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Re: Partitioner vs GroupComparator
@Jan, why not, not send the 'hidden' part of the key as a value? Why not
then pass value as null or with some other value part. So in the reducer
side there is no duplication and you can extract the 'hidden' part of the
key yourself (which should be possible as you will be encapsulating it in a
some class/object model...?

Regards,
Shahab
On Fri, Aug 23, 2013 at 12:22 PM, Jan Lukavsk√Ĺ <[EMAIL PROTECTED]
> wrote:

> Hi all,
>
> when speaking about this, has anyone ever measured how much more data
> needs to be transferred over the network when using GroupingComparator the
> way Harsh suggests? What do I mean, when you use the GroupingComparator, it
> hides you the real key that you have emitted from Mapper. You just see the
> first key in the reduce group and any data that was carried in the key
> needs to be duplicated in the value in order to be accessible on the reduce
> end.
>
> Let's say you have key consisting of two parts (base, extension), you
> partition by the 'base' part and use GroupingComparator to group keys with
> the same base part. Than you have no other chance than to emit from Mapper
> something like this - (key: (base, extension), value: extension), which
> means the 'extension' part is duplicated in the data, that has to be
> transferred over the network. This overhead can be diminished by using
> compression between map and reduce side, but I believe that in some cases
> this can be significant.
>
> It would be nice if the API allowed to access the 'real' key for each
> value, not only the first key of the reduce group. The only way to get rid
> of this overhead now is by not using the GroupingComparator and instead
> store some internal state in the Reducer class, that is persisted across
> mutliple calls to reduce() method, which in my opinion makes using
> GroupingComparator this way less 'preferred' way of doing secondary sort.
>
> Does anyone have any experience with this overhead?
>
> Jan
>
>
> On 08/23/2013 06:05 PM, Harsh J wrote:
>
>> The partitioner runs on the map-end. It assigns a partition ID
>> (reducer ID) to each key.
>> The grouping comparator runs on the reduce-end. It helps reducers,
>> which read off a merge-sorted single file, to understand how to break
>> the sequential file into reduce calls of <key, values[]>.
>>
>> Typically one never overrides the GroupingComparator, and it is
>> usually the same as the SortComparator. But if you wish to do things
>> such as Secondary Sort, then overriding this comes useful - cause you
>> may want to sort over two parts of a key object, but only group by one
>> part, etc..
>>
>> On Fri, Aug 23, 2013 at 8:49 PM, Eugene Morozov
>> <[EMAIL PROTECTED]> wrote:
>>
>>> Hello,
>>>
>>> I have two different types of keys emerged from Map and processed by
>>> Reduce.
>>> These keys have some part in common. And I'd like to have similar keys in
>>> one reducer. For that purpose I used Partitioner and partition everything
>>> gets in by this common part. It seems to be fine, but MRUnit seems
>>> doesn't
>>> know anything about Partitioners. So, here is where GroupComparator comes
>>> into play. It seems that MRUnit well aware of the guy, but it surprises
>>> me:
>>> it looks like Partitioner and GroupComparator are actually doing exactly
>>> same - they both somehow group keys to have them in one reducer.
>>> Could you shed some light on it, please.
>>> --
>>>
>>>
>>
>>