As per the specification of this test, the data has to be committed on disk before it is considered sorted. So even if it all fits in memory, it has to be on disk before the end.

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.

Ah, so you're saying as this is network bound, you want to launch much fewer wimpy nodes in exchange for a few bigger ones (those w/ SSDs -- over 1PB worth!). If it was memory-bound, you wouldn't care how they're spread as long as it was still in-memory.

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?