On Tue, Sep 6, 2011 at 6:57 PM, Chris Lu <[email protected]> wrote: > Thanks. Very helpful to me! > > I tried to change the setting of "mapred.map.tasks". However, the number > map task is still just one on one of the 20 machines. > > ./elastic-mapreduce --create --alive \ > --num-instances 20 --name "LDA" \ > --bootstrap-action s3://elasticmapreduce/**bootstrap-actions/configure-* > *hadoop \ > --bootstrap-name "Configuring number of map tasks per job" \ > --args "-m,mapred.map.tasks=40" > > Anyone knows how to configure the number of mappers? > Again, the input size is only 46M. > > Chris > > > On 09/06/2011 12:09 PM, Ted Dunning wrote: > >> Well, I think that using small instances is a disaster in general. The >> performance that you get from them can vary easily by an order of >> magnitude. >> My own preference for real work is either m2xl or cc14xl. The latter >> machines give you nearly bare metal performance and no noisy neighbors. >> The >> m2xl is typically very much underpriced on the spot market. >> >> Sean is right about your job being misconfigured. The Hadoop overhead is >> considerable and you have only given it two threads to overcome that >> overhead. >> >> On Tue, Sep 6, 2011 at 6:12 PM, Sean Owen<[email protected]> wrote: >> >> That's your biggest issue, certainly. Only 2 mappers are running, even >>> though you have 20 machines available. Hadoop determines the number of >>> mappers based on input size, and your input isn't so big that it thinks >>> you >>> need 20 workers. It's launching 33 reducers, so your cluster is put to >>> use >>> there. But it's no wonder you're not seeing anything like 20x speedup in >>> the >>> mapper. >>> >>> You can of course force it to use more mappers, and that's probably a >>> good >>> idea here. -Dmapred.map.tasks=20 perhaps. More mappers means more >>> overhead >>> of spinning up mappers to process less data, and Hadoop's guess indicates >>> that it thinks it's not efficient to use 20 workers. If you know that >>> those >>> other 18 are otherwise idle, my guess is you'd benefit from just making >>> it >>> use 20. >>> >>
Sean, I too have always been confused about how Hadoop decides to set the number of mappers so you could help my understanding here... Is -Dmapred.map.tasks just a hint to the framework for the number of mappers (just like using the combiner is a hint) or does it actually set the number of workers to that number (provided our input is large enough)? The reason I ask is because on http://wiki.apache.org/hadoop/HowManyMapsAndReduces, it is mentioned that the framework uses the HDFS block size to decide on the number of mapper workers to be invoked. Should we be setting that parameter instead? > >>> If this were a general large cluster where many people are taking >>> advantage >>> of the workers, then I'd trust Hadoop's guesses until you are sure you >>> want >>> to do otherwise. >>> >>> On Tue, Sep 6, 2011 at 7:02 PM, Chris Lu<[email protected]> wrote: >>> >>> Thanks for all the suggestions! >>>> >>>> All the inputs are the same. It takes 85 hours for 4 iterations on 20 >>>> Amazon small machines. On my local single node, it got to iteration 19 >>>> >>> for >>> >>>> also 85 hours. >>>> >>>> Here is a section of the Amazon log output. >>>> It covers the start of iteration 1, and between iteration 4 and >>>> iteration >>>> 5. >>>> >>>> The number of map tasks is set to 2. Should it be larger or related to >>>> number of CPU cores? >>>> >>>> >>>> >
