Concept
GraphX is Apache Spark’s API for graphs and graph-parallel computation.
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. To support graph computation, GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregateMessages) as well as an optimized variant of the pregel API. In addition, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks.
GraphX is a layer on top of Spark that provides a graph data structure composed of Spark RDDs, and it provides an API to operate on those graph data structures. GraphX comes with the standard Spark distribution, and you use it through a combination of the GraphX-specific API and the regular Spark API
GraphX is not a database. Instead, it’s a graph processing system, which is useful,
for example, for fielding web service queries or performing one-off, long-running
standalone computations. Because GraphX isn’t a database, it doesn’t handle updates
and deletes like Neo4j and Titan, which are graph databases.
Apache Giraph is another example of a graph processing system, but Giraph is limited to slow Hadoop Map/Reduce.
GraphX, Giraph, and GraphLab are all separate implementations of the ideas expressed in the Google pregel paper. Such graph processing systems are optimized for running algorithms on the entire graph in a massively parallel manner, as opposed to working with small pieces of graphs like graph databases.
#画一个类似于标准关系数据库的比较
To draw a comparison to the world of standard relational databases,
graph databases like Neo4j are like OLTP (Online Transaction Processing) whereas graph processing systems like GraphX are like OLAP (Online Analytical Processing).
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 [email protected] 举报，一经查实，本站将立刻删除。
