Curious about this:
> parallelism can speed up big data queries by as much as 32 times faster
Why would it be only 32 times faster? The sky's the limit if there aren't major bottlenecks on the way.
For a sequential full table scan I could process about 2000MB/s of data(only 125MB/s was read from each SSD), I was limited by CPU power.
Anyway, same query took about 25 minutes on PostgreSQL 9.5 and now it was down to 2minutes and 30 seconds. For comparison, SQL Server 2012 spent 7 minutes on the same dataset on the same hardware.
I realize I'm asking a stranger on the internet to do something for free for me. If you don't have time or inclination to do this, no worries, but it seems like you've got a nice setup to be able to play with this. I'm sure I'm not the only one curious to see such a comparison.
So yes, it is very workload specific. For random read/write they are probably more similar. But for reading a lot of data that can be read sequentially, PostgreSQL seems to win hands down, because it can get a lot of help from ZFS compression.
I would love to run the same test when SQL Server is available on Linux. But ZFS do also deliver slightly better throughput and slightly more iops on the FreeBSD platform, which I ran this benchmark on. And SQL Server probably demands a 4k block size, which is so small that LZ4 compression has no effect as I've already tried to run SQL Server on ZFS via iSCSI.
(You can probably get more memory level parallelism with random access, but your overall bandwidth will likely be lower... fully exploiting memory bandwidth is complicated and difficult to do for real applications).