Welcome to Sudheer's blog...

Example: spark-shell --deploy-mode cluster --master yarn --executor-cores 4 --num-executors 6 --executor-memory 12g Like how we tune spark-submit parameters, same tuning parameters are applicable for spark-shell as-well. Except that deploy-mode can not be 'cluster', of-course right. Also make sure spark.dynamicAllocation.enabled is set to  true. With these settings, you can see that Yarn Executors are allocated on demand and removed when no more required.

Often while performing statistical aggregations, we get scenario to perform aggregations on next n number of seconds from current row for each of the rows. Spark Window api provides a nice rangeBetween functionality which facilitates performing above. For Example: // Sample data with timestamp // Sample data with timestamp val customers = sc.parallelize(List(("Alice", "2016-05-01 00:00:00", 10,4), ("Alice", "2016-05-01 00:00:01", 20,2), ("Alice", "2016-05-01 00:00:02", 30,4), ("Alice", "2016-05-01 00:00:02", 40,6), ("Alice", "2016-05-01 00:00:03", 50,1), ("Alice", "2016-05-01 00:00:03", 60,4), ("Alice", "2016-05-01 00:00:04", 70,2), ("Alice", "2016-05-01 00:00:05", 80,4), ("Bob", "2016-05-01 00:00:03", 25,6), ("Bob", "2016-05-01 00:00:04", 29,7), ("Bob", "2016-05-