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From Mohit Jaggi <>
Subject Re: RDD.combineBy without intermediate (k,v) pair allocation
Date Thu, 29 Jan 2015 17:52:00 GMT
RDD.aggregate() does not support aggregation by key. But, indeed, that is the kind of implementation
I am looking for, one that does not allocate intermediate space for storing (K,V) pairs. When
working with large datasets this type of intermediate memory allocation wrecks havoc with
garbage collection, not to mention unnecessarily increases the working memory requirement
of the program.

I wonder if someone has already noticed this and there is an effort underway to optimize this.
If not, I will take a shot at adding this functionality.


> On Jan 27, 2015, at 1:52 PM, wrote:
> Have you looked at the `aggregate` function in the RDD API ? 
> If your way of extracting the “key” (identifier) and “value” (payload) parts
of the RDD elements is uniform (a function), it’s unclear to me how this would be more efficient
that extracting key and value and then using combine, however.
> —
> FG
> On Tue, Jan 27, 2015 at 10:17 PM, Mohit Jaggi < <>>
> Hi All, 
> I have a use case where I have an RDD (not a k,v pair) where I want to do a combineByKey()
operation. I can do that by creating an intermediate RDD of k,v pairs and using PairRDDFunctions.combineByKey().
However, I believe it will be more efficient if I can avoid this intermediate RDD. Is there
a way I can do this by passing in a function that extracts the key, like in RDD.groupBy()?
[oops, RDD.groupBy seems to create the intermediate RDD anyway, maybe a better implementation
is possible for that too?] 
> If not, is it worth adding to the Spark API? 
> Mohit. 
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