Apache Giraph is a high-scalability iterative graph processing framework. It is a real-time graph processing program mainly used to analyze social media data. Yahoo, Facebook, and Twitter are all Giraph users who have customized the program to their specific needs.
With the rise of Facebook, LinkedIn, and other new social networks, Big Data challenges have given way to Big Graph problems as people attempt to analyze large graphs, such as determining the shortest path between two nodes. Giraph supports this logic and is a method to parallelize the processing of a graph. Hadoop and its ecosystem are designed to process massive distributed amounts of data, and Giraph follows this logic and is a means to parallelize the treatment of a graph.
Graph-processing programs in Giraph are represented as a series of iterations known as supersteps. During a superstep, the framework theoretically parallelizes a user-defined function for every vertex. The user-defined function specifies the behavior at a single vertex V and a particular superstep S.
The function can read the messages sent to V in superstep S-1, convey messages received in superstep S+1 to other vertices, and alter the state of V and its outgoing edges. Messages are customarily transmitted along outgoing edges, although any vertex with a known identification can receive a notification. Each superstep represents atomic parallel computing units.
Natural language-based relational searches enabled by the software make social network data more useful. Giraph may be used to display relationships between entities in a social graph. The nodes in a graph are the entities, and the edges represent the interactions or connections between them.
Giraph, like the Hadoop platform, is a fast, robust, and fault-tolerant implementation on thousands of commodity machines, with distribution-related complexities concealed behind an abstraction. Vertices and edges are kept in memory on a computation machine, and network transfers are used exclusively for messages.
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