data-analysis applications.

Social network graphs are growing rapidly. From the graph-processing point of view, the basic MapReduce programming model is inadequate because most graph algorithms are iterative and traverse the graph in some way. Explore a wealth of articles and other resources on Apache Hadoop and its related technologies. The project started in at Carnegie Mellon University.

You need also to define a VertexOutputFormat to write back the result for example, vertex, pageRank. However, they differ in how they collect and disseminate information.

At the conclusion of the article, I also briefly describe some other open source projects for graph data processing. A graph that has many nodes with few connections, and few nodes with many connections. Surfer is an experimental large-scale graph-processing engine that provides two primitives for programmers: Hence, program execution ends when at one stage all vertices are inactive.

The locations of these buffered pairs on the local disk are passed back to the designated master program instance, which is responsible for forwarding the locations to the reduce guraph. Before GBASE runs the matrix-vector multiplication, it selects the grids that contain the blocks that are relevant to the input queries. InGoogle introduced the Pregel system as a scalable platform for implementing graph algorithms see Related girph.

In contrast, Pregel update functions are initiated by messages and can only access the data in the message, limiting what can be expressed.

Processing large-scale graph data: A guide to current technology

The default partition mechanism is hash-partitioning, but custom partition is also supported. The key feature of GBASE is that it unifies node-based and edge-based queries as query vectors and unifies different operations types on the graph through matrix-vector multiplication on the adjacency and incidence matrices.


Visit the GraphLab project site. The GraphLab abstraction also implicitly defines the communication aspects of the gather and scatter phases see GraphLab in action later in the article by ensuring that changes made to the vertex or edge data are automatically visible to adjacent vertices. A worker starts the compute function for the active vertices. The buffered data is then sorted by the intermediate keys so that all occurrences of the same key are grouped.

Apr Giraph in Action

Then, it compresses all nonempty blocks through a standard compression mechanism such as GZip. The remainder of the article describes and compares two such systems in depth:.

Apache Hama Apache Hama: The Reduce function receives an intermediate key with its set of values and merges them together. The process of finding a path connection between two nodes vertices in a graph such that the number of its constituent edges is minimized. Check out Wikipedia’s article on BSP. These two proposals promise only limited success:. Hence, in Superstep 2, only the vertex with value 1 updates its value to higher received value 5 and sends its new value.

Bulk synchronous parallel Bulk synchronous parallel: Periodically, the buffered pairs are written to local disk and partitioned into regions by the partitioning function.

On a machine that performs computation, it keeps vertices and edges in memory and uses network transfers only for messages.