Julien Cuquemelle commented on SPARK-22683:

Thanks a lot for your feedbacks, some clarification: 
 - the default remains 1, it means resource is not an issue and you want to 
minimize latency.
 - if you want to be resource-aware, use 2 which brings the easiest resource 
saving setting (which seems to be already very interesting)
 - if you want to maximize resource saving you need to measure resource 
consumption with higher values of the parameter.

regarding the multi-job use case, I do agree that this will not be optimal (but 
it will not be less optimal than today's state), and that we need a per-job 
configuration. We have a use-case where we had to split an application with 2 
jobs into 2 applications, because the optimal tuning of the executors was very 
different. But if that happens, the question of resource saving with the 
dynamic allocation still remains for each job, so this new parameter will still 
be useful.

Regarding the possibility to set it programmatically, right now the number of 
tasks per executors is computed during the starting of the AllocationManager, 
which happens during the SparkContext initialization, so it is not possible to 
update it. But it does not seem difficult to make it mutable so that the number 
of needed executors computation takes it into account: 
 * The maximum number of executors we would need under the current load to 
satisfy all running
 * and pending tasks, rounded up.
private def maxNumExecutorsNeeded(): Int = {
  val numRunningOrPendingTasks = listener.totalPendingTasks + 
  (numRunningOrPendingTasks + tasksPerExecutor - 1) / tasksPerExecutor

> DynamicAllocation wastes resources by allocating containers that will barely 
> be used
> ------------------------------------------------------------------------------------
>                 Key: SPARK-22683
>                 URL: https://issues.apache.org/jira/browse/SPARK-22683
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 2.1.0, 2.2.0
>            Reporter: Julien Cuquemelle
>            Priority: Major
>              Labels: pull-request-available
> While migrating a series of jobs from MR to Spark using dynamicAllocation, 
> I've noticed almost a doubling (+114% exactly) of resource consumption of 
> Spark w.r.t MR, for a wall clock time gain of 43%
> About the context: 
> - resource usage stands for vcore-hours allocation for the whole job, as seen 
> by YARN
> - I'm talking about a series of jobs because we provide our users with a way 
> to define experiments (via UI / DSL) that automatically get translated to 
> Spark / MR jobs and submitted on the cluster
> - we submit around 500 of such jobs each day
> - these jobs are usually one shot, and the amount of processing can vary a 
> lot between jobs, and as such finding an efficient number of executors for 
> each job is difficult to get right, which is the reason I took the path of 
> dynamic allocation.  
> - Some of the tests have been scheduled on an idle queue, some on a full 
> queue.
> - experiments have been conducted with spark.executor-cores = 5 and 10, only 
> results for 5 cores have been reported because efficiency was overall better 
> than with 10 cores
> - the figures I give are averaged over a representative sample of those jobs 
> (about 600 jobs) ranging from tens to thousands splits in the data 
> partitioning and between 400 to 9000 seconds of wall clock time.
> - executor idle timeout is set to 30s;
> Definition: 
> - let's say an executor has spark.executor.cores / spark.task.cpus taskSlots, 
> which represent the max number of tasks an executor will process in parallel.
> - the current behaviour of the dynamic allocation is to allocate enough 
> containers to have one taskSlot per task, which minimizes latency, but wastes 
> resources when tasks are small regarding executor allocation and idling 
> overhead. 
> The results using the proposal (described below) over the job sample (600 
> jobs):
> - by using 2 tasks per taskSlot, we get a 5% (against -114%) reduction in 
> resource usage, for a 37% (against 43%) reduction in wall clock time for 
> Spark w.r.t MR
> - by trying to minimize the average resource consumption, I ended up with 6 
> tasks per core, with a 30% resource usage reduction, for a similar wall clock 
> time w.r.t. MR
> What did I try to solve the issue with existing parameters (summing up a few 
> points mentioned in the comments) ?
> - change dynamicAllocation.maxExecutors: this would need to be adapted for 
> each job (tens to thousands splits can occur), and essentially remove the 
> interest of using the dynamic allocation.
> - use dynamicAllocation.backlogTimeout: 
>     - setting this parameter right to avoid creating unused executors is very 
> dependant on wall clock time. One basically needs to solve the exponential 
> ramp up for the target time. So this is not an option for my use case where I 
> don't want a per-job tuning. 
>     - I've still done a series of experiments, details in the comments. 
> Result is that after manual tuning, the best I could get was a similar 
> resource consumption at the expense of 20% more wall clock time, or a similar 
> wall clock time at the expense of 60% more resource consumption than what I 
> got using my proposal @ 6 tasks per slot (this value being optimized over a 
> much larger range of jobs as already stated)
>     - as mentioned in another comment, tampering with the exponential ramp up 
> might yield task imbalance and such old executors could become contention 
> points for other exes trying to remotely access blocks in the old exes (not 
> witnessed in the jobs I'm talking about, but we did see this behavior in 
> other jobs)
> Proposal: 
> Simply add a tasksPerExecutorSlot parameter, which makes it possible to 
> specify how many tasks a single taskSlot should ideally execute to mitigate 
> the overhead of executor allocation.
> PR: https://github.com/apache/spark/pull/19881

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