hadoop-mapreduce-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Jay Vyas <jayunit...@gmail.com>
Subject Re: partition as block?
Date Wed, 01 May 2013 00:00:34 GMT
What do you mean "increasing the size"?  Im talking more about increasing the number of partitions...
Which actually decreases individual file size.

On Apr 30, 2013, at 4:09 PM, Mohammad Tariq <dontariq@gmail.com> wrote:

> Increasing the size can help us to an extent, but increasing it further might cause problems
during copy and shuffle. If the partitions are too big to be held in the memory, we'll end
up with disk based shuffle which is gonna be slower than RAM based shuffle, thus delaying
the entire reduce phase. Furthermore N/W might get overwhelmed.
> 
> I think keeping it "considerably" high will definitely give you some boost. But it'll
require a high level tinkering.
> 
> Warm Regards,
> Tariq
> https://mtariq.jux.com/
> cloudfront.blogspot.com
> 
> 
> On Wed, May 1, 2013 at 1:29 AM, Jay Vyas <jayunit100@gmail.com> wrote:
>> Yes it is a problem at the first stage.  What I'm wondering, though, is wether the
intermediate results - which happen after the mapper phase - can be optimized.
>> 
>> 
>> On Tue, Apr 30, 2013 at 3:38 PM, Mohammad Tariq <dontariq@gmail.com> wrote:
>>> Hmmm. I was actually thinking about the very first step. How are you going to
create the maps. Suppose you are on a block-less filesystem and you have a custom Format that
is going to give you the splits dynamically. This mean that you are going to store the file
as a whole and create the splits as you continue to read the file. Wouldn't it be a bottleneck
from 'disk' point of view??Are you not going away from the distributed paradigm??
>>> 
>>> Am I taking it in the correct way. Please correct me if I am getting it wrong.
>>> 
>>> Warm Regards,
>>> Tariq
>>> https://mtariq.jux.com/
>>> cloudfront.blogspot.com
>>> 
>>> 
>>> On Wed, May 1, 2013 at 12:34 AM, Jay Vyas <jayunit100@gmail.com> wrote:
>>>> Well, to be more clear, I'm wondering how hadoop-mapreduce can be optimized
in a block-less filesystem... And am thinking about application tier ways to simulate blocks
- i.e. by making the granularity of partitions smaller. 
>>>> 
>>>> Wondering, if there is a way to hack an increased numbers of partitions as
a mechanism to simulate blocks - or wether this is just a bad idea altogether :) 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> On Tue, Apr 30, 2013 at 2:56 PM, Mohammad Tariq <dontariq@gmail.com>
wrote:
>>>>> Hello Jay,
>>>>> 
>>>>>     What are you going to do in your custom InputFormat and partitioner?Is
your InputFormat is going to create larger splits which will overlap with larger blocks?If
that is the case, IMHO, then you are going to reduce the no. of mappers thus reducing the
parallelism. Also, much larger block size will put extra overhead when it comes to disk I/O.
>>>>> 
>>>>> Warm Regards,
>>>>> Tariq
>>>>> https://mtariq.jux.com/
>>>>> cloudfront.blogspot.com
>>>>> 
>>>>> 
>>>>> On Wed, May 1, 2013 at 12:16 AM, Jay Vyas <jayunit100@gmail.com>
wrote:
>>>>>> Hi guys:
>>>>>> 
>>>>>> Im wondering - if I'm running mapreduce jobs on a cluster with large
block sizes - can i increase performance with either:
>>>>>> 
>>>>>> 1) A custom FileInputFormat
>>>>>> 
>>>>>> 2) A custom partitioner 
>>>>>> 
>>>>>> 3) -DnumReducers
>>>>>> 
>>>>>> Clearly, (3) will be an issue due to the fact that it might overload
tasks and network traffic... but maybe (1) or (2) will be a precise way to "use" partitions
as a "poor mans" block.  
>>>>>> 
>>>>>> Just a thought - not sure if anyone has tried (1) or (2) before in
order to simulate blocks and increase locality by utilizing the partition API.
>>>>>> 
>>>>>> -- 
>>>>>> Jay Vyas
>>>>>> http://jayunit100.blogspot.com
>>>> 
>>>> 
>>>> 
>>>> -- 
>>>> Jay Vyas
>>>> http://jayunit100.blogspot.com
>> 
>> 
>> 
>> -- 
>> Jay Vyas
>> http://jayunit100.blogspot.com
> 

Mime
View raw message