How does partitions map to tasks in Spark?

Question

If I partition an RDD into say 60 and I have a total of 20 cores spread across 20 machines, i.e. 20 instances of single core machines, then the number of tasks is 60 (equal to the number of partitions). Why is this beneficial over having a single partition per core and having 20 tasks?

Additionally, I have run an experiment where I have set the number of partitions to 2, checking the UI shows 2 tasks running at any one time; however, what has surprised me is that it switches instances on completion of tasks, e.g. node1 and node2 do the first 2 tasks, then node6 and node8 do the next set of 2 tasks etc. I thought by setting the number of partitions to less than the cores (and instances) in a cluster then the program would just use the minimum number of instances required. Can anyone explain this behaviour?

Answer 1

For the first question: you might want to have more granular tasks than strictly necessary in order to load less into memory at the same time. Also, it can help with error tolerance, as less work needs to be redone in case of failure. It is nevertheless a parameter. In general the answer depends on the kind of workload (IO bound, memory bound, CPU bound).

As for the second one, I believe version 1.3 has some code to dynamically request resources. I'm unsure in which version the break is, but older versions just request the exact resources you configure your driver with. As for how comes a partition moves from one node to another, well, AFAIK it will pick the data for a task from the node that has a local copy of that data on HDFS. Since hdfs has multiple copies (3 by default) of each block of data, there are multiple options to run any given piece).