How many datanodes do you use fir your job? On 4/3/12 8:11 PM, "Jane Wayne" <jane.wayne2...@gmail.com> wrote:
>i don't have the option of setting the map heap size to 2 GB since my >real environment is AWS EMR and the constraints are set. > >http://hadoop.apache.org/common/docs/r0.20.2/mapred_tutorial.html this >link is where i am currently reading on the meaning of io.sort.factor >and io.sort.mb. > >it seems io.sort.mb tunes the map tasks and io.sort.factor tunes the >shuffle/reduce task. am i correct to say then that io.sort.factor is >not relevant here (yet, anways)? since i don't really make it to the >reduce phase (except for only a very small data size). > >in that link above, here is the description for, io.sort.mb: The >cumulative size of the serialization and accounting buffers storing >records emitted from the map, in megabytes. there's a paragraph above >the table that is value is simply the threshold that triggers a sort >and spill to the disk. furthermore, it says, "If either buffer fills >completely while the spill is in progress, the map thread will block," >which is what i believe is happening in my case. > >this sentence concerns me, "Minimizing the number of spills to disk >can decrease map time, but a larger buffer also decreases the memory >available to the mapper." to minimize the number of spills, you need a >larger buffer; however, this statement seems to suggest to NOT >minimize the number of spills; a) you will not decrease map time, b) >you will not decrease the memory available to the mapper. so, in your >advice below, you say to increase, but i may actually want to decrease >the value for io.sort.mb. (if i understood the documentation >correctly, ????) > >it seems these three map tuning parameters, io.sort.mb, >io.sort.record.percent, and io.sort.spill.percent are a pain-point >trading off between speed and memory. to me, if you set them high, >more serialized data + metadata are stored in memory before a spill >(an I/O operation is performed). you also get less merges (less I/O >operation?), but the negatives are blocking map operations and more >memory requirements. if you set them low, there are more frequent >spills (more I/O operations), but less memory requirements. it just >seems like no matter what you do, you are stuck: you may stall the >mapper if the values are high because of the amount of time required >to spill an enormous amount of data; you may stall the mapper if the >values are low because of the amount of I/O operations required >(spill/merge). > >i must be understanding something wrong here because everywhere i >read, hadoop is supposed to be #1 at sorting. but here, in dealing >with the intermediary key-value pairs, in the process of sorting, >mappers can stall for any number of reasons. > >does anyone know any competitive dynamic hadoop clustering service >like AWS EMR? the reason why i ask is because AWS EMR does not use >HDFS (it uses S3), and therefore, data locality is not possible. also, >i have read the TCP protocol is not efficient for network transfers; >if the S3 node and task nodes are far, this distance will certainly >exacerbate the situation of slow speed. it seems there are a lot of >factors working against me. > >any help is appreciated. > >On Tue, Apr 3, 2012 at 7:48 AM, Bejoy Ks <bejoy.had...@gmail.com> wrote: >> >> Jane, >> From my first look, properties that can help you could be >> - Increase io sort factor to 100 >> - Increase io.sort.mb to 512Mb >> - increase map task heap size to 2GB. >> >> If the task still stalls, try providing lesser input for each mapper. >> >> Regards >> Bejoy KS >> >> On Tue, Apr 3, 2012 at 2:08 PM, Jane Wayne <jane.wayne2...@gmail.com> >>wrote: >> >> > i have a map reduce job that is generating a lot of intermediate >>key-value >> > pairs. for example, when i am 1/3 complete with my map phase, i may >>have >> > generated over 130,000,000 output records (which is about 9 >>gigabytes). to >> > get to the 1/3 complete mark is very fast (less than 10 minutes), but >>at >> > the 1/3 complete mark, it seems to stall. when i look at the counter >>logs, >> > i do not see any logging of spilling yet. however, on the web job UI, >>i see >> > that FILE_BYTES_WRITTEN and Spilled Records keeps increasing. >>needless to >> > say, i have to dig deeper to see what is going on. >> > >> > my question is, how do i fine tune my map reduce job with the above >> > properties? namely, the property of generating a lot of intermediate >> > key-value pairs? it seems the I/O operations are negatively impacting >>the >> > job speed. there are so many map- and reduce-side tuning properties >>(see >> > Tom White, Hadoop, 2nd edition, pp 181-182), i am a little unsure >>about >> > just how to approach the tuning parameters. since the slow down is >> > happening during the map-phase/task, i assume i should narrow down on >>the >> > map-side tuning properties. >> > >> > by the way, i am using the CPU-intensive c1.medium instances of >>amazon web >> > service's (AWS) elastic map reduce (EMR) on hadoop v0.20. a compute >>node >> > has 2 mappers, 1 reducers, and 384 MB JVM memory per task. this >>instance >> > type is documented to have moderate I/O performance. >> > >> > any help on fine tuning my particular map reduce job is appreciated. >> >
