Hi Boris,

MapReduce is a very flexible and powerful way of querying Riak and allows 
processing to be performed locally where the data resides, which allows for 
efficient processing of larger data sets. A result of this is that every 
mapreduce job requires a covering set of vnodes (all vnodes that hold the data 
required for processing) to participate, meaning that it puts considerable more 
load on the system compared to straight K/V access and therefore does not scale 
quite as well. It is primarily designed for batch type processing over 
reasonably large amounts of data and scales well with increased data volumes as 
new nodes are added. We do however usually not recommended using it as an 
interface for realtime queries where low and predictable latencies are required 
and the concurrency level, and therefore load level on the cluster, can not be 
controlled.

I am not sure I understand what you mean by the performance degrading with the 
number of nodes, unless you are strictly measuring latency rather than 
throughput. As the number of nodes increase, it gets more and more likely that 
multiple physical nodes will be involved in the job, which will add to the 
amount of communication and coordination required between the nodes, thereby 
increasing latency. Could you please explain in more detail what you are trying 
to achieve?

Best regards,

Christian 


On 25 Feb 2013, at 16:41, Boris Okner <boris.ok...@gmail.com> wrote:

> Hello,
> 
> I'm experimenting with 2 Riak 1.3.0 nodes (both are "bare metal"), and it 
> looks like mapreduce performs better when one of the nodes is down. The 
> mapreduce requests are running on 20-key blocks. So am I doing something 
> wrong, or is it an expected behaviour, i.e. mapreduce degrades with the the 
> number of nodes increased? If the former, could 
> you give me some pointers on how to set up it to get advantage of multiple 
> nodes?
> 
> Thanks in advance for your help,
> Boris
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