Comparing Riak MapReduce and Hadoop MapReduce

Xiaoming Gao mkobie at gmail.com
Mon Jul 22 11:07:04 EDT 2013


Thanks for the clarification, Jeremiah!

One last question: how should I configure the MR job to have multiple
reducer processes on a single node?

Regards,
Xiaoming


On Mon, Jul 22, 2013 at 1:33 AM, Jeremiah Peschka [via Riak Users] <
ml-node+s197444n4028477h0 at n3.nabble.com> wrote:

> Ah, yeah, I'm mistaken about search partitioning. The docs are correct.
>
> I have no idea how the scheduling works.
>
> If I had to guess, I would guess that it is a streaming operation.
>
> --
> Jeremiah Peschka - Founder, Brent Ozar Unlimited
> MCITP: SQL Server 2008, MVP
> Cloudera Certified Developer for Apache Hadoop
>
> On Jul 21, 2013, at 10:08 PM, Xiaoming Gao <[hidden email]<http://user/SendEmail.jtp?type=node&node=4028477&i=0>>
> wrote:
>
> Thanks a lot, Jeremiah! Your answers really help clarify the issues.
>
> Just one more question, by "document-based indices", do you mean
> document-based partitioning for the indices? Because what I found in the
> online document
> http://docs.basho.com/riak/latest/dev/advanced/search/#Search-KV-and-MapReduceis "Search
> uses term-based partitioning – also known as a global index." I am not sure
> if the implementation has changed for the latest version of Riak, but if
> term-based partitioning is used, does that mean Riak will only schedule the
> mappers after the whole list of <bucket, key> pair is returned from the
> index?
>
> Thanks,
> Xiaoming
>
>
> On Sun, Jul 21, 2013 at 11:20 PM, Jeremiah Peschka [via Riak Users] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=4028476&i=0>> wrote:
>
>> Responses inline. Hopefully they shed some light on the subject.
>>
>> ---
>> Jeremiah Peschka - Founder, Brent Ozar Unlimited
>> MCITP: SQL Server 2008, MVP
>> Cloudera Certified Developer for Apache Hadoop
>>
>>
>> On Fri, Jul 19, 2013 at 5:07 PM, Xiaoming Gao <[hidden email]<http://user/SendEmail.jtp?type=node&node=4028474&i=0>
>> > wrote:
>>
>>> Hi everyone,
>>>
>>> I am trying to learn about Riak MapReduce and comparing it with Hadoop
>>> MapReduce, and there are some details that I am interested in but not
>>> covered in the online documents. So hopefully we can get some help here
>>> about the following questions? Thanks in advance!
>>>
>>
>> They're not at all similar. Hadoop MR is optimized for sequential data
>> processing in large batches. Riak MR works better when you think of it like
>> a multi-processing engine - you can perform work across a matching set of
>> items and that work will be distributed across the cluster during map
>> phases.
>>
>> Take a look at this thread for a bit of discussion about when you should
>> use Riak MapReduce: http://markmail.org/message/qpoilvmm635inb5v
>>
>> Or, if you want to, you can run a Riak MR job across an entire bucket,
>> which really is like scanning every table in an RDBMS while looking for
>> rows from a single table. MR jobs run with an R of 1. So, at least there's
>> that.
>>
>>
>>> 1. For a given MapReduce request (or to say, job), how does Riak decide
>>> how
>>> many mappers to use for the job? For example, if I have 8 nodes and my
>>> data
>>> are distributed across all nodes with an "N" value of 2, will I have 4
>>> mappers running on 4 nodes concurrently? Is it possible to have multiple
>>> mappers (e.g., 4 or even 6) for the same MR job running on each node (for
>>> better processing speed)?
>>>
>>
>> To the best of my recollection, this will be based on either:
>>
>> 1) If you're using JavaScript MR jobs, the number of mappers and reducers
>> is controlled by the the map_js_vm_count and reduce_js_vm_count settings
>> from each node's app.config file.
>> 2) If you're using Erlang: magic. This will be handled by the Erlang VM
>> and is based on number of processors and your overall Erlang VM
>> configuration.
>>
>>
>>>
>>> 2. If I run a MapReduce job over the results of a Riak Search query, how
>>> does Riak schedule the mappers based on the search results?
>>>
>>
>> Riak Search uses document-based indices - search will query every node in
>> the cluster. Map phases happen and then results are then streamed to the
>> reducer.
>>
>>
>>>
>>> 3. How does Riak handle intermediate data generated by mappers?
>>> Specifically:
>>> (1) In Hadoop MapReduce, the output of mappers are <key, value> pairs,
>>> and
>>> the output from all mappers are first grouped based on keys, and then
>>> handed
>>> over to the reducer. Does Riak do similar grouping of intermediate data?
>>>
>>
>> The only reason for the intermediate grouping/scratch work in Hadoop MR
>> jobs is to deal with multiple reducers. Although, I'm not entirely sure how
>> this works in Riak, my suspicion is that data is streamed across the wire
>> after the data is read from disk.
>>
>>
>>>
>>> (2) How are mapper outputs transmitted to the reducer? Does Riak use
>>> local
>>> disks on the mapper nodes or reducer nodes to store the intermediate data
>>> temporarily?
>>
>>
>> Since large MR jobs can cause out of memory errors, you can bet good
>> money that the answer is "no".
>>
>>
>>>
>>> 4. According to the document
>>> http://docs.basho.com/riak/latest/dev/advanced/mapreduce/#How-Phases-Work,
>>> each MR job only schedules one reducer, which runs on the coordinate
>>> node.
>>> Is there any way to configure a MR job to use multiple reducers?
>>>
>>
>> Using Riak MR, there's no way to create a job that runs reducers on
>> multiple nodes. You can have multiple reducer processes on a single node,
>> but not reducers on multiple nodes.
>>
>>
>>>
>>> Best regards,
>>> Xiaoming
>>>
>>>
>>>
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>>>
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