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From Joris Billen <>
Subject Why are in 1 stage most of my executors idle: are tasks within a stage dependent of each other?
Date Fri, 10 Sep 2021 14:20:58 GMT
Dear community,

I have a job that runs quite well for most stages: resource are consumed quite optimal (not
much memoy/vcoresleft idle). My cluster is managed and works well.
I end up with 27 executors and have 2 cores for each, so can run 54 tasks. For many stages
I see I have a high number of tasks running in parallel. For the longest stage (80k tasks)
however I see in the beginning for first 20k tasks that I still have many tasks in parallel
so it advances fastly), but after a while   only 7 tasks run concurrently, all on the same
3 executors residing on the same node. So that means that my other nodes (8 of them and over
45 healthy executors) are idle for over 3 hours. 
I notice in the logs that all tasks are run at "NODE_LOCAL"

I wonder what is causing this and if I can do something to make the idle executors also do
work. 2 options:
1)It is just the way it is: at some point in this stage, there are dependencies of the further
tasks. So the task manager can not submit more tasks at once?
I thought that especially the stages have dependencies on each other-thats why often (not
always) they have to wait on the previous one for the next to start. But I cant find anywhere
if also the tasks within a stage can be dependent on each other? I thought the number of tasks
was the number of partitions and that these by definition could be executed in parallel. I
guess probably they are dependent, as when I look at the DAG of this stage, it is very complex.
2)I am playing with spark.local.wait to see if this can help. Maybe somehow he likes to run
everything as close to the data as possible . HEnce NODE_LOCAL for all tasks. Maybe if I decrease
the time spark.local.wait from default 3s to lower (1s), then he will also start shuffling
more data and give more tasks to the idle executors.

Anyone any idea?
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