Earlier this week, Andrew Morgan wrote a piece on running MySQL Cluster on Raspberry Pi. Since the term “Cluster” is hideously overloaded, I’ll note that we’re talking about the NDB cluster storage engine here, a very specific architecture originally acquired by MySQL AB from Ericsson (telco).
Raspberry Pi is a new single-board computer based on the ARM processor series (same stuff that powers most mobile phones these days), and it can run Linux without any fuss. Interfaces include Ethernet, USB, and HDMI video, and the cost is $25-50. I’m looking to use one for the front-end of a MythTV setup (digital video recorder and TV system), I can just strap the Raspberry Pi to the back of a TV or monitor to do its job.
As Andrew already notes, in practical terms you’re not likely to use Raspberry Pi for a cluster – perhaps for development and certain testing, and it’d be a neat solid state management server. Primarily, it’s “techie cool”.
Knowing the NDB architecture, one of the key issues is that all nodes need to communicate with each other (NxN) so the system is very network intensive, and network latency significantly affects performance. So commonly, a cluster would have at least separate interfaces for direct connections to its siblings (no switch), and possibly Dolphin Interconnect cards to provide a link with much less latency than regular Ethernet offers. And you can’t do either with Raspberry Pi.
However, there are important positive lessons in this setup:
- Using the open source nature of the software it can be utilised in a new environment with only minimal tweaks. Not everybody needs to or wants to tweak, but the ability to do so is critical to innovation.
- Overall, scaling out rather than up makes sense. There are cost, power-efficiency and other factors involved. More, cheap, relatively low-powered, systems can deliver a system architecture that would otherwise be unaffordable (and the expensive construct might not scale anyway).
- Affordable resilience (redundancy).
What if you needed lots of MySQL slaves with a fairly small dataset? Raspberry Pi could well be the solution. Not everybody is “big” or “high performance” in the same way.