Contents
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.
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.
Which programming languages can be used for using GraphX?
Support for Python and Java in addition to Scala APIs. Now we can use GraphX algorithms in all three languages.
What is GraphX in Pyspark?
GraphX is the Spark API for graphs and graph-parallel computation. GraphX extends the Spark RDD with a Resilient Distributed Property Graph. The property graph is a directed multigraph which can have multiple edges in parallel. Every edge and vertex have user defined properties associated with it.
What is GraphX in PySpark?
What is Spark RDD Dataframe?
Spark RDD APIs – An RDD stands for Resilient Distributed Datasets. It is Read-only partition collection of records. It is an immutable distributed collection of data. DataFrame in Spark allows developers to impose a structure onto a distributed collection of data, allowing higher-level abstraction.
How to find strongly connected components in a graph?
For example consider the following graph. We strongly recommend to minimize your browser and try this yourself first. We have discussed algorithms for finding strongly connected components in directed graphs in following posts. Kosaraju’s algorithm for strongly connected components .
Which is an iterative graph algorithm in spark?
Many iterative graph algorithms (e.g., PageRank, Shortest Path, and connected components) repeatedly aggregate properties of neighboring vertices (e.g., current PageRank Value, shortest path to the source, and smallest reachable vertex id).
How does GraphX optimize the representation of vertex and edge types?
GraphX optimizes the representation of vertex and edge types when they are primitive data types (e.g., int, double, etc…) reducing the in memory footprint by storing them in specialized arrays. In some cases it may be desirable to have vertices with different property types in the same graph. This can be accomplished through inheritance.
Which is the default partitioning strategy in GraphX?
Users can choose between different strategies by repartitioning the graph with the Graph.partitionBy operator. The default partitioning strategy is to use the initial partitioning of the edges as provided on graph construction. However, users can easily switch to 2D-partitioning or other heuristics included in GraphX.