How does Pregel work?

How does Pregel work?

Pregel is essentially a message-passing interface constrained to the edges of a graph. The idea is to ”think like a vertex” – algorithms within the Pregel framework are algorithms in which the computation of state for a given node depends only on the states of its neighbours.

What is Pregel in big data analytics?

The basic idea of Pregel is that we implement an algorithm that is executed on every vertex of a graph. It receives all messages from neighbor vertices and can optionally send messages to other vertices or update vertex value. Messages sent by this function will be received on the next iteration.

What is Pregel API?

Introduction. Pregel is a vertex-centric computation model to define your own algorithms via a user-defined compute function. Within that function, a node can receive messages from other nodes, typically its neighbors. Based on the received messages and its currently stored value, a node can compute a new value.

What is the purpose of GraphX?

GraphX unifies ETL, exploratory analysis, and iterative graph computation within a single system. You can view the same data as both graphs and collections, transform and join graphs with RDDs efficiently, and write custom iterative graph algorithms using the Pregel API.

What is Spark Pregel?

Simple Pregel in Spark. Separate RDDs for immutable graph state and. for vertex states and messages at each iteration. Use groupByKey to perform each step. Cache the resulting vertex and message RDDs.

What is Spark GraphX used for?

What is Spark GraphX? GraphX is the Spark API for graphs and graph-parallel computation. It includes a growing collection of graph algorithms and builders to simplify graph analytics tasks. GraphX extends the Spark RDD with a Resilient Distributed Property Graph.

What is unique feature of GraphX?

Speed. Speed is one of the best features of GraphX. It provides comparable performance to the fastest specialized graph processing systems. It is fastest on comparing with the other graph systems.

What is PageRank GraphX?

Summary: The application of PageRank extends beyond ranking of websites and can be used to find authority of vertices in any network graph. GraphX from Apache Spark provides an inbuilt implementation of PageRank which can be run at scale on any big data cluster where Spark is available.

What is synchronicity in Pregel programming?

The synchronicity makes it easier to reason about program semantics, and ensures that Pregel programs are inherently free of deadlocks and data races. Within each superstep the vertices compute in parallel, each executing the same user-defined function that expresses the logic of a given algorithm.

What is the Pregel programming model?

Let’s first take a look at Pregel’s programming model, and then we can dive into a few implementation details. The high-level organization of Pregel programs is inspired by Valiant’s Bulk Synchronous Parallel model. Pregel computations consist of a sequence of iterations, called supersteps.

What is a superstep in pypregel?

Pregel computations consist of a sequence of iterations, called supersteps. During a superstep the framework invokes a user-defined function for each vertex, conceptually in parallel. The function specifies behavior at a single vertex V and a single superstep S.

What is Google’s Pregel?

Pregel is Google’s scalable and fault-tolerant platform with an API that is sufficiently flexible to express arbitrary graph algorithms. Within Google, even as of 2010: Dozens of Pregel applications have been deployed, and many more are being designed, implemented, and tuned.

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