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From Kim Chew <kchew...@gmail.com>
Subject Re: Why is HDFS_BYTES_WRITTEN is much larger than HDFS_BYTES_READ in this case?
Date Fri, 28 Mar 2014 00:57:51 GMT
Thanks folks.

I am not awared my input data file has been compressed.
FileOutputFromat.setCompressOutput() is set to true when the file is
written. 8-(

Kim


On Thu, Mar 27, 2014 at 5:46 PM, Mostafa Ead <mostafa.g.ead@gmail.com>wrote:

> The following might answer you partially:
>
> Input key is not read from HDFS, it is auto generated as the offset of the
> input value in the input file. I think that is (partially) why read hdfs
> bytes is smaller than written hdfs bytes.
>  On Mar 27, 2014 1:34 PM, "Kim Chew" <kchew534@gmail.com> wrote:
>
>> I am also wondering if, say, I have two identical timestamp so they are
>> going to be written to the same file. Does MulitpleOutputs handle appending?
>>
>> Thanks.
>>
>> Kim
>>
>>
>> On Thu, Mar 27, 2014 at 12:30 PM, Thomas Bentsen <th@bentzn.com> wrote:
>>
>>> Have you checked the content of the files you write?
>>>
>>>
>>> /th
>>>
>>> On Thu, 2014-03-27 at 11:43 -0700, Kim Chew wrote:
>>> > I have a simple M/R job using Mapper only thus no reducer. The mapper
>>> > read a timestamp from the value, generate a path to the output file
>>> > and writes the key and value to the output file.
>>> >
>>> >
>>> > The input file is a sequence file, not compressed and stored in the
>>> > HDFS, it has a size of 162.68 MB.
>>> >
>>> >
>>> > Output also is written as a sequence file.
>>> >
>>> >
>>> >
>>> > However, after I ran my job, I have two output part files from the
>>> > mapper. One has a size of 835.12 MB and the other has a size of 224.77
>>> > MB. So why is the total outputs size is so much larger? Shouldn't it
>>> > be more or less equal to the input's size of 162.68MB since I just
>>> > write the key and value passed to mapper to the output?
>>> >
>>> >
>>> > Here is the mapper code snippet,
>>> >
>>> > public void map(BytesWritable key, BytesWritable value, Context
>>> > context) throws IOException, InterruptedException {
>>> >
>>> >         long timestamp = bytesToInt(value.getBytes(),
>>> > TIMESTAMP_INDEX);;
>>> >         String tsStr = sdf.format(new Date(timestamp * 1000L));
>>> >
>>> >         mos.write(key, value, generateFileName(tsStr)); // mos is a
>>> > MultipleOutputs object.
>>> >     }
>>> >
>>> >         private String generateFileName(String key) {
>>> >         return outputDir+"/"+key+"/raw-vectors";
>>> >     }
>>> >
>>> >
>>> > And here are the job outputs,
>>> >
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Launched map tasks=2
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Data-local map tasks=2
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=0
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:   File Output Format
>>> > Counters
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Bytes Written=0
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:   FileSystemCounters
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     HDFS_BYTES_READ=171086386
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=54272
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:
>>> > HDFS_BYTES_WRITTEN=1111374798
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:   File Input Format Counters
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Bytes Read=170782415
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:   Map-Reduce Framework
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Map input records=547
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Physical memory (bytes)
>>> > snapshot=166428672
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Spilled Records=0
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Total committed heap
>>> > usage (bytes)=38351872
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     CPU time spent (ms)=20080
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Virtual memory (bytes)
>>> > snapshot=1240104960
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     SPLIT_RAW_BYTES=286
>>> > 14/03/27 11:00:56 INFO mapred.JobClient:     Map output records=0
>>> >
>>> >
>>> > TIA,
>>> >
>>> >
>>> > Kim
>>> >
>>>
>>>
>>>
>>

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