Thanks Gary. Understood the behavior. I am leaning towards running 7 TM on each machine(8 core), I have 4 nodes, that will end up 28 taskmanagers and 1 job manager. I was wondering if this can bring additional burden on jobmanager? Is it recommended?
Thanks, Jins George On 2/14/19 8:49 AM, Gary Yao wrote: Hi Jins George, This has been asked before [1]. The bottom line is that you currently cannot pre-allocate TMs and distribute your tasks evenly. You might be able to achieve a better distribution across hosts by configuring fewer slots in your TMs. Best, Gary [1] http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Flink-1-5-job-distribution-over-cluster-nodes-td21588.html On Wed, Feb 13, 2019 at 6:20 AM Tzu-Li (Gordon) Tai <tzuli...@apache.org<mailto:tzuli...@apache.org>> wrote: Hi, I'm forwarding this question to Gary (CC'ed), who most likely would have an answer for your question here. Cheers, Gordon On Wed, Feb 13, 2019 at 8:33 AM Jins George <jins.geo...@aeris.net<mailto:jins.geo...@aeris.net>> wrote: Hello community, I am trying to upgrade a Flink Yarn session cluster running BEAM pipelines from version 1.2.0 to 1.6.3. Here is my session start command: yarn-session.sh -d -n 4 -jm 1024 -tm 3072 -s 7 Because of the dynamic resource allocation, no taskmanager gets created initially. Now once I submit a job with parallelism 5, I see that 1 task-manager gets created and all 5 parallel instances are scheduled on the same taskmanager( because I have 7 slots). This can create hot spot as only one physical node ( out of 4 in my case) is utilized for processing. I noticed the legacy mode, which would provision all task managers at cluster creation, but since legacy mode is expected to go away soon, I didn't want to try that route. Is there a way I can configure the multiple jobs or parallel instances of same job spread across all the available Yarn nodes and continue using the 'new' mode ? Thanks, Jins George
