I doubt Titan would be able to give you traversal of billions of nodes in real-time either. In-memory traversal is typically much faster than Cassandra-based tree traversal, even including in-memory caching.
On Tue, Apr 8, 2014 at 1:23 PM, Nick Pentreath <nick.pentre...@gmail.com>wrote: > GraphX, like Spark, will not typically be "real-time" (where by "real-time" > here I assume you mean of the order of a few 10s-100s ms, up to a few > seconds). > > Spark can in some cases approach the upper boundary of this definition (a > second or two, possibly less) when data is cached in memory and the > computation is not "too heavy", while Spark Streaming may be able to get > closer to the mid-to-upper boundary of this under similar conditions, > especially if aggregating over relatively small windows. > > However, for this use case (while I haven't used GraphX yet) I would say > something like Titan (https://github.com/thinkaurelius/titan/wiki) or a > similar OLTP graph DB may be what you're after. But this depends on what > kind of graph traversal you need. > > > > > On Tue, Apr 8, 2014 at 10:02 PM, love2dishtech <love2disht...@gmail.com > >wrote: > > > Hi, > > > > Is Graphx on top of Apache Spark, is able to process the large scale > > distributed graph traversal and compute, in real time. What is the query > > execution engine distributing the query on top of graphx and apache > spark. > > My typical use case is a large scale distributed graph traversal in real > > time, with billions of nodes. > > > > Thanks, > > Love. > > > > > > > > -- > > View this message in context: > > > http://apache-spark-developers-list.1001551.n3.nabble.com/Apache-Spark-and-Graphx-for-Real-Time-Analytics-tp6261.html > > Sent from the Apache Spark Developers List mailing list archive at > > Nabble.com. > > > -- -- Evan Chan Staff Engineer e...@ooyala.com | <http://www.ooyala.com/> <http://www.facebook.com/ooyala><http://www.linkedin.com/company/ooyala><http://www.twitter.com/ooyala>
