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From java8964 <java8...@hotmail.com>
Subject RE: hadoop/yarn and task parallelization on non-hdfs filesystems
Date Fri, 15 Aug 2014 20:11:57 GMT
Interesting to know that.
I also want to know what underline logic holding the force to only generate 25-35 parallelized
containers, instead of up to 1300.
Another suggestion I can give is following:
1) In your driver, generate a text file, including all your 1300 bz2 file names with absolute
path.2) In your MR job, use the NLineInputFormat, with default setting, each line content
will trigger one mapper task.3) In your mapper, key/value pair will be offset byte loc/line
content, just start to process the file, as it should be available from the mount path in
the local data nodes.4) I assume that you are using Yarn. In this case, at least 1300 container
requests will be issued to the cluster. You generate 1300 parallelized request, now it is
up to the cluster to decide how many containers can be parallel run.
Yong

> Date: Fri, 15 Aug 2014 12:30:09 -0600
> Subject: Re: hadoop/yarn and task parallelization on non-hdfs filesystems
> From: iphcalvin@gmail.com
> To: user@hadoop.apache.org
> 
> Thanks for the responses!
> 
> To clarify, I'm not using any special FileSystem implementation. An
> example input parameter to a MapReduce job would be something like
> "-input file:///scratch/data". Thus I think (any clarification would
> be helpful) Hadoop is then utilizing LocalFileSystem
> (org.apache.hadoop.fs.LocalFileSystem).
> 
> The input data is large enough and splittable (1300 .bz2 files, 274MB
> each, 350GB total). Thus even if it the input data weren't splittable,
> Hadoop should be able to parallelize up to 1300 map tasks if capacity
> is available; in my case, I find that the Hadoop cluster is not fully
> utilized (i.e., ~25-35 containers running when it can scale up to ~80
> containers) when not using HDFS, while achieving maximum use when
> using HDFS.
> 
> I'm wondering if Hadoop is "holding back" or throttling the I/O if
> LocalFileSystem is being used, and what changes I can make to have the
> Hadoop tasks scale.
> 
> In the meantime, I'll take a look at the API calls that Harsh mentioned.
> 
> 
> On Fri, Aug 15, 2014 at 10:15 AM, Harsh J <harsh@cloudera.com> wrote:
> > The split configurations in FIF mentioned earlier would work for local files
> > as well. They aren't deemed unsplitable, just considered as one single
> > block.
> >
> > If the FS in use has its advantages it's better to implement a proper
> > interface to it making use of them, than to rely on the LFS by mounting it.
> > This is what we do with HDFS.
> >
> > On Aug 15, 2014 8:52 PM, "java8964" <java8964@hotmail.com> wrote:
> >>
> >> I believe that Calvin mentioned before that this parallel file system
> >> mounted into local file system.
> >>
> >> In this case, will Hadoop just use java.io.File as local File system to
> >> treat them as local file and not split the file?
> >>
> >> Just want to know the logic in hadoop handling the local file.
> >>
> >> One suggestion I can think is to split the files manually outside of
> >> hadoop. For example, generate lots of small files as 128M or 256M size.
> >>
> >> In this case, each mapper will process one small file, so you can get good
> >> utilization of your cluster, assume you have a lot of small files.
> >>
> >> Yong
> >>
> >> > From: harsh@cloudera.com
> >> > Date: Fri, 15 Aug 2014 16:45:02 +0530
> >> > Subject: Re: hadoop/yarn and task parallelization on non-hdfs
> >> > filesystems
> >> > To: user@hadoop.apache.org
> >> >
> >> > Does your non-HDFS filesystem implement a getBlockLocations API, that
> >> > MR relies on to know how to split files?
> >> >
> >> > The API is at
> >> > http://hadoop.apache.org/docs/stable2/api/org/apache/hadoop/fs/FileSystem.html#getFileBlockLocations(org.apache.hadoop.fs.FileStatus,
> >> > long, long), and MR calls it at
> >> >
> >> > https://github.com/apache/hadoop-common/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/input/FileInputFormat.java#L392
> >> >
> >> > If not, perhaps you can enforce a manual chunking by asking MR to use
> >> > custom min/max split sizes values via config properties:
> >> >
> >> > https://github.com/apache/hadoop-common/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/input/FileInputFormat.java#L66
> >> >
> >> > On Fri, Aug 15, 2014 at 10:16 AM, Calvin <iphcalvin@gmail.com> wrote:
> >> > > I've looked a bit into this problem some more, and from what another
> >> > > person has written, HDFS is tuned to scale appropriately [1] given
the
> >> > > number of input splits, etc.
> >> > >
> >> > > In the case of utilizing the local filesystem (which is really a
> >> > > network share on a parallel filesystem), the settings might be set
> >> > > conservatively in order not to thrash the local disks or present a
> >> > > bottleneck in processing.
> >> > >
> >> > > Since this isn't a big concern, I'd rather tune the settings to
> >> > > efficiently utilize the local filesystem.
> >> > >
> >> > > Are there any pointers to where in the source code I could look in
> >> > > order to tweak such parameters?
> >> > >
> >> > > Thanks,
> >> > > Calvin
> >> > >
> >> > > [1]
> >> > > https://stackoverflow.com/questions/25269964/hadoop-yarn-and-task-parallelization-on-non-hdfs-filesystems
> >> > >
> >> > > On Tue, Aug 12, 2014 at 12:29 PM, Calvin <iphcalvin@gmail.com>
wrote:
> >> > >> Hi all,
> >> > >>
> >> > >> I've instantiated a Hadoop 2.4.1 cluster and I've found that running
> >> > >> MapReduce applications will parallelize differently depending
on what
> >> > >> kind of filesystem the input data is on.
> >> > >>
> >> > >> Using HDFS, a MapReduce job will spawn enough containers to maximize
> >> > >> use of all available memory. For example, a 3-node cluster with
172GB
> >> > >> of memory with each map task allocating 2GB, about 86 application
> >> > >> containers will be created.
> >> > >>
> >> > >> On a filesystem that isn't HDFS (like NFS or in my use case, a
> >> > >> parallel filesystem), a MapReduce job will only allocate a subset
of
> >> > >> available tasks (e.g., with the same 3-node cluster, about 25-40
> >> > >> containers are created). Since I'm using a parallel filesystem,
I'm
> >> > >> not as concerned with the bottlenecks one would find if one were
to
> >> > >> use NFS.
> >> > >>
> >> > >> Is there a YARN (yarn-site.xml) or MapReduce (mapred-site.xml)
> >> > >> configuration that will allow me to effectively maximize resource
> >> > >> utilization?
> >> > >>
> >> > >> Thanks,
> >> > >> Calvin
> >> >
> >> >
> >> >
> >> > --
> >> > Harsh J
 		 	   		  
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