Not an expert on capacity scheduler but the above two are not queue-level configurations, so I think the changes would not reflect on running refreshqueues. You would need to restart the RM for the new values to take effect.
Thanks, Hari On Thu, Jan 10, 2019 at 7:41 PM Or Raz <r...@post.bgu.ac.il> wrote: > I have googled more about it, and it seems like two parameters should > define the "bin packing problem". > According to > https://hadoop.apache.org/docs/r2.9.1/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html#Other_Properties > yarn.scheduler.capacity.per-node-heartbeat.multiple-assignments-enabled is > by default set to true and with parameter > yarn.scheduler.capacity.per-node-heartbeat.maximum-container-assignments r > set to -1 it can assign all the containers the Node manager "said" it is > capable of (which could somehow explain the bin packing problem for the > first Nodemanager who answer with a Heartbeat message). > Following Apache's instructions, I have inserted to my > *capacity-scheduler.xml* in hadoop/etc/hadoop folder > > <property> > > <name>yarn.scheduler.capacity.per-node-heartbeat.multiple-assignments-enabled</name> > <value>true</value> > <description> > Whether to allow multiple container assignments in one NodeManager > heartbeat. Defaults to true. > </description> > </property> > <property> > > <name>yarn.scheduler.capacity.per-node-heartbeat.maximum-container-assignments</name> > <value>2</value> > <description> > If multiple-assignments-enabled is true, the maximum amount of > containers that can be assigned in one NodeManager heartbeat. Defaults to > -1, which sets no limit. > </description> > </property> > I have checked the configuration file, and I am using the capacity > scheduler (I have enabled > yarn.scheduler.capacity.per-node-heartbeat.multiple-assignments-enabled again > just to be sure). > Furthermore, after I have run "yarn rmadmin -refreshQueues" I haven't seen > any change in the Mappers allocation nor Reducers. > hadoop2@master:~$ yarn rmadmin -refreshQueues > 19/01/10 16:06:33 INFO client.RMProxy: Connecting to ResourceManager at > master/172.31.24.83:8033 > > What am I missing over here? > > Or > > > בתאריך יום ד׳, 9 בינו׳ 2019 ב-23:57 מאת Or Raz <r...@post.bgu.ac.il > >: > >> Thanks for the tips! >> Because I haven't set any scheduler (on purpose) for YARN then, I am >> using the default one (Capacity). >> I have looked in yarn-site.xml and in the configuration tab (using >> JobHistory UI), and both of the parameters that you have mentioned weren't >> there (so they haven't been set). >> You said that I should look at "locality settings" can you be more >> specific on what and where to look? >> Also, it is worth mentioning that I am using three computers and the >> replication factor (of HDFS) is three too. Thus, every data (even input) >> would be on every computer, and the memory of each computer is the same >> (two t2.xlarge and one m4.xlarge) while I am >> using DefaultResourceCalculator. >> >> Or >> >> בתאריך יום ד׳, 9 בינו׳ 2019 ב-23:28 מאת Aaron Eng <a...@mapr.com >> >: >> >>> The settings are very relevant to having an equal number of containers >>> running on each node if you have an idle cluster and want to distribute >>> containers for a single job. An application master submits requests for >>> container allocations to the ResourceManager. The MRAppMaster will request >>> all the map containers at once, the FairScheduler will find NodeManagers >>> with capacity to fulfill the container requests. If assign multiple is >>> enabled then you generally won't get an even number of containers assigned >>> to each node +/- 1 container. Before you say it's not relevant, you should >>> check if your environment uses the FairScheduler and whether multiple >>> assignment is enabled. If so, that's likely why there isn't an even >>> assignment +/- 1 container. If not using FairScheduler and/or multiple >>> assign, then you should look at locality settings, which can cause >>> containers to be preferentially run on a subset of nodes, resulting in an >>> uneven container assignment per node. >>> >>> On Wed, Jan 9, 2019 at 2:19 PM Or Raz <r...@post.bgu.ac.il> wrote: >>> >>>> As far as I know, the scheduler in YARN is only scheduling the jobs and >>>> not the containers inside each job. Therefore, I don't believe it is >>>> relevant. >>>> Also, I haven't used or set those two parameters, and I haven't picked >>>> nor set any particular schedule for my research (Fair, FIFO or Capacity). >>>> Please correct if I am wrong. >>>> P.S. currently I have no interest in a situation when I run a few jobs >>>> concurrently, my case is much simpler with one job that I would like that >>>> allocation of containers will be more balanced... >>>> Or >>>> >>>> >>>> בתאריך יום ד׳, 9 בינו׳ 2019 ב-19:11 מאת Aaron Eng <a...@mapr.com >>>> >: >>>> >>>>> Have you checked the yarn.scheduler.fair.assignmultiple >>>>> and yarn.scheduler.fair.max.assign parameters for the ResourceManager >>>>> configuration? >>>>> >>>>> On Wed, Jan 9, 2019 at 9:49 AM Or Raz <r...@post.bgu.ac.il> wrote: >>>>> >>>>>> How can I change/suggest a different allocation of containers to >>>>>> tasks in Hadoop? Regarding a native Hadoop (2.9.1) cluster on AWS. >>>>>> >>>>>> I am running a native Hadoop cluster (2.9.1) on AWS (with EC2, not >>>>>> EMR) and I want the scheduling/allocating of the containers >>>>>> (Mappers/Reducers) would be more balanced than it is currently. It seems >>>>>> like RM is assigning the Mappers in a Bin Packing way (where the data >>>>>> resides) and for the reducers, it looks more balanced. My setup includes >>>>>> three Machines with replication rate three (all the data is on every >>>>>> machine), and I run my jobs with >>>>>> mapreduce.job.reduce.slowstart.completedmaps=0 to start shuffle as fast >>>>>> as >>>>>> possible (It is vital for me that all the containers are working in >>>>>> concurrency, it is a must condition). Also, according to the EC2 >>>>>> instances >>>>>> I have chosen and my settings of the YARN cluster, I can run at most 93 >>>>>> containers (31 each). >>>>>> >>>>>> For example, if I want to have nine reducers then (93-9-1=83), 83 >>>>>> containers could be left for the mappers, and one is for the AM. I have >>>>>> played with the size of split input >>>>>> (mapreduce.input.fileinputformat.split.minsize, >>>>>> mapreduce.input.fileinputformat.split.maxsize) to find the right balance >>>>>> where all of the machines have the same "work" for the map phase. But it >>>>>> seems like the first 31 mappers would be allocated in one computer, the >>>>>> next 31 to the second one and the last 31 in the last machine. Thus, I >>>>>> can >>>>>> try to use 87 mappers where 31 of them in Machine #1, another 31 in >>>>>> Machine >>>>>> #2 and another 25 in Machine #3 and the rest is left for the reducers and >>>>>> as Machine #1 and Machine #2 are fully occupied then the reducers would >>>>>> have to be placed in Machine #3. This way I get an almost balanced >>>>>> allocation of mappers at the expense of unbalanced reducers allocation. >>>>>> And >>>>>> this is not what I want... >>>>>> >>>>>> # of mappers = size_input / split size [Bytes] >>>>>> >>>>>> split size >>>>>> =max(mapreduce.input.fileinputformat.split.minsize,min(mapreduce.input.fileinputformat.split.maxsize, >>>>>> dfs.blocksize)) >>>>>> >>>>>
