It really depends on the type of the computation. For example, if vertices and edges are associated with properties and you want to operate on (vertex-edge-vertex) triplets or use the Pregel API, GraphX is the way to go. -Xiangrui
On Sat, Oct 4, 2014 at 9:39 PM, ll <duy.huynh....@gmail.com> wrote: > hi. i am working on an algorithm that has a graph data structure. > > it looks like there 2 ways to implement this with spark > > option 1: use graphx which already provide Vetices and Edges to build out > the graph pretty nicely. > > option 2: use mllib sparse vector / matrix to build out the graph. the > reason i consider mllib because it looks like it's more stable than graphx. > > what are the pros and cons of these 2 options? > > when would you use one vs the other? > > any advice is much appreciated! > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/mllib-sparse-vector-matrix-vs-graphx-graph-tp15759.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
