Comparing Riak MapReduce and Hadoop MapReduce

Jeremiah Peschka jeremiah.peschka at gmail.com
Mon Jul 22 13:40:59 EDT 2013


Oh, I almost forgot, you can also supply the do_prereduce argument to your
reduce phase - this performs a pre-reduce phase on the mapper. This can,
depending on the workload, significantly decrease the network overhead
between the mappers and the reducer.

---
Jeremiah Peschka - Founder, Brent Ozar Unlimited
MCITP: SQL Server 2008, MVP
Cloudera Certified Developer for Apache Hadoop


On Mon, Jul 22, 2013 at 10:21 AM, Jeremiah Peschka <
jeremiah.peschka at gmail.com> wrote:

> For JavaScript the number of reducers is configured in the app.config file
> on each node with the reduce_js_vm_count property.
>
> ---
> Jeremiah Peschka - Founder, Brent Ozar Unlimited
> MCITP: SQL Server 2008, MVP
> Cloudera Certified Developer for Apache Hadoop
>
>
> On Mon, Jul 22, 2013 at 8:07 AM, Xiaoming Gao <mkobie at gmail.com> wrote:
>
>> 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] <[hidden
>> email] <http://user/SendEmail.jtp?type=node&node=4028486&i=0>> 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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