You are viewing a plain text version of this content. The canonical link for it is here.
Posted to common-user@hadoop.apache.org by Niels Basjes <Ni...@basjes.nl> on 2011/04/27 09:55:48 UTC
Unsplittable files on HDFS
Hi,
In some scenarios you have gzipped files as input for your map reduce
job (apache logfiles is a common example).
Now some of those files are several hundred megabytes and as such will
be split by HDFS in several blocks.
When looking at a real 116MiB file on HDFS I see this (4 nodes, replication = 2)
Total number of blocks: 2
25063947863662497: 10.10.138.62:50010 10.10.138.61:50010
1014249434553595747: 10.10.138.64:50010 10.10.138.63:50010
As you can see the file has been distributed over all 4 nodes.
When actually reading those files they are unsplittable due to the
nature of the Gzip codec.
So a job will (in the above example) ALWAYS need to pull "the other
half" of the file over the network, if a file is bigger and the
cluster is bigger then the percentage of the file that goes over the
network will probably increase.
Now if I can tell HDFS that a ".gz" file should always be "100% local"
for the node that will be doing the processing this would reduce the
network IO during the job dramatically.
Especially if you want to run several jobs against the same input.
So my question is: Is there a way to force/tell HDFS to make sure that
a datanode that has blocks of this file must always have ALL blocks of
this file?
--
Best regards,
Niels Basjes
Re: Unsplittable files on HDFS
Posted by Steve Loughran <st...@apache.org>.
On 27/04/11 10:48, Niels Basjes wrote:
> Hi,
>
> I did the following with a 1.6GB file
> hadoop fs -Ddfs.block.size=2147483648 -put
> /home/nbasjes/access-2010-11-29.log.gz /user/nbasjes
> and I got
>
> Total number of blocks: 1
> 4189183682512190568: 10.10.138.61:50010 10.10.138.62:50010
>
> Yes, that does the trick. Thank you.
>
> Niels
>
> 2011/4/27 Harsh J<ha...@cloudera.com>:
>> Hey Niels,
>>
>> The block size is a per-file property. Would putting/creating these
>> gzip files on the DFS with a very high block size (such that it
>> doesn't split across for such files) be a valid solution to your
>> problem here?
>>
Don't set a block size >2GB, not all the bits of the code that use
signed 32 bit integers have been eliminated yet.
Re: Unsplittable files on HDFS
Posted by Niels Basjes <Ni...@basjes.nl>.
Hi,
I did the following with a 1.6GB file
hadoop fs -Ddfs.block.size=2147483648 -put
/home/nbasjes/access-2010-11-29.log.gz /user/nbasjes
and I got
Total number of blocks: 1
4189183682512190568: 10.10.138.61:50010 10.10.138.62:50010
Yes, that does the trick. Thank you.
Niels
2011/4/27 Harsh J <ha...@cloudera.com>:
> Hey Niels,
>
> The block size is a per-file property. Would putting/creating these
> gzip files on the DFS with a very high block size (such that it
> doesn't split across for such files) be a valid solution to your
> problem here?
>
> On Wed, Apr 27, 2011 at 1:25 PM, Niels Basjes <Ni...@basjes.nl> wrote:
>> Hi,
>>
>> In some scenarios you have gzipped files as input for your map reduce
>> job (apache logfiles is a common example).
>> Now some of those files are several hundred megabytes and as such will
>> be split by HDFS in several blocks.
>>
>> When looking at a real 116MiB file on HDFS I see this (4 nodes, replication = 2)
>>
>> Total number of blocks: 2
>> 25063947863662497: 10.10.138.62:50010 10.10.138.61:50010
>> 1014249434553595747: 10.10.138.64:50010 10.10.138.63:50010
>>
>> As you can see the file has been distributed over all 4 nodes.
>>
>> When actually reading those files they are unsplittable due to the
>> nature of the Gzip codec.
>> So a job will (in the above example) ALWAYS need to pull "the other
>> half" of the file over the network, if a file is bigger and the
>> cluster is bigger then the percentage of the file that goes over the
>> network will probably increase.
>>
>> Now if I can tell HDFS that a ".gz" file should always be "100% local"
>> for the node that will be doing the processing this would reduce the
>> network IO during the job dramatically.
>> Especially if you want to run several jobs against the same input.
>>
>> So my question is: Is there a way to force/tell HDFS to make sure that
>> a datanode that has blocks of this file must always have ALL blocks of
>> this file?
>>
>> --
>> Best regards,
>>
>> Niels Basjes
>>
>
>
>
> --
> Harsh J
>
--
Met vriendelijke groeten,
Niels Basjes
Re: Unsplittable files on HDFS
Posted by Harsh J <ha...@cloudera.com>.
Hey Niels,
The block size is a per-file property. Would putting/creating these
gzip files on the DFS with a very high block size (such that it
doesn't split across for such files) be a valid solution to your
problem here?
On Wed, Apr 27, 2011 at 1:25 PM, Niels Basjes <Ni...@basjes.nl> wrote:
> Hi,
>
> In some scenarios you have gzipped files as input for your map reduce
> job (apache logfiles is a common example).
> Now some of those files are several hundred megabytes and as such will
> be split by HDFS in several blocks.
>
> When looking at a real 116MiB file on HDFS I see this (4 nodes, replication = 2)
>
> Total number of blocks: 2
> 25063947863662497: 10.10.138.62:50010 10.10.138.61:50010
> 1014249434553595747: 10.10.138.64:50010 10.10.138.63:50010
>
> As you can see the file has been distributed over all 4 nodes.
>
> When actually reading those files they are unsplittable due to the
> nature of the Gzip codec.
> So a job will (in the above example) ALWAYS need to pull "the other
> half" of the file over the network, if a file is bigger and the
> cluster is bigger then the percentage of the file that goes over the
> network will probably increase.
>
> Now if I can tell HDFS that a ".gz" file should always be "100% local"
> for the node that will be doing the processing this would reduce the
> network IO during the job dramatically.
> Especially if you want to run several jobs against the same input.
>
> So my question is: Is there a way to force/tell HDFS to make sure that
> a datanode that has blocks of this file must always have ALL blocks of
> this file?
>
> --
> Best regards,
>
> Niels Basjes
>
--
Harsh J