The locality is how close the data is to the code that's processing it. PROCESS_LOCAL means data is in the same JVM as the code that's running, so it's really fast. NODE_LOCAL might mean that the data is in HDFS on the same node, or in another executor on the same node, so is a little slower because the data has to travel across an IPC connection. RACK_LOCAL is even slower -- data is on a different server so needs to be sent over the network.
Spark switches to lower locality levels when there's no unprocessed data on a node that has idle CPUs. In that situation you have two options: wait until the busy CPUs free up so you can start another task that uses data on that server, or start a new task on a farther away server that needs to bring data from that remote place. What Spark typically does is wait a bit in the hopes that a busy CPU frees up. Once that timeout expires, it starts moving the data from far away to the free CPU. The main tunable option is how far long the scheduler waits before starting to move data rather than code. Those are the spark.locality.* settings here: http://spark.apache.org/docs/latest/configuration.html If you want to prevent this from happening entirely, you can set the values to ridiculously high numbers. The documentation also mentions that "0" has special meaning, so you can try that as well. Good luck! Andrew On Thu, Jun 5, 2014 at 3:13 PM, Sung Hwan Chung <coded...@cs.stanford.edu> wrote: > I noticed that sometimes tasks would switch from PROCESS_LOCAL (I'd assume > that this means fully cached) to NODE_LOCAL or even RACK_LOCAL. > > When these happen things get extremely slow. > > Does this mean that the executor got terminated and restarted? > > Is there a way to prevent this from happening (barring the machine > actually going down, I'd rather stick with the same process)? >
