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. > >
