Actually I didn't have any of the GC tuning in the beginning and then
adding them also didn't made any difference. As mentioned earlier I tried
low number executors of higher configuration and vice versa. Nothing helps.
About the code its simple logistic regression nothing with explicit
broadcast o
I would remove the all GC tuning and add it later once you found the underlying
root cause. Usually more GC means you need to provide more memory, because
something has changed (your application, spark Version etc.)
We don’t have your full code to give exact advise, but you may want to rethink
Hi,
We were running Logistic Regression in Spark 2.2.X and then we tried to see
how does it do in Spark 2.3.X. Now we are facing an issue while running a
Logistic Regression Model in Spark 2.3.X on top of Yarn(GCP-Dataproc). In
the TreeAggregate method it takes a huge time due to very High GC Acti
