I'm glad to hear it helped.
Thanks & Regards Brahma Reddy Battula ________________________________ From: sandeep das [[email protected]] Sent: Monday, November 09, 2015 11:54 AM To: [email protected] Subject: Re: Max Parallel task executors After increasing yarn.nodemanager.resource.memory-mb to 24 GB more number of parallel map tasks are being spawned. Its resolved now. Thanks a lot for your input. Regards, Sandeep On Mon, Nov 9, 2015 at 9:49 AM, sandeep das <[email protected]<mailto:[email protected]>> wrote: BTW Laxman according to the formula that you had provided it turns out that only 8 jobs per node will be initiated which is matching with what i'm seeing on my setup. min (yarn.nodemanager.resource.memory-mb / mapreduce.[map|reduce].memory.mb, yarn.nodemanager.resource.cpu-vcores / mapreduce.[map|reduce].cpu.vcores) yarn.nodemanager.resource.memory-mb: 16 GB mapreduce.map.memory.mb: 2 GB yarn.nodemanager.resource.cpu-vcores: 80 mapreduce.map.cpu.vcores: 1 So if apply the formula then min(16/2, 80/1) -> min(8,80) -> 8 Should i reduce memory per map operation or increase memory for resource manager? On Mon, Nov 9, 2015 at 9:43 AM, sandeep das <[email protected]<mailto:[email protected]>> wrote: Thanks Brahma and Laxman for your valuable input. Following are the statistics available on YARN RM GUI. Memory Used : 0 GB Memory Total : 64 GB (16*4 = 64 GB) VCores Used: 0 VCores Total: 320 (Earlier I had mentioned that I've configured 40 Vcores but recently I increased to 80 that's why its appearing 80*4 = 321) Note: These statistics were captured when there was no job running in background. Let me know whether it was sufficient to nail the issue. If more information is required please let me know. Regards, Sandeep On Fri, Nov 6, 2015 at 7:04 PM, Brahma Reddy Battula <[email protected]<mailto:[email protected]>> wrote: The formula for determining the number of concurrently running tasks per node is: min (yarn.nodemanager.resource.memory-mb / mapreduce.[map|reduce].memory.mb, yarn.nodemanager.resource.cpu-vcores / mapreduce.[map|reduce].cpu.vcores) . For you scenario : As you told yarn.nodemanager.resource.memory-mb is configured to 16 GB and yarn.nodemanager.resource.cpu-vcores configured to 40. and I am thinking mapreduce.map/reduce.memory.mb, mapreduce.map/reduce.cpu.vcores default values. min (16GB/1GB,40Core/1Core )=16 tasks for Node. Then total should be 16*4=64 (63+1AM).. I am thinking, Two Nodemanger's are unhealthy (OR) you might have configured mapreduce.map/reduce.memory.mb=2GB(or 5 core). As laxman pointed you can post RMUI or you can cross check like above. Hope this helps. Thanks & Regards Brahma Reddy Battula ________________________________ From: Laxman Ch [[email protected]<mailto:[email protected]>] Sent: Friday, November 06, 2015 6:31 PM To: [email protected]<mailto:[email protected]> Subject: Re: Max Parallel task executors Can you please copy paste the cluster metrics from RM dashboard. Its under http://rmhost:port/cluster/cluster In this page, check under Memory Total vs Memory Used and VCores Total vs VCores Used On 6 November 2015 at 18:21, sandeep das <[email protected]<mailto:[email protected]>> wrote: HI Laxman, Thanks for your response. I had already configured a very high value for yarn.nodemanager.resource.cpu-vcores e.g. 40 but still its not increasing more number of parallel tasks to execute but if this value is reduced then it runs less number of parallel tasks. As of now yarn.nodemanager.resource.memory-mb is configured to 16 GB and yarn.nodemanager.resource.cpu-vcores configured to 40. Still its not spawning more tasks than 31. Let me know if more information is required to debug it. I believe there is upper limit after which yarn stops spawning tasks. I may be wrong here. Regards, Sandeep On Fri, Nov 6, 2015 at 6:15 PM, Laxman Ch <[email protected]<mailto:[email protected]>> wrote: Hi Sandeep, Please configure the following items to the cores and memory per node you wanted to allocate for Yarn containers. Their defaults are 8 cores and 8GB. So that's the reason you were stuck at 31 (4nodes * 8cores - 1 AppMaster) http://hadoop.apache.org/docs/r2.6.0/hadoop-yarn/hadoop-yarn-common/yarn-default.xml yarn.nodemanager.resource.cpu-vcores yarn.nodemanager.resource.memory-mb On 6 November 2015 at 17:59, sandeep das <[email protected]<mailto:[email protected]>> wrote: May be to naive to ask but How do I check that? Sometimes there are almost 200 map tasks pending to run but at a time only 31 runs. On Fri, Nov 6, 2015 at 5:57 PM, Chris Mawata <[email protected]<mailto:[email protected]>> wrote: Also check that you have more than 31 blocks to process. On Nov 6, 2015 6:54 AM, "sandeep das" <[email protected]<mailto:[email protected]>> wrote: Hi Varun, I tried to increase this parameter but it did not increase number of parallel tasks but if It is decreased then YARN reduces number of parallel tasks. I'm bit puzzled why its not increasing more than 31 tasks even after its value is increased. Is there any other configuration as well which controls on how many maximum tasks can execute in parallel? Regards, Sandeep On Tue, Nov 3, 2015 at 7:29 PM, Varun Vasudev <[email protected]<mailto:[email protected]>> wrote: The number of parallel tasks that are run depends on the amount of memory and vcores on your machines and the amount of memory and vcores required by your mappers and reducers. The amount of memory can be set via yarn.nodemanager.resource.memory-mb(the default is 8G). The amount of vcores can be set via yarn.nodemanager.resource.cpu-vcores(the default is 8 vcores). -Varun From: sandeep das <[email protected]<mailto:[email protected]>> Reply-To: <[email protected]<mailto:[email protected]>> Date: Monday, November 2, 2015 at 3:56 PM To: <[email protected]<mailto:[email protected]>> Subject: Max Parallel task executors Hi Team, I've a cloudera cluster of 4 nodes. Whenever i submit a job my only 31 parallel tasks are executed whereas my machines have more CPU available but still YARN/AM does not create more task. Is there any configuration which I can change to start more MAP/REDUCER task in parallel? Each machine in my cluster has 24 CPUs. Regards, Sandeep -- Thanks, Laxman -- Thanks, Laxman
