> From: Jie Li [mailto:ji...@cs.duke.edu]
> Sent: 28 February 2012 16:35
> To: common-user@hadoop.apache.org
> Subject: Re: Spilled Records
>
> Hello Dan,
>
> The fact that the spilled records are double as the output records means
> the map task produces more than one sp
t would be much good.
Thanks again, Dan.
-Original Message-
From: Jie Li [mailto:ji...@cs.duke.edu]
Sent: 28 February 2012 16:35
To: common-user@hadoop.apache.org
Subject: Re: Spilled Records
Hello Dan,
The fact that the spilled records are double as the output records means
the map tas
Hello Dan,
The fact that the spilled records are double as the output records means
the map task produces more than one spill file, and these spill files are
read, merged and written to a single file, thus each record is spilled
twice.
I can't infer anything from the numbers of the two
67,108,864
FILE_BYTES_WRITTEN 429,278,388
Map-Reduce Framework
Combine output records 0
Map input records 2,221,478
Spilled Records 4,442,956
Map output bytes 210,196,148
Combine input records 0
Map output records 2,221,478
And another task in the same job (16 of 16) that took 7 minutes and 19 seconds
________
> From: maha [m...@umail.ucsb.edu]
> Sent: Tuesday, February 22, 2011 12:19 PM
> To: common-user@hadoop.apache.org
> Subject: Re: Spilled Records
>
> Thank you Saurabh, but the following setting didn't change # of spilled
> records:
From: maha [m...@umail.ucsb.edu]
Sent: Tuesday, February 22, 2011 12:19 PM
To: common-user@hadoop.apache.org
Subject: Re: Spilled Records
Thank you Saurabh, but the following setting didn't change # of spilled records:
conf.set("mapred.job.shuffle.merge.percent"
Thank you Saurabh, but the following setting didn't change # of spilled records:
conf.set("mapred.job.shuffle.merge.percent", ".9");//instead of .66
conf.set("mapred.inmem.merge.threshold", "1000");// instead of 1000
IS it's because of my me
Hi Maha,
The spilled record has to do with the transient data during the map and reduce
operations. Note that it's not just the map operations that generate the
spilled records. When the in-memory buffer (controlled by
mapred.job.shuffle.merge.percent) runs out or reaches the threshold n
Hello every one,
Does spilled records mean that the sort-buffer size for sorting is not enough
to sort all the input records, hence some records are written to local disk ?
If so, I tried setting my io.sort.mb from the default 100 to 200 and there was
still the same # of spilled records. Why
Thanks. It's clear now. :)
On Wed, Jul 15, 2009 at 11:40 AM, Jothi Padmanabhan
wrote:
> It is true, map writes its output to a memory buffer. But when the map
> process is complete, the contents of this buffer are sorted and spilled to
> the disk so that the Task Tracker running on that node can
It is true, map writes its output to a memory buffer. But when the map
process is complete, the contents of this buffer are sorted and spilled to
the disk so that the Task Tracker running on that node can serve these map
outputs to the requesting reducers.
On 7/15/09 7:59 AM, "Mu Qiao" wrote:
>
Thanks. But when I refer to "Hadoop: The Definitive Guide" chapter 6, I find
that the map writes its outputs to a memory buffer(not to local disk) whose
size is controlled by io.sort.mb. Only the buffer reaches its threshold, it
will spill the outputs to local disk. If that is true, I can't see any
There is no requirement that all of the reduces are running while the map is
running. The dataflow is that the map writes its output to local disk and
that the reduces pull the map outputs when they need them. There are threads
handling sorting and spill of the records to disk, but that doesn't rem
restart the reduce jobs that use those
> spilled records in case of a reduce task failure.
>
> Dali
> On Mon, Jul 13, 2009 at 6:32 PM, Mu Qiao wrote:
>
> > Thank you. But why need map outputs to be written to disk at least once?
> I
> > think my io.sort.mb is large eno
If I am not mistaken (I am new to this stuff), that's because you need to
have a checkpoint from which you can restart the reduce jobs that use those
spilled records in case of a reduce task failure.
Dali
On Mon, Jul 13, 2009 at 6:32 PM, Mu Qiao wrote:
> Thank you. But why need map ou
ote:
>
> I notice it from the web console after I've tried to run serveral jobs.
>> Every one of the jobs has the number of Spilled Records equal to Map
>> output
>> records, even if there are only 5 map output records
>>
>
>
> This is good. The map ou
On Jul 12, 2009, at 3:55 AM, Mu Qiao wrote:
I notice it from the web console after I've tried to run serveral
jobs.
Every one of the jobs has the number of Spilled Records equal to Map
output
records, even if there are only 5 map output records
This is good. The map outputs need
Hi, everyone
I'm a beginner of hadoop.
I notice it from the web console after I've tried to run serveral jobs.
Every one of the jobs has the number of Spilled Records equal to Map output
records, even if there are only 5 map output records
In the reduce phase, there are also spill
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