Thanks for the explanation. Try creating a custom RDD whose getPartitions returns an array of custom partition objects of size n (= number of nodes). In a custom partition object, you can have the file path and ip/hostname where the partition needs to be computed. Then, have getPreferredLocations return the ip/hostname from the partition object and in compute function, assert that you are in right ip/hostname (or fail) and read the content of the file.
Not a 100% sure it will work though. On Tue, Jul 19, 2016, 2:54 AM Josh Asplund <joshaspl...@gmail.com> wrote: > The spark workers are running side-by-side with scientific simulation > code. The code writes output to local SSDs to keep latency low. Due to the > volume of data being moved (10's of terabytes +), it isn't really feasible > to copy the data to a global filesystem. Executing a function on each node > would allow us to read the data in situ without a copy. > > I understand that manually assigning tasks to nodes reduces fault > tolerance, but the simulation codes already explicitly assign tasks, so a > failure of any one node is already a full-job failure. > > On Mon, Jul 18, 2016 at 3:43 PM Aniket Bhatnagar < > aniket.bhatna...@gmail.com> wrote: > >> You can't assume that the number to nodes will be constant as some may >> fail, hence you can't guarantee that a function will execute at most once >> or atleast once on a node. Can you explain your use case in a bit more >> detail? >> >> On Mon, Jul 18, 2016, 10:57 PM joshuata <joshaspl...@gmail.com> wrote: >> >>> I am working on a spark application that requires the ability to run a >>> function on each node in the cluster. This is used to read data from a >>> directory that is not globally accessible to the cluster. I have tried >>> creating an RDD with n elements and n partitions so that it is evenly >>> distributed among the n nodes, and then mapping a function over the RDD. >>> However, the runtime makes no guarantees that each partition will be >>> stored >>> on a separate node. This means that the code will run multiple times on >>> the >>> same node while never running on another. >>> >>> I have looked through the documentation and source code for both RDDs and >>> the scheduler, but I haven't found anything that will do what I need. >>> Does >>> anybody know of a solution I could use? >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/Execute-function-once-on-each-node-tp27351.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >>>
