I've heard "Spark is not just MapReduce" mentioned during Spark talks, but it seems like every method that Spark has is really doing something like (Map -> Reduce) or (Map -> Map -> Map -> Reduce) etc behind the scenes, with the performance benefit of keeping RDDs in memory between stages.
Am I wrong about that? Is Spark doing anything more efficiently than a series of Maps followed by a Reduce in memory? What methods does Spark have that can't easily be mapped (with somewhat similar efficiency) to Map and Reduce in memory? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/What-does-Spark-is-not-just-MapReduce-mean-Isn-t-every-Spark-job-a-form-of-MapReduce-tp23518.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
