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From Amogh Vasekar <am...@yahoo-inc.com>
Subject Re: How do I sum by Key in the Reduce Phase AND keep the initial value
Date Wed, 13 Jan 2010 07:50:59 GMT
Hi Stephen,
I'm pretty sure the re-iterable reducer works by storing <k,v> in memory and spilling
to disk once a certain threshold limit is reached. I'm don't know how they decide the limit
though ( probably a parameter like io.sort.mb? ), but the patch will throw some light on this.
The pattern you described was what I meant with a successor map only job, but using HOD and
shared cluster had some other associated issues, so I preferred writing to file instead.

Thanks,
Amogh


On 1/13/10 2:24 AM, "Stephen Watt" <swatt@us.ibm.com> wrote:

Thanks for responding Amogh.

I'm using Hadoop 0.20.1 and see by the JIRA you mentioned its resolved in 0.21. Bummer...
 I've thought about the same thing you mentioned however, its my understanding that keeping
those values or records in memory is dangerous as you can run out of memory depending on how
many values you have (and I have a big dataset).  Really, what I am trying to understand here
is the Map Reduce Pattern for solving this type of problem. I think until we have a reduce
values iterator we can move through more than once, I believe the pattern would be:

1) Have the first job simply store the key and the sum value
2) By using the same keys, one would have the second job append the value from the first job
to each record in the reducer. This would be achieved by FIRST going to the HDFS and looking
up the value for the key from the first job and then iterating through the values for all
the keys on the second job and appending the sum value to each record.

Kind regards
Steve Watt


From: Amogh Vasekar <amogh@yahoo-inc.com>
To: "mapreduce-user@hadoop.apache.org" <mapreduce-user@hadoop.apache.org>
Date: 01/12/2010 02:01 PM
Subject: Re: How do I sum by Key in the Reduce Phase AND keep the initial value
________________________________



Hi,
I ran into a very similar situation quite some time back and had then encountered this : http://issues.apache.org/jira/browse/HADOOP-475
<http://issues.apache.org/jira/browse/HADOOP-475>
After speaking to a few Hadoop folks, they had said complete cloning was not a straightforward
option for some optimization reasons.
There were a few things I tried , to run this in a single MR job emitting <k,v>  from
mapper one more time with some tagging info ( this bumped up S&S phase by quite a lot
); run a map only successor job etc. But keeping records in memory and writing to disk after
certain threshold amount worked pretty well for me ( all this on Hadoop 0.17.2 )
Anyways, they seem to have resolved it in next Hadoop release.

Amogh



On 1/12/10 10:29 PM, "Stephen Watt" <swatt@us.ibm.com <swatt@us.ibm.com> > wrote:

The Key Value pairs coming into my Reducer are as Follows

KEY(Text)        VALUE(IntWritable)
A               11
A               9
B               2
B                3

I want my reducer to sum the Values for each input key and then output the key with a Text
Value containing the original value and the sum.

KEY(Text)        VALUE(Text)
A               11       20
A               9       20
B               2       5
B                3        5

Here is the issue :  In the reducer, I am iterating through the values for each using values.iterator()
and storing the total amount in a variable. Then I am TRYING to iterate through the keys again,
except this time, writing the new value (A, new Text("11 20") in the output collector to create
the Value structure displayed in the example above. This fails because it appears I can only
iterate through the values for each key ONCE. I know this because additional attempts to get
new iterators from the context or the Iterable type thats passed into the reducer always return
false on the initial hasNext().

I have to iterate through it twice because the first time I have to sum the values and the
second time I need to write the write the initial (11) value and the sum(20) as I need both
values as part of a calculation in the next job. Any ideas on how to do this ?

Kind regards
Steve Watt



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