Clearly your input size isn't changing. And depending on how they are distributed on the nodes, there could be Datanode/disks contention.
The better way to model this is by scaling the input data also linearly. More nodes should process more data in the same amount of time.
On Sep 6, 2013, at 8:27 AM, 牛兆捷 wrote:
> Hi all：
> I vary the computational nodes of cluster and get the speedup result in attachment.
> In my mind, there are three type of speedup model: linear, sub-linear and super-linear. However the curve of my result seems a little strange. I have attached it.
> This is sort in example.jar, actually it is done only using the default map-reduce mechanism of Hadoop.
> I use hadoop-1.2.1, set 8 map slots and 8 reduce slots per node(12 cpu, 20g men)
> io.sort.mb = 512, block size = 512mb, heap size = 1024mb, reduce.slowstart = 0.05, the others are default.
> Input data: 20g, I divide it to 64 files
> Sort example: 64 map tasks, 64 reduce tasks
> Computational nodes: varying from 2 to 9
> Why the speedup mechanism is like this? How can I model it properly?
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