If only that were true -- the shuffle is typically seek-bound when the intermediate data doesn't fit into cache (plenty of papers show this pretty conclusively).
Except in the case of MR 2100 nodes the entire dataset fit in memory :)
Edit: on the other hand, this is an endorsement of the current wave of "per node performance stinks, let's avoid rewriting software for an extra year or two by throwing SSDs at it." Great for hardware vendors!
Also this was primary network bound. The old record had 2100 nodes with 10Gbps network.
Another way of looking at this is performance per watt or dollar. The r3.2 has 60GB, so comparing to that, Spark cost the same ~$1.4K while giving a 3X speedup. (Or on host that charges per minute, it'd be the same performance at 3X cheaper.)
This is me not knowing the space: would MR (or more modern things like Tez) perform worse on this HW setup, or is this a reminder that hardware/config tuning matters?
But as soon as we have more than 1 node, then having more nodes is better. We can actually demonstrate this quantitatively. We are required to replicate the output, which means 100 TB of data would generate 100 TB of network for replication, and 100 * (N-1)/N TB of network for shuffle, where N = num nodes. That is the overall 200 - 100/N in network. Assuming each node has 1GB/s network, then you'd need (200 - 100/N) / N seconds just to transfer the data across network, i.e. the optimal number of nodes is the one that gives you the lowest (200/N - 100/N^2).
The problem is with last year's MR run was that it wasn't saturating the network at all. It had roughly 1.5GB/s/node HDDs, and the overall network throughput was probably around 20MB/s/node when they were using 10Gbps network (I'm assuming only half of the time is doing network. If they were doing network the full time, then network throughput was at ~10MB/s/node).
Had we been using the same HDDs last year's entry had, our map phase would slow down by about 2x, and the reduce phase shouldn't change. This would mean a total run time of less than 30 mins on ~200 nodes. Still way better.
So you have 100TB of disk read, followed by 100TB of disk write, all on HDDs. That's about 100GB/node; and since Hadoop nodes are typically in RAID-6, each write has an associated read and write too.
This does not even include the intermediate files, which (depending on how the kernel parameters have been set), could have been written on disk. Typical dirty_background_ratio is 10; so after 6GB of dirty pages, pdflush will kick in and start writing to the spinning disk.
Maybe you can email me offline. I can tell you more about the setup and how Spark / MapReduce works w.r.t. to it.