What is Pregel algorithm?

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 analysis?

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 graph processing?

A graph processing framework (GPF) is a set of tools oriented to process graphs. Graph vertices are used to model data and edges model relationships between vertices. Since real graphs can be large, complex, and dynamic, GPFs have to deal with the three challenges of data growth: volume, velocity, and variety.

What is the Think Like a vertex mode of programming?

vertex-centric programming model, where users express their al- gorithms by “thinking like a vertex”. In Pregel, a common vertex-centric computation involves receiving messages from other vertices, updating the state of itself and its edges, and sending mes- sages to other vertices.

What is GraphX spark?

Overview. GraphX is a new component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex and edge.

Is a graph processing engine designed to work in Hadoop?

Graph analysis in Hadoop With the advent of YARN in Hadoop 2, graph analysis and other specialized processing techniques will become increasingly popular on Hadoop. It’s solely a processing engine because it loads data as a graph into the cluster’s memory, and it’s optimized for batch-oriented queries.

Why do we need graph?

Graphs are a common method to visually illustrate relationships in the data. The purpose of a graph is to present data that are too numerous or complicated to be described adequately in the text and in less space. If the data shows pronounced trends or reveals relations between variables, a graph should be used.

What is vertex centric model?

The vertex-centric programming model is an established computational paradigm recently incorporated into distributed processing frameworks to address challenges in large-scale graph processing.

What is a triplet in GraphX?

In addition to the vertex and edge views of the property graph, GraphX also exposes a triplet view. The triplet view logically joins the vertex and edge properties yielding an RDD[EdgeTriplet[VD, ED]] containing instances of the EdgeTriplet class. This join can be expressed in the following SQL expression: SELECT src.

What is GraphX used for?

GraphX is a new component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex and edge.

Why spark is 100x faster than MapReduce?

The biggest claim from Spark regarding speed is that it is able to “run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.” Spark could make this claim because it does the processing in the main memory of the worker nodes and prevents the unnecessary I/O operations with the disks.

What are the two main components of Hadoop?

HDFS (storage) and YARN (processing) are the two core components of Apache Hadoop.