Hi Virajith, The default FIFO scheduler just isn't optimized for locality for small jobs. You should be able to get substantially more locality even with 1 replica if you use the fair scheduler, although the version of the scheduler in 0.20 doesn't contain the locality optimization. Try the Cloudera distribution to get a 0.20-compatible Hadoop that does contain it.
I also think your value of 10% inferred on completion time might be a little off, because you have quite a few more data blocks than nodes so it should be easy to make the first few waves of tasks data-local. Try a version of Hadoop that correctly measures this counter. Matei On Jul 12, 2011, at 1:27 PM, Virajith Jalaparti wrote: > I agree that the scheduler has lesser leeway when the replication factor is > 1. However, I would still expect the number of data-local tasks to be more > than 10% even when the replication factor is 1. Presumably, the scheduler > would have greater number of opportunities to schedule data-local tasks as > compared to just 10%. (Please note that I am inferring that a map was > non-local based on the observed completion time. I don't know why but the > logs of my jobs don't show the DATA_LOCAL_MAPS counter information.) > > I will try using higher replication factors and see how much improvement I > can get. > > Thanks, > Virajith > > On Tue, Jul 12, 2011 at 6:15 PM, Arun C Murthy <a...@hortonworks.com> wrote: > As Aaron mentioned the scheduler has very little leeway when you have a > single replica. > > OTOH, schedulers equate rack-locality to node-locality - this makes sense > sense for a large-scale system since intra-rack b/w is good enough for most > installs of Hadoop. > > Arun > > On Jul 12, 2011, at 7:36 AM, Virajith Jalaparti wrote: > >> I am using a replication factor of 1 since I dont to incur the overhead of >> replication and I am not much worried about reliability. >> >> I am just using the default Hadoop scheduler (FIFO, I think!). In case of a >> single rack, rack-locality doesn't really have any meaning. Obviously >> everything will run in the same rack. I am concerned about data-local maps. >> I assumed that Hadoop would do a much better job at ensuring data-local maps >> but it doesnt seem to be the case here. >> >> -Virajith >> >> On Tue, Jul 12, 2011 at 3:30 PM, Arun C Murthy <a...@hortonworks.com> wrote: >> Why are you running with replication factor of 1? >> >> Also, it depends on the scheduler you are using. The CapacityScheduler in >> 0.20.203 (not 0.20.2) has much better locality for jobs, similarly with >> FairScheduler. >> >> IAC, running on a single rack with replication of 1 implies rack-locality >> for all tasks which, in most cases, is good enough. >> >> Arun >> >> On Jul 12, 2011, at 5:45 AM, Virajith Jalaparti wrote: >> >> > Hi, >> > >> > I was trying to run the Sort example in Hadoop-0.20.2 over 200GB of input >> > data using a 20 node cluster of nodes. HDFS is configured to use 128MB >> > block size (so 1600maps are created) and a replication factor of 1 is >> > being used. All the 20 nodes are also hdfs datanodes. I was using a >> > bandwidth value of 50Mbps between each of the nodes (this was configured >> > using linux "tc"). I see that around 90% of the map tasks are reading data >> > over the network i.e. most of the map tasks are not being scheduled at the >> > nodes where the data to be processed by them is located. >> > My understanding was that Hadoop tries to schedule as many data-local maps >> > as possible. But in this situation, this does not seem to happen. Any >> > reason why this is happening? and is there a way to actually configure >> > hadoop to ensure the maximum possible node locality? >> > Any help regarding this is very much appreciated. >> > >> > Thanks, >> > Virajith >> >> > >
