Github user tgravescs commented on the issue:
https://github.com/apache/spark/pull/18388
200k+ connections seems to be your problem then. Is this all a single
application? You say 6000 nodes with 64 executors on each host, how many cores
per executor? Or do you mean basically each host can run max 64 tasks in
parallel. (6000*64) = 384000 which would be your 200K. I'd be surprised if
every reducer is hitting the all nodes at the same time. We are randomizing the
blocks to fetch in hope they don't hit all the same one at once.
have you tried using spark.reducer.maxReqsInFlight?
Rejecting connection could also slow things down or even worse make them
fail. You have a wait between retries and if you hit the max retries and fail
tasks that is much worse then flow control.
Reconnection does have a cost but either way you are going to wait some
between retries, you don't actually want to retry to quickly or you will just
have same issue. What problem are you seeing with the close?
I agree that I think both are good to have but personally think the reject
connections should be the last thing you want to do.
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