DataCore Parallel I/O technology seems like a kind of magic, and too good to be true… but you only need to try it once to understand that it is real and has the potential to save you loads of money!
Benchmarks Vs Real world
Frankly, I was skeptical at first and I totally underestimated this technology. The benchmark posted a while ago was incredibly good (too good to be true?!). And even though this one wasn’t false, sometimes you can just work around some limits of the benchmarking suite and build specific and unrealistic configurations to get numbers that look very good, but that are hard to reproduce in real world scenarios.
When I was briefed by DataCore they convinced me not with sterile benchmarks, but with real workload testing! In fact, I was particularly impressed by a set of amazing demos I had the chance to watch where a Windows database server, equipped with Parallel I/O Technology, was able to process data dozens of times faster than the same server without DataCore’s software… and the same happened with a cloud VM instance (which is theoretically the same, since this is a software technology, but is much more important than you think… especially if you look at how much money you could save by adopting it).
Yes, dozens of times faster!
I know it seems ridiculous, but it isn’t. DataCore Parallel Server is a very simple piece of software that changes the way IO operations are performed. It takes advantage of the large number of CPU cores and RAM available on a server and allows to organize all the IOs in a parallel fashion, instead of serial, allowing to achieve microsecond level latency and, consequently, a very large number of IOPs. Want to know more? You’ll have to read my paper then.
This kind of performance allows to build smaller clusters or get results much faster with the same amount of nodes… and without changing the software stack or adding expensive in-memory options to your DB. It is ideal for Big Data Analytics use cases, but there are also other scenarios where this technology can be of great benefit!
I don’t want to downplay DataCore’s work by saying “just software”, quite the contrary indeed! The fact that we are talking about a relatively simple piece of software makes it applicable not only to your physical server but also to a VM or, better, a cloud VM.
If you look at cloud VM prices, you’ll realise that it is much better to run a job in a small set of large-CPU large-memory VMs than in a large amount of SSD-based VMs for example… and this simply means that you can spend less to do more, faster. And, again, when it comes to Big Data Analytics this is a great result, isn’t it?
Closing the circle
DataCore is one of those companies that has been successful and profitable for years. Last year, with the introduction of Parallel I/O they demonstrated their capability of still being able to innovate and bring value to their customers. Now, thanks to an evolution of Parallel I/O, they are entering in a totally new market, with a solution that can easily enable end users to save loads of money and get faster results. It’s not magic of course, just a much better way to use the resources available in modern servers.
Parallel Server is perfect for Big Data Analytics, makes it available to a larger audience, and I’m sure we will see other interesting use cases for this solution over time…
[Disclaimer: DataCore is a customer of mine and I wrote this white paper for them.]