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From RJ Nowling <rnowl...@gmail.com>
Subject Re: Grouping runs of elements in a RDD
Date Tue, 30 Jun 2015 19:00:38 GMT
Thanks, Reynold.  I still need to handle incomplete groups that fall
between partition boundaries. So, I need a two-pass approach. I came up
with a somewhat hacky way to handle those using the partition indices and
key-value pairs as a second pass after the first.

OCaml's std library provides a function called group() that takes a break
function that operators on pairs of successive elements.  It seems a
similar approach could be used in Spark and would be more efficient than my
approach with key-value pairs since you know the ordering of the partitions.

Has this need been expressed by others?

On Tue, Jun 30, 2015 at 1:03 PM, Reynold Xin <rxin@databricks.com> wrote:

> Try mapPartitions, which gives you an iterator, and you can produce an
> iterator back.
>
>
> On Tue, Jun 30, 2015 at 11:01 AM, RJ Nowling <rnowling@gmail.com> wrote:
>
>> Hi all,
>>
>> I have a problem where I have a RDD of elements:
>>
>> Item1 Item2 Item3 Item4 Item5 Item6 ...
>>
>> and I want to run a function over them to decide which runs of elements
>> to group together:
>>
>> [Item1 Item2] [Item3] [Item4 Item5 Item6] ...
>>
>> Technically, I could use aggregate to do this, but I would have to use a
>> List of List of T which would produce a very large collection in memory.
>>
>> Is there an easy way to accomplish this?  e.g.,, it would be nice to have
>> a version of aggregate where the combination function can return a complete
>> group that is added to the new RDD and an incomplete group which is passed
>> to the next call of the reduce function.
>>
>> Thanks,
>> RJ
>>
>
>

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