Are these Cloudera specific acronyms? Not sure how Cloudera configures
Spark differently, but obviously the number of nodes is too small,
considering each app only uses a small number of cores and RAM. So you
may consider increase the number of nodes. When all these apps jam on
a few nodes, the cluster manager/scheduler and/or the network becomes
overwhelmed...
On 10/26/22 8:09 AM, Sean Owen wrote:
Resource contention. Now all the CPU and I/O is competing and probably
slows down
On Wed, Oct 26, 2022, 5:37 AM eab...@163.com <eab...@163.com> wrote:
Hi All,
I have a CDH5.16.2 hadoop cluster with 1+3 nodes(64C/128G, 1NN/RM
+ 3DN/NM), and yarn with 192C/240G. I used the following test
scenario:
1.spark app resource with 2G driver memory/2C driver vcore/1
executor nums/2G executor memory/2C executor vcore.
2.one spark app will use 5G4C on yarn.
3.first, I only run one spark app takes 40s.
4.Then, I run 30 the same spark app at once, and each spark app
takes 80s on average.
So, I want to know why the run time gap is so big, and how to
optimize?
Thanks