The first line is distributing your fileList variable in the cluster as a RDD, partitioned using the default partitioner settings (e.g. Number of cores in your cluster).
Each of your workers would one or more slices of data (depending on how many cores each executor has) and the abstraction is called partition. What is your use case? If you want to load the files and continue processing in parallel, then a simple .map should work. If you want to execute arbitrary code based on the list of files that each executor received, then you need to use .foreach that will get executed for each of the entries, on the worker. -adrian From: Vinoth Sankar Date: Wednesday, October 28, 2015 at 2:49 PM To: "user@spark.apache.org<mailto:user@spark.apache.org>" Subject: How do I parallize Spark Jobs at Executor Level. Hi, I'm reading and filtering large no of files using Spark. It's getting parallized at Spark Driver level only. How do i make it parallelize to Executor(Worker) Level. Refer the following sample. Is there any way to paralleling iterate the localIterator ? Note : I use Java 1.7 version JavaRDD<String> files = javaSparkContext.parallelize(fileList) Iterator<String> localIterator = files.toLocalIterator(); Regards Vinoth Sankar
